Child Development and Early Learning
The domains of child development and early learning are discussed in different terms and categorized in different ways in the various fields and disciplines that are involved in research, practice, and policy related to children from birth through age 8. To organize the discussion in this report, the committee elected to use the approach and overarching terms depicted in Figure 4-1. The committee does not intend to present this as a single best set of terms or a single best categorical organization. Indeed, it is essential to recognize that the domains shown in Figure 4-1 are not easily separable and that a case can be made for multiple different categorizations. For example, different disciplines and researchers have categorized different general cognitive processes under the categorical term “executive function.” General cognitive processes also relate to learning competencies such as persistence and engagement. Similarly, self-regulation has both cognitive and emotional dimensions. It is sometimes categorized as a part of executive function, as a part of socioemotional competence, or as a part of learning competencies. Attention and memory could be considered a part of general cognitive processes, as embedded within executive function, or linked to learning competencies related to persistence. Mental health is closely linked to socioemotional competence, but is also inseparable from health.
The challenge of cleanly separating these concepts highlights a key attribute of all of these domains, which is that they do not develop or operate in isolation. Each enables and mutually supports learning and development in the others. Therefore, the importance of the interactions among the domains is emphasized throughout this chapter. For example, socioemotional
FIGURE 4-1 Report’s organizational approach for the domains of child development and early learning.
competence is important for self-regulation, as are certain cognitive skills, and both emotional and cognitive self-regulation are important for children to be able to exercise learning competencies. Similarly, although certain skills and concept knowledge are distinct to developing proficiency in particular subject areas, learning in these subject areas also both requires and supports general cognitive skills such as reasoning and attention, as well as learning competencies and socioemotional competence. In an overarching example of interactions, a child’s security both physically and in relationships creates the context in which learning is most achievable across all of the domains.
It is less important that all fields of research, practice, and policy adhere to the exact same categorizations, and more important that all conduct their work in a way that is cognizant and inclusive of all the elements that contribute to child development and early learning, and that all fields recognize that they are interactive and mutually reinforcing rather than hierarchical. This point foreshadows a theme that is addressed more fully in subsequent chapters. Because different fields and sectors may not use the same categorizations and vocabulary for these domains and skills, developing practices and policies that support more consistent and continuous development and early learning across birth through age 8 will require a concerted effort to communicate clearly and come to a mutual understanding of the goals for children. To communicate across fields and between research and practice communities requires being aware of the different categorical frameworks and terms that are used and being able to discuss the various concepts and content—and their implications—with clarity across those different frameworks. Practitioners and policy makers will be aided in achieving greater precision and clarity in their actions and decisions if those conducting and communicating future research keep this challenge in mind across domains, especially in those cases in which the taxonomy is most variable (e.g., self-regulation, executive function, general learning competencies).
With these caveats in mind, the remainder of this chapter addresses in turn the domains of child development and early learning depicted in Figure 4-1: cognitive development, including learning of specific subjects; general learning competencies; socioemotional development; and physical development and health. The final section examines a key overarching issue: the effects on child development and early learning of the stress and adversity that is also an important theme in the discussion of the interaction between biology and environment in Chapter 3.
This section highlights what is known about cognitive development in young children. It begins with key concepts from research viewpoints that
have contributed to recent advances in understanding of the developing mind, and then presents the implications of this knowledge for early care and education settings. The following section addresses the learning of specific subjects, with a focus on language and mathematics.
Studies of early cognitive development have led researchers to understand the developing mind as astonishingly competent, active, and insightful from a very early age. For example, infants engage in an intuitive analysis of the statistical regularities in the speech sounds they hear en route to constructing language (Saffran, 2003). Infants and toddlers derive implicit theories to explain the actions of objects and the behavior of people; these theories form the foundation for causal learning and more sophisticated understanding of the physical and social worlds. Infants and young children also are keenly responsive to what they can learn from the actions and words directed to them by other people. This capacity for joint attention may be the foundation that enables humans to benefit from culturally transmitted knowledge (Tomasello et al., 2005). Infants respond to cues conveying the communicative intentions of an adult (such as eye contact and infant-directed speech) and tune in to what the adult is referring to and what can be learned about it. This “natural pedagogy” (Csibra, 2010; Csibra and Gergely, 2009) becomes more sophisticated in the sensitivity of preschoolers to implicit pedagogical guides in adult speech directed to them (Butler and Markman, 2012a,b, 2014). Young children rely so much on what they learn from others that they become astute, by the preschool years, in distinguishing adult speakers who are likely to provide them with reliable information from those who are not (Harris, 2012; Jaswal, 2010; Koenig and Doebel, 2013). This connection of relationships and social interactions to cognitive development is consistent with how the brain develops and how the mind grows, and is a theme throughout this chapter.
Much of what current research shows is going on in young children’s minds is not transparent in their behavior. Infants and young children may not show what they know because of competing demands on their attention, limitations in what they can do, and immature self-regulation. This is one of the reasons why developmental scientists use carefully designed experiments for elucidating what young children know and understand about the world. By designing research procedures that eliminate competing distractions and rely on simple responses (such as looking time and expressions of surprise), researchers seek to uncover cognitive processes that might otherwise be more difficult to see. Evidence derived in this experimental manner, such as the examples in the sections that follow, can be helpful in explaining young children’s rapid growth in language learning, imitation, problem solving, and other skills.
One of the most important discoveries about the developing mind is how early and significantly very young children, even starting in infancy, are uniting disparate observations or discrete facts into coherent conceptual systems (Carey, 2009; Gopnik and Wellman, 2012; Spelke and Kinzler, 2007). From very early on, children are not simply passive observers, registering the superficial appearance of things. Rather, they are building explanatory systems—implicit theories—that organize their knowledge. Such implicit theories contain causal principles and causal relations; these theories enable children to predict, explain, and reason about relevant phenomena and, in some cases, intervene to change them. As early as the first year of life, babies are developing incipient theories about how the world of people, other living things, objects, and numbers operates. It is important to point out that these foundational theories are not simply isolated forms of knowledge, but play a profound role in children’s everyday lives and subsequent education.
One major example of an implicit theory that is already developing as early as infancy is “theory of mind,” which refers to the conceptual framework people use to reason about the mental lives of others as well as themselves. This example is discussed in detail below. Some additional illustrative examples of the development of implicit theories are provided in Box 4-1.
Theory of Mind
People intuitively understand others’ actions as motivated by desires, goals, feelings, intentions, thoughts, and other mental states, and we understand how these mental states affect one another (for example, an unfulfilled desire can evoke negative feelings and a motivation to continue trying to achieve the goal). One remarkable discovery of research on young children is that they are developing their own intuitive “map” of mental processes like these from very early in life (Baillargeon et al., 2010; Saxe, 2013; Wellman and Woolley, 1990). Children’s developing theory of mind transforms how they respond to people and what they learn from them. Infants and young children are beginning to understand what goes on in people’s minds, and how others’ feelings and thoughts are similar to and different from their own.
Infants first have a relatively simple theory of mind. They are aware of some basic characteristics: what people are looking at is a sign of what they are paying attention to; people act intentionally and are goal directed; people have positive and negative feelings in response to things around them; and people have different perceptions, goals, and feelings. Children add to this mental map as their awareness grows. From infancy on, developing
Examples of the Development of Implicit Theories
Theories of Physical Objects
Even babies hold some fundamental principles about how objects move about in space and time (Baillargeon et al., 2009). For example, babies are surprised (as measured by their increased looking time) if an object in one location pops up in another location when they did not see it traverse the space between.
Theories of Numbers
Even babies seem capable of intuitively understanding something that approximates addition and subtraction, and they are surprised when something counter to these principles occurs (Wynn, 1992a). For example, when babies witness one object that is then screened from view and they see that another object is placed behind the screen, they are surprised when the screen is lowered if there is still only one object there.
There has been a recent explosion of research on quantitative abilities of infants and toddlers. These studies have examined these young children’s representations and processing of small exact numbers, as well as their capacities in an approximate number system in which very large numbers can be represented and discriminated from each other (Carey, 2009; Feigenson et al., 2013; Hyde and Spelke, 2011; Pinhas et al., 2014). These very early developing capacities in these two numerical systems lay the foundation for later mathematical abilities that will be taught explicitly to children.
Theories of Living Things
Young children also understand some fundamental characteristics of living things. They distinguish between living and nonliving things; they know living things grow and inanimate objects do not; they know sick or injured people can heal while broken objects do not repair themselves; they attribute life, growth, and biological processes to some sort of vital force or energy, and they know that food is necessary to nourish this vital force (Inagaki and Hatano, 2004).
For example, babies understand observed events in ways that distinguish
theory of mind permeates everyday social interactions—affecting what and how children learn, how they react to and interact with other people, how they assess the fairness of an action, and how they evaluate themselves.
One-year-olds, for example, will look in their mother’s direction when faced with someone or something unfamiliar to “read” mother’s expression and determine whether this is a dangerous or benign unfamiliarity. Infants also detect when an adult makes eye contact, speaks in an infant-directed
between animate and inanimate objects. If 6-month-old babies view a human arm reaching for an object, they interpret this as an intentional agent pursuing a specific goal, and they are surprised if the arm reaches for a different object but not if it changes its trajectory as the object’s location changes. In contrast, babies who see an inanimate rod move on the same trajectory toward an object are surprised if the rod changes its trajectory to pursue the object but not if it continues on the old trajectory toward a new object. The emergence of the ability to make this distinction is tied to the baby’s own capacity to reach for objects—babies need experience reaching on their own to recognize the intent behind reaching in others (Gerson and Woodward, 2014).
An example of building on intuitive understanding to develop a more elaborate understanding of biology comes from a study on teaching preschool through early elementary school children about nutrition. Children at this age have an understanding that people need food to survive, but their implicit theory provides no causal mechanism for how food accomplishes its vital functions. The approach in this study was to move beyond very simplified, nonexplanatory teaching material and instead to teach children in age-appropriate ways that different foods contain different nutrients that are too small to see, which in turn have different functions that are required to support diverse biological processes. The core concepts and causal principles provided a coherent conceptual framework that explains why it is important to eat a variety of healthy foods. Children became able to explain why it is not healthy to eat only broccoli; they could pick a healthier snack based on the variety of foods included; they understood why people need blood to carry nutrients to all parts of the body. Moreover, when assessed at snack time, the children who received this intuitive theory-based training increased their vegetable consumption (Gripshover and Markman, 2013).
In another example of intentionally contributing to a more elaborate biological theory for children at the older end of the birth-to-8 age range, third- and fourth-grade students during the severe acute respiratory syndrome (SARS) epidemic in Hong Kong increased their hand-washing behaviors after receiving lessons that germs are living things that thrive under some circumstances and die in others, and that reproduce quickly under some conditions and very slowly or not at all in others. These lessons provided children with a conceptual framework that explained the reasons behind the standard instructions provided to the control group, which typically are taught as fact-like, rule-like slogans to wash one’s hands and wear a face mask (Au et al., 2008).
manner (such as using higher pitch and melodic intonations), and responds contingently to the infant’s behavior. Under these circumstances, infants are especially attentive to what the adult says and does, thus devoting special attention to social situations in which the adult’s intentions are likely to represent learning opportunities.
Other examples also illustrate how a developing theory of mind underlies children’s emerging understanding of the intentions of others. Take
imitation, for example. It is well established that babies and young children imitate the actions of others. Children as young as 14 to 18 months are often imitating not the literal observed action but the action they thought the actor intended—the goal or the rationale behind the action (Gergely et al., 2002; Meltzoff, 1995). Word learning is another example in which babies’ reasoning based on theory of mind plays a crucial role. By at least 15 months old, when babies hear an adult label an object, they take the speaker’s intent into account by checking the speaker’s focus of attention and deciding whether they think the adult indicated the object intentionally. Only when babies have evidence that the speaker intended to refer to a particular object with a label will they learn that word (Baldwin, 1991; Baldwin and Moses, 2001; Baldwin and Tomasello, 1998).
Babies also can perceive the unfulfilled goals of others and intervene to help them; this is called “shared intentionality.” Babies as young as 14 months old who witness an adult struggling to reach for an object will interrupt their play to crawl over and hand the object to the adult (Warneken and Tomasello, 2007). By the time they are 18 months old, shared intentionality enables toddlers to act helpfully in a variety of situations; for example, they pick up dropped objects for adults who indicate that they need assistance (but not for adults who dropped the object intentionally) (Warneken and Tomasello, 2006). Developing an understanding of others’ goals and preferences and how to facilitate them affects how young children interpret the behavior of people they observe and provides a basis for developing a sense of helpful versus undesirable human activity that is a foundation for later development of moral understanding (cf. Bloom, 2013; Hamlin et al., 2007; Thompson, 2012, 2015).
Developing Implicit Theories: Implications for Adults
The research on the development of implicit theories in children has important implications for how adults work with and educate young children. Failure to recognize the extent to which they are construing information in terms of their lay theories can result in educational strategies that oversimplify material for children. Educational materials guided by the assumption that young children are “concrete” thinkers—that they cannot deal with abstraction or reason hypothetically—leads educators to focus on simple, descriptive activities that can deprive children of opportunities to advance their conceptual frameworks. Designing effective materials in a given domain or subject matter requires knowing what implicit theories children hold, what core causal principles they use, and what misconceptions and gaps in knowledge they have, and then using empirically validated steps to help lead them to a more accurate, more advanced conceptual framework.
Statistical learning refers to the range of ways in which children, even babies, are implicitly sensitive to the statistical regularities in their environment, although they are not explicitly learning or applying statistics. Like the development of implicit theories, this concept of statistical learning counters the possible misconception of babies as passive learners and bears on the vital importance of their having opportunities to observe and interact with the environment. Several examples of statistical learning are provided in Box 4-2.
Understanding Causal Inference
Children’s intuitive understanding of causal inference has long been recognized as a fundamental component of conceptual development. Young children, although not explicitly or consciously experimenting with causality, can experience observations and learning that allow them to conclude that a particular variable X causes (or prevents) an effect Y. Recent advances in the field have documented the ways young children can implicitly use the statistics of how events covary to infer causal relations, make predictions, generate explanations, guide their exploration, and enable them to intervene in the environment. The understanding of causal inference also provides an example of how different cognitive abilities—such as a sensitivity to statistical regularities and the development of implicit theories based on observation and learning (discussed in the two preceding sections and Box 4-2)—interact with and can mutually support each other. There is now a substantial literature on young children’s implicit ability to use what they observe in different conditions to understand the relations between variables. Several examples of young children developing the ability to understand causal inference are provided in Box 4-3.
Sensitivity to Teaching Cues
Csibra and Gergely (2009) argue that humans are equipped with a capacity to realize when someone is communicating something for their benefit and that they construe that information differently than when they merely witness it. As noted previously in the discussion of developing theory of mind, children as early as infancy devote special attention to social situations that are likely to represent learning opportunities because adults communicate that intention. Information learned in such communicative contexts is treated as more generalizable and robust than that learned in a noncommunicative context.
Examples of Statistical Learning
Sensitivity to Conditional Probabilities
Infants can use information about the statistics of syllables in the speech they hear to help them parse words. How do we know from hearing prettybaby that baby is more likely to be a word than tyba? One way is that the conditional probability of by following ba is higher than that of ba following ty. Babies can use such conditional probabilities of syllables following each other to detect word boundaries, that is, to distinguish between clusters of syllables that form a word and clusters that could be different words strung together. In a pioneering study to test this notion, Saffran and colleagues (1996) exposed 8-month-old babies to recordings of trios of syllables that followed each other more frequently and syllables that were at the junctions between these trios and followed each other less frequently. The latter had a lower conditional probability, representing how words compared with nonwords have syllable combinations that occur more frequently. After a period of exposure to the recording, the time the babies spent looking toward a sound source varied depending on whether they heard a trio of syllables that had appeared together more frequently or one that had appeared together less frequently. This increased attention time served as a measure of their understanding of the difference between what they had been exposed to as “words” versus nonwords. Many subsequent studies have both replicated this finding and extended it to demonstrate that the same sensitivity can be seen in babies’ parsing of music (Saffran et al., 1999) and even of how visual displays are organized (Kirkham et al., 2002).
Sensitivity to Sampling Statistics
Babies and young children are sensitive to the statistical likelihood of events, which reveals that they both are attuned to regularities they observe in the world and use such regularities to draw inferences and make predictions based on their observations. In one set of studies, for example, 11-month-old babies were shown a box full of many red balls and only a few white balls. The babies were surprised when balls were poured out of the box and all of them happened to be white or when someone reached into the opaque box and happened to retrieve all white balls. Thus the babies were registering the low proportion of white balls and recognizing the improbability of these events (Xu and Denison, 2009). In an important variation, however, if the experimenter looked into the box as she picked up the balls, the babies were not surprised if all white balls were selected. This finding suggests that babies’ implicit knowledge of theory of mind—in this example, understanding that a person can deliberately select objects—will trump their reasoning based on the sampling distribution.
Examples of Understanding Causal Inference
Distinguishing Causal Variables
One of the first studies of children’s understanding of causal inference showed that children can rule out one variable and isolate another (Gopnik et al., 2001). Preschool children were presented with a machine and told that “blickets” make the machine go. Block A placed on the machine always made it go. Block B was associated with the machine turning on but only when Block A was also on the machine. Children correctly identified Block A as the “blicket” and not Block B. They were also able to intervene correctly to make the machine stop by removing Block A and not Block B.
Using Exploratory Play to Understand Causality
Schulz and Bonawitz (2007) demonstrated that children use exploratory play to help them recognize causal relationships. They presented children (mean age 57 months) with a toy with an ambiguous causal mechanism, being one of two possibilities, or a toy with an unambiguous causal mechanism. In the ambiguous case, children and an adult played with a box that had two levers, one controlled by the child and one by the adult. On the count of three, both the child and the experimenter pressed their levers, and two toys popped out of the box. The child and the experimenter simultaneously released the levers, and both toys disappeared into the box. The ambiguity lay in whether one or both of the levers caused the toys to emerge from the box. In the other case, children had a couple of trials in which they and the experimenter depressed the levers simultaneously on the count of three, but also had trials in which they depressed their levers individually; in the latter cases, the child’s lever controlled one toy, while the experimenter’s lever controlled the other. After this interaction, a different toy was brought out, and children could play with either of the toys. Children who witnessed ambiguous evidence for the causal mechanism played with the familiar toy more than the novel toy, while children who had seen unambiguous evidence for the mechanism elected to play more with a novel toy. The causal ambiguity of the familiar toy motivated children to continue their exploration. Schulz and Bonawitz (2007, p. 1049) conclude that “the exploratory play of even very young children appears to reflect some of the logic of scientific inquiry.”
Using an Understanding of Causality to Solve Problems
Babies also can use the statistical distribution of events to infer the reason for failed actions and then deploy strategies to solve the problem. Suppose babies cannot get a toy to work. Is the failure because the toy is broken or because they do not know how to use it properly? In one series of studies (Gweon and Schulz, 2011), 16-month-old babies witnessed two adults pressing a button on a toy that
then did or did not play music. In one condition, one of the adults succeeded twice in getting the toy to play, while the other adult failed twice. In the other condition, each adult failed once and succeeded once. Babies were then handed a similar toy to play with that failed to produce the music when they pressed the button. Babies who earlier saw one adult succeed and the other fail turned to their mothers for help in getting the toy to work. In contrast, babies who saw each adult succeed and fail once reached for a different toy. Thus, depending on the prior information babies observed, they inferred that there was either some lack of ability on their part or some problem with the toy. When they inferred that the problem was with their ability, they turned to their mother for help; when they inferred that the toy was broken, they reached for another one. Through observing intentional, goal-directed behavior of others, preverbal babies are quickly identifying the goal, registering patterns of others’ successes and failures, using those statistical patterns to infer the more likely causes of failure, and subsequently recruiting that information to interpret their own failure and guide their choice of appropriate solutions.
In one study, for example, 9-month-old babies saw an adult either reach for an object (a noncommunicative act) or point to an object (a communicative act). The entire display was then screened from view, and after a brief delay, the curtains were opened, and babies saw either the same object in a new location or a new object in the same location. The short delay imposed a memory requirement, and for babies this young, encoding both the location and the identity of the object taxes their memory. The location of the object will typically be more salient and memorable to babies than the object’s properties, but the prediction of this study was that babies who saw the adult point to the object would construe the pointing as a communicative act—“this adult is showing me something”—and would thus be more likely to encode the properties as opposed to the location of the object. Babies’ looking times served as a measure of their surprise at or interest in an unexpected event. As predicted, babies appeared to encode different aspects of the event in the different conditions. When they had previously witnessed the adult reaching for the object, they were surprised when the object was in a new location but showed no renewed interest when there was a different object in the old location. In contrast, when babies first saw an adult point to the object, they were surprised when a new object appeared in the old location but not when the old object had changed locations (Yoon et al., 2008).
Infants’ Sensitivity to Teaching Cues: Implications for Adults
Babies have the capacity to realize when someone is communicating something for their benefit and therefore to construe information differently than when they merely witness it. When adults use face-to-face contact, call a baby’s name, and point for the baby’s benefit, these signals lead babies to recognize that someone is teaching them something, and this awareness can affect how and what they learn.
The significance of eye contact and other communication cues also is evident in research on whether, how, and when young children learn from video and other forms of digital media. Experiments conducted with 24-month-olds, for example, revealed that they can learn from a person on a video screen if that person is communicating with them through a webcam-like environment, but they showed no evidence of learning from a prerecorded video of that person. The webcam environment included social cues, such as back-and-forth conversation and other forms of social contact that are not possible in prerecorded video. Other studies found that toddlers learned verbs better during Skype video chats than during prerecorded video chats that did not allow for authentic eye contact or back-and-forth interaction (Roseberry et al., 2014; Troseth et al., 2006). (See also Chapter 6 for more on technology and learning.)
The benefits of communicative pedagogical contexts for the conceptual development of preschool children also have been investigated. In one set of studies, 4-year-old children were exposed to a novel object’s function either by seeing an adult deliberately use the object or by seeing the adult deliberately use the object after maintaining eye contact with the child and saying “watch this.” In both conditions, children noticed the object’s property and attempted to elicit it from other similar objects. But when those objects were doctored to be nonfunctional, the children in the nonpedagogical condition quickly abandoned their attempts to elicit the property and played with the objects in some other way. Children who saw the same evidence but with direct communication for their benefit persisted in trying to elicit the property from other objects (Butler and Markman, 2012a,b). In other words, children’s conviction that other similar objects should have the same unforeseen property was bolstered by their belief that the adult was performing the function for their benefit. Moreover the intentional (but nonpedagogical) condition versus the pedagogical condition produced strikingly different conceptions of the function (Butler and Markman, 2014). Four- and 5-year-old children witnessed an object’s function and were then given a set of objects to play with. Some objects were identical in appear-
ance to the first object, while some differed in color (in one study) or shape (in another). Half of the objects of each color (or shape) had the unforeseen property, and half did not. Children were told they could play with the objects for a while and then should put them away in their appropriate boxes when done. The goal was to see whether children would sort the objects by the salient perceptual property (color or shape) or by function. Children in the pedagogical condition viewed the function as definitive and classified the objects by systematically testing each to see whether it had the function, while children in the nonpedagogical condition sorted by the salient color or shape. Thus, identical evidence is construed differently when children believe it has been produced for their benefit.
Effects of Adult Language on Cognition
Understanding the power of language is important for people who interact with children. Simple labels can help children unify disparate-looking things into coherent categories; thus labeling is a powerful way to foster conceptual development. Labels also can reify categories or concepts in ways that may or may not be intended. For example, frequently hearing “boys and girls” line up for recess, quiet down, etc. implicitly reinforces gender as an important dimension, compared with saying “children.” Box 4-4 presents examples of linguistic distinctions that affect children’s construction of conceptual systems.
Effects of Language Used by Adults on Children’s Cognitive Development: Implications for Adults
Awareness of the benefits and pitfalls of the language used by adults is important for people who interact with children. The language used by adults affects cognitive growth and learning in children in many subtle ways. Labeling is a powerful way to foster conceptual development. Simple labels can help children unify disparate things into coherent categories, but can also have the unintended consequence of reinforcing categories or concepts that are not desirable.
Examples of the Effects of Adult Language on Cognition
Effects of Labeling Objects on Inductive Reasoning
Some kinds of categories—two round balls, for example—are fairly easy to form, such that even babies treat the objects as similar. But many objects that adults view as members of the same category are perceptually dissimilar, and children would not, on their own, categorize them together. Some categories have very diverse members: consider a greyhound and a bichon frise as dogs, or a tie and a raincoat as clothing. Atypical members of categories—thinking of a penguin as a bird, for example—also are difficult for children to categorize on their own. Hearing perceptually diverse objects called by the same label enables children to treat them as members of the same category, which in turn affects the kinds of inductive inferences children draw about them (cf. Gelman, 2003). Even very young children will base their inductive inferences on the category to which objects belong rather than their perceptual features when the objects are labeled. Children who hear, for example, a flamingo called a “bird” and are told that it feeds mashed-up food to its babies whereas a bat feeds milk to its babies will judge that a raven, called a bird, will feed its babies mashed-up food even though it looks more like a bat than a flamingo (Gelman and Markman, 1987). The power of labels to influence children’s inductive reasoning has been demonstrated in children as young as 13 months old. Thus, the simple act of providing a label for objects affects children’s learning, and what might appear to be merely acquisition of vocabulary, actually has important consequences in shaping a child’s conceptual development and knowledge. Providing a common label for perceptually disparate objects also is a way of transmitting cultural knowledge to children. This effect of labeling objects speaks to one of the ways in which ordinary interaction with babies enriches their cognitive development and early learning (Graham et al., 2004). While categorization has many benefits for developing inductive reasoning, it can also ultimately be associated with inferences that exaggerate differences between categories and similarities within categories. This may be linked to some undesirable consequences, such as stereotyping or prejudice based on these inferences (Master et al., 2012).
Effects of Generic Language on Children’s Cognition
Generic language—for example, “dogs bark” rather than “this dog is barking”—conveys information about an entire category. It is impossible for any individual to experience first-hand all of the exemplars of a category. The use of generics is thus an indispensable way of learning about the category as a whole. Generics are a powerful way of conveying general facts, properties, or information about a category, and those generalizations often can stand even in the face of counterexamples (Gelman, 2003). The generic statement “dogs bark” is considered true, for example, even though some dogs do not bark and the universal statement “all dogs bark” can be falsified by a counterexample. Therefore, not only
are generic statements an important means of conveying generalizations, but they also lead to a stable form of knowledge that is highly resistant to counterexamples. This stability has many advantages, but as with categorization, it also can be problematic—for example, generic statements about social categories can reify the categories and beliefs about them. When an individual encounters members of a social category that do not share the relevant trait or behavior, those people may then be seen as exceptions but the generalization will still stand.
Properties conveyed by generics also are construed as central or essential to the category (Cimpian and Markman, 2009). Four- and 5-year-old children given the same information conveyed using generic versus nongeneric phrases interpret the information quite differently. Hearing, for example, that “this snake has holes in its teeth” and then being asked why, preschool children come up with explanations such as it doesn’t brush its teeth so it has cavities or it bit into a rock. But hearing that “snakes have holes in their teeth” and then being asked why, children come up with explanations such as they squirt poison out of the holes. Subtle differences in generic versus nongeneric language used to convey information to children can shape the kinds of generalizations they make, the strength of those generalizations, and the extent to which properties are considered central or defining of the category. Here, too, generics can sometimes play an unwanted role (Cimpian and Markman, 2011). Preschoolers who heard that “girls are really good at a game called gorp” would explain this by referring to more central, inherent causes—for example, because girls are smart. Those children who heard “this girl is really good at a game called gorp” would more commonly invoke effort and practice. Dweck and colleagues have shown that children who believe an ability is inherent and fixed are more likely to give up when faced with failure and to lose motivation for and interest in a task, while children who view an ability as malleable are more likely to take on the challenge and work to improve their skill. Therefore, adults’ use of generic rather than nongeneric praise for children may undermine their achievement motivation, leading them to believe that their performance is due to an inherent ability (or lack thereof) rather than to effort, practice, and persistence (Cimpian, 2010; Cimpian et al., 2007).
Conclusions About Cognitive Development and Early Learning
Learning begins prenatally, and children are not only “ready to learn” but already actively learning from the time they are born. From birth, children’s minds are active and inquisitive, and early thinking is insightful and complex. Many of the foundations of sophisticated forms of learning, including those important to academic success, are established in the earliest years of life.
Development and early learning can be supported continuously as a child develops, and early knowledge and skills inform and influence future learning. When adults understand how the mind develops, what progress children make in their cognitive abilities, and how active inquiry and learning are children’s natural inclination, they can foster cognitive growth by supporting children’s active engagement with new experiences and providing developmentally appropriate stimulation of new learning through responsive, secure, and sustained caregiving relationships.
Implications for Care and Education Settings and Practitioners
The research findings on cognitive development in young children summarized above reflect an evolving understanding of how the mind develops during the early years and should be part of the core knowledge that influences how care and education professionals support young children’s learning, as discussed in Chapter 7. Many of these concepts describe cognitive processes that are implicit. By contrast with the explicit knowledge that older children and adults can put into words, implicit knowledge is tacit or nonconscious understanding that cannot readily be consciously described (see, e.g., Mandler, 2004). Examples of implicit knowledge in very young children include many of the early achievements discussed above, such as their implicit theories of living things and of the human mind and their nonconscious awareness of the statistical frequency of the associations among speech sounds in the language they are hearing. Infants’ and young children’s “statistical learning” does not mean that they can count, nor are their “implicit theories” consciously worked out. Not all early learning is implicit, of course. Very young children are taking significant strides in their explicit knowledge of language, the functioning of objects, and the characteristics of people and animals in the world around them. Thus early learning occurs on two levels: the growth of knowledge that is visible and apparent, and the growth of implicit understanding that is sometimes more difficult to observe.
This distinction between implicit and explicit learning can be confusing to early childhood practitioners (and parents), who often do not observe or recognize evidence for the sophisticated implicit learning—or even the explicit learning—taking place in the young children in their care. Many of the astonishingly competent, active, and insightful things that research on early cognitive development shows are going on in young children’s minds are not transparent in their behavior. Instead, toddlers and young children seem highly distractable, emotional, and not very capable of managing their impulses. All of these observations about young children are true, but at the same time, their astonishing growth in language skills, their very different
ways of interacting with objects and living things, and their efforts to share attention (such as through pointing) or goals (such as through helping) with an adult suggest that the cognitive achievements demonstrated in experimental settings have relevance to their everyday behavior.
This point is especially important because the cognitive abilities of young children are so easily underestimated. In the past, for example, the prevalent belief that infants lack conceptual knowledge meant that parents and practitioners missed opportunities to explore with them cause and effect, number, or symbolic play. Similarly, the view that young children are egocentric caused many adults to conclude that there was little benefit to talking about people’s feelings until children were older—this despite the fact that most people could see how attentive young children were to others’ emotions and how curious about their causes.
In light of these observations, how do early educators contribute to the cognitive growth of children in their first 3 years? One way is by providing appropriate support for the learning that is occurring in these very young children (see, e.g., Copple et al., 2013). Using an abundance of child-directed language during social interaction, playing counting games (e.g., while stacking blocks), putting into words what a classroom pet can do or why somebody looks sad, exploring together what happens when objects collide, engaging in imitative play and categorization (sorting) games—these and other shared activities can be cognitively provocative as long as they remain within the young child’s capacities for interest and attention. They also build on understandings that young children are implicitly developing related to language; number; object characteristics; and implicit theories of animate and inanimate objects, physical causality, and people’s minds. The purpose of these and other activities is not just to provide young children with cognitive stimulation, but also to embed that stimulation in social interaction that provokes young children’s interest, elicits their curiosity, and provides an emotional context that enables them to focus their thinking on new discoveries. The central and consistent feature of all these activities is the young child’s shared activity with an adult who thoughtfully capitalizes on his or her interests to provoke cognitive growth. The implications for instructional practices and curricula for educators working with infants and toddlers are discussed further in Chapter 6.
Another way that educators contribute to the cognitive growth of infants and toddlers is through the emotional support they provide (Jamison et al., 2014). Emotional support is afforded by the educator’s responsiveness to young children’s interests and needs (including each child’s individual temperament), the educator’s development of warm relationships with children, and the educator’s accessibility to help when young children are exploring on their own or interacting with other children (Thompson, 2006). Emotional support of this kind is important not only as a positive
accompaniment to the task of learning but also as an essential prerequisite to the cognitive and attentional engagement necessary for young children to benefit from learning opportunities. Because early capacities to self-regulate emotion are so limited, a young child’s frustration or distress can easily derail cognitive engagement in new discoveries, and children can lose focus because their attentional self-regulatory skills are comparably limited. An educator’s emotional support can help keep young children focused and persistent, and can also increase the likelihood that early learning experiences will yield successful outcomes. Moreover, the secure attachments that young children develop with educators contribute to an expectation of adult support that enables young children to approach learning opportunities more positively and confidently. Emotional support and socioemotional development are discussed further later in this chapter.
The characteristics of early learning call for specific curricular approaches and thoughtful professional learning for educators, but it is also true that less formal opportunities to stimulate early cognitive growth emerge naturally in children’s everyday interactions with a responsive adult. Consider, for example, a parent or other caregiver interacting with a 1-year-old over a shape-sorting toy. As they together are choosing shapes of different colors and the child is placing them in the appropriate (or inappropriate) cutout in the bin, the adult can accompany this task with language that describes what they are doing and why, and narrates the child’s experiences of puzzlement, experimentation, and accomplishment. The adult may also be using number words to count the blocks as they are deposited. The baby’s attention is focused by the constellation of adult behavior—infant-directed language, eye contact, and responsiveness—that signals the adult’s teaching, and this “pedagogical orientation” helps focus the young child’s attention and involvement. The back-and-forth interaction of child and adult activity provides stimulus for the baby’s developing awareness of the adult’s thinking (e.g., she looks at each block before commenting on it or acting intentionally on it) and use of language (e.g., colors are identified for each block, and generic language is used to describe blocks in general). In this interaction, moreover, the baby is developing both expectations for what this adult is like—safe, positive, responsive—and skills for social interaction (such as turn taking). Although these qualities and the learning derived from them are natural accompaniments to child-focused responsive social interaction with an adult caregiver, the caregiver’s awareness of the child’s cognitive growth at this time contributes significantly to the adult’s ability to intentionally support new discovery and learning.
As children further develop cognitively as preschoolers, their growth calls for both similar and different behavior by the adults who work with them. While the educator’s emotional support and responsiveness remain important, children from age 3 to 5 years become different kinds of thinkers
than they were as infants and toddlers (NRC, 2001). First, they are more consciously aware of their knowledge—much more of their understanding is now explicit. This means they are more capable of deliberately enlisting what they know into new learning situations, although they are not yet as competent or strategic in doing so as they will be in the primary grades. When faced with a problem or asked a question, they are more capable of offering an answer based on what they know, even when their knowledge is limited. Second, preschoolers are more competent in learning from their deliberate efforts to do so, such as trial-and-error or informal experimentation. While their success in this regard pales by comparison with the more strategic efforts of a grade-schooler, their “let’s find out” approach to new challenges reflects their greater behavioral and mental competence in figuring things out. Third, preschoolers also are intuitive and experiential, learning by doing rather than figuring things out “in the head.” This makes shared activities with educators and peers potent opportunities for cognitive growth.
Nonetheless, the potential to underestimate the cognitive abilities of young children persists in the preschool and kindergarten years. In one study, for example, children’s actual performance was six to eight times what was estimated by their own preschool teachers and other experts in consulting, teacher education, educational research, and educational development (Claessens et al., 2014; Van den Heuvel-Panhuizen, 1996). Such underestimation represents a lost opportunity that can hinder children’s progress. A study in kindergarten revealed that teachers spent most of their time in basic content that children already knew, yet the children benefited more from advanced reading and mathematics content (Claessens et al., 2014)—an issue discussed in depth in Chapter 6. Unfortunately, when care and education professionals underestimate children’s abilities to understand and learn subject-matter content, the negative impact is greatest on those with the fewest prior learning experiences (Bennett et al., 1984; Clements and Sarama, 2014).
Conversely, when educators practice in a way that is cognizant of the cognitive progress of children at this age, they can more deliberately enlist the preschool child’s existing knowledge and skills into new learning situations. One example is interactive storybook reading, in which children describe the pictures and label their elements while the adult and child ask and answer questions of each other about the narrative. Language and literacy skills also are fostered at this age by the adult’s use of varied vocabulary in interaction with the child, as well as by extending conversation on a single topic (rather than frequently switching topics), asking open-ended questions of the child, and initiating conversation related to the child’s experiences and interests (Dickinson, 2003; Dickinson and Porche, 2011; Dickinson and Tabors, 2001). In each case, dialogic conversation about text
or experience draws on while also extending children’s prior knowledge and language skills. Language and literacy skills are discussed further in a subsequent section of this chapter, as well as in Chapter 6.
Another implication of these cognitive changes is that educators can engage preschool children’s intentional activity in new learning opportunities. Children’s interest in learning by doing is naturally suited to experimental inquiry related to science or other kinds of inquiry-based learning involving hypothesis and testing, especially in light of the implicit theories of living things and physical causality that children bring to such inquiry (Samarapungavan et al., 2011). In a similar manner, board games can provide a basis for learning and extending number concepts. In several experimental demonstrations, when preschool children played number board games specifically designed to foster their mental representations of numerical quantities, they showed improvements in number line estimates, count-on skill, numerical identification, and other important quantitative concepts (Laski and Siegler, 2014).
Other research has shown that instructional strategies that promote higher-level thinking, creativity, and even abstract understanding, such as talking about ideas or about future events, is associated with greater cognitive achievement by preschool-age children (e.g., Diamond et al., 2013; Mashburn et al., 2008). For example, when educators point out how cardinal numbers can be used to describe diverse sets of elements (four blocks, four children, 4 o’clock), it helps them generalize an abstract concept (“fourness”) that describes a set rather than the characteristics of each element alone. These activities also can be integrated into other instructional practices during a typical day.
Another implication of the changes in young children’s thinking during the preschool years concerns the motivational features of early learning. Preschool-age children are developing a sense of themselves and their competencies, including their academic skills (Marsh et al., 1998, 2002). Their beliefs about their abilities in reading, counting, vocabulary, number games, and other academic competencies derive from several sources, including spontaneous social comparison with other children and feedback from teachers (and parents) concerning their achievement and the reasons they have done well or poorly. These beliefs influence, in turn, children’s self-confidence, persistence, intrinsic motivation to succeed, and other characteristics that may be described as learning skills (and are discussed more extensively later in this chapter). Consequently, how teachers provide performance feedback to young children and support for their self-confidence in learning situations also is an important predictor of children’s academic success (Hamre, 2014).
In the early elementary years, children’s cognitive processes develop further, which accordingly influences the strategies for educators in early
elementary classrooms. Primary grade children are using more complex vocabulary and grammar. They are growing in their ability to make mental representations, but they still have difficulty grasping abstract concepts without the aid of real-life references and materials (Tomlinson, 2014). This is a critical time for children to develop confidence in all areas of life. Children at this age show more independence from parents and family, while friendship, being liked and accepted by peers, becomes more important. Being in school most of the day means greater contact with a larger world, and children begin to develop a greater understanding of their place in that world (CDC, 2014).
Children’s growing ability to self-regulate their emotions also is evident in this period (discussed more extensively later in this chapter). Children understand their own feelings more and more, and learn better ways to describe experiences and express thoughts and feelings. They better understand the consequences of their actions, and their focus on concern for others grows. They are very observant, are willing to play cooperatively and work in teams, and can resolve some conflicts without seeking adult intervention (CDC, 2014). Children also come to understand that they can affect others’ perception of their emotions by changing their affective displays (Aloise-Young, 1993). Children who are unable to self-regulate have emotional difficulties that may interfere with their learning. Just as with younger children, significant adults in a child’s life can help the child learn to self-regulate (Tomlinson, 2014).
Children’s increasing self-regulation means they have a greater ability to follow instructions independently in a manner that would not be true of preschool or younger children. Educators can rely on the growing cognitive abilities in elementary school children in using instructional approaches that depend more independently on children’s own discoveries, their use of alternative inquiry strategies, and their greater persistence in problem solving. Educators in these settings are scaffolding the skills that began to develop earlier, so that children are able to gradually apply those skills with less and less external support. This serves as a bridge to succeeding in upper primary grades, so if students lack necessary knowledge and skills in any domain of development and learning, their experience during the early elementary grades is crucial in helping them gain those competencies.
Building on many of the themes that have emerged from this discussion, the following sections continue by looking in more depth at cognitive development with respect to learning specific subjects and then at other major elements of development, including general learning competencies, socioemotional development, and physical development and health.
Interrelationships among different kinds of skills and abilities contribute to young children’s acquisition of content knowledge and competencies, which form a foundation for later academic success. These skills and abilities include the general cognitive development discussed above, the general learning competencies that allow children to control their own attention and thinking; and the emotion regulation that allows children to control their own emotions and participate in classroom activities in a productive way (the latter two are discussed in sections later in this chapter). Still another important category of skills and abilities, the focus of this section, is subject-matter content knowledge and skills, such as competencies needed specifically for learning language and literacy or mathematics.
Content knowledge and skills are acquired through a developmental process. As children learn about a topic, they progress through increasingly sophisticated levels of thinking with accompanying cognitive components. These developmental learning paths can be used as the core of a learning trajectory through which students can be supported by educators who understand both the content and those levels of thinking. Each learning trajectory has three parts: a goal (to develop a certain competence in a topic), a developmental progression (children constructing each level of thinking in turn), and instructional activities (tasks and teaching practices designed to enable thinking at each higher level). Learning trajectories also promote the learning of skills and concepts together—an effective approach that leads to both mastery and more fluent, flexible use of skills, as well as to superior conceptual understanding (Fuson and Kwon, 1992; National Mathematics Advisory Panel, 2008). See Chapter 6 for additional discussion of using learning trajectories and other instructional practices.
Every subject area requires specific content knowledge and skills that are acquired through developmental learning processes. It is not possible to cover the specifics here for every subject area a young child learns. To maintain a feasible scope, this chapter covers two core subject areas: (1) language and literacy and (2) mathematics. This scope is not meant to imply that learning in other areas, such as science, engineering, social studies, or the arts, is unimportant or less subject specific. Rather, these two were selected because they are foundational for other subject areas and for later academic achievement, and because how they are learned has been well studied in young children compared with many other subject areas.
Language and Literacy
Children’s language development and literacy development are central to each other. The development of language and literacy includes knowl-
edge and skills in such areas as vocabulary, syntax, grammar, phonological awareness, writing, reading, comprehension, and discourse skills. The following sections address the development of language and literacy skills, including the relationship between the two; the role of the language-learning environment; socioeconomic disparities in early language environments; and language and literacy development in dual language learners.
Development of Oral Language Skills
Language skills build in a developmental progression over time as children increase their vocabulary, average sentence length, complexity and sophistication of sentence structure and grammar, and ability to express new ideas through words (Kipping et al., 2012). Catts and Kamhi (1999) define five features of language that both work independently and interact as children develop language skills: phonology (speech sounds of language), semantics (meanings of words and phrases), morphology (meaningful parts of words and word tenses), syntax (rules for combining and ordering words in phrases), and pragmatics (appropriate use of language in context). The first three parameters combined (phonology, semantics, and morphology) enable listening and speaking vocabulary to develop, and they also contribute to the ability to read individual words. All five features of language contribute to the ability to understand sentences, whether heard or read (O’Connor, 2014). Thus, while children’s development of listening and speaking abilities are important in their own right, oral language development also contributes to reading skills.
Developing oral communication skills are closely linked to the interactions and social bonds between adults and children. As discussed earlier in this chapter, parents’ and caregivers’ talk with infants stimulates—and affects—language comprehension long before children utter their first words. This comprehension begins with pragmatics—the social aspects of language that include facial and body language as well as words, such that infants recognize positive (and negative) interactions. Semantics (understanding meanings of words and clusters of words that are related) soon follows, in which toddlers link objects and their attributes to words. Between the ages of 2 and 4, most children show dramatic growth in language, particularly in understanding the meanings of words, their interrelationships, and grammatical forms (Scarborough, 2001).
Karmiloff and Karmiloff-Smith (2001) suggest that children build webs among words with similar semantics, which leads to broader generalizations among classes of related words. When adults are responsive to children’s questions and new experiences, children expand their knowledge of words and the relationships among them. Then, as new words arise from conversation, storytelling, and book reading, these words are linked to
existing webs to further expand the store of words children understand through receptive language and use in their own conversation. The more often adults use particular words in conversation with young children, the sooner children will use those words in their own speech (Karmiloff and Karmiloff-Smith, 2001). Research has linked the size of vocabulary of 2-year-olds to their reading comprehension through fifth grade (Lee, 2011).
One of the best-documented methods for improving children’s vocabularies is interactive storybook reading between children and their caregivers (O’Connor, 2014). Conversations as stories are read improve children’s vocabulary (Hindman et al., 2008; Weizman and Snow, 2001), especially when children are encouraged to build on the possibilities of storybooks by following their interests (Whitehurst et al., 1988; Zucker et al., 2013). Book reading stimulates conversation outside the immediate context—for example, children ask questions about the illustrations that may or may not be central to the story. This introduces new words, which children attach to the features of the illustrations they point out and incorporate into book-centered conversations. This type of language, removed from the here and now, is decontextualized language. Children exposed to experiences not occurring in their immediate environment are more likely to understand and use decontextualized language (Hindman et al., 2008). Repeated routines also contribute to language development. As books are read repeatedly, children become familiar with the vocabulary of the story and their conversations can be elaborated. Routines help children with developmental delays acquire language and use it more intelligibly (van Kleek, 2004).
Conversation around a story’s content and emphasis on specific words in the text (i.e., the phonological and print features of words alongside their meanings) have long-term effects (Zucker et al., 2013). The quality of adult readers’ interactions with children appears to be especially important to children’s vocabulary growth (see also Coyne et al., 2009; Justice et al., 2005). In a study with preschool children, Zucker and colleagues (2013) found that teachers’ intentional talk during reading had a longer-lasting effect on the children’s language skills than the frequency of the teachers’ reading to the children. Moreover, the effect of the teachers’ talk during reading was not moderated by the children’s initial vocabulary or literacy abilities. The long-term effect of high-quality teacher–child book-centered interactions in preschool lasted through the end of first grade.
New research shows that the effects of interactive reading also hold when adapted to the use of digital media as a platform for decontextualized language and other forms of language development. A study of videobooks showed that when adults were trained to use dialogic questioning techniques with the videos, 3-year-olds learned new words and recalled the books’ storylines (Strouse et al., 2013). However, a few studies of e-books also have shown that the bells and whistles of the devices can get in the
way of those back-and-forth conversations if the readers and the e-book designers are not intentional about using the e-books to develop content knowledge and language skills (Parish-Morris et al., 2013). (See also the discussion of effective use of technology in instruction in Chapter 6.)
Alongside developing depth of vocabulary (including the meaning of words and phrases and their appropriate use in context), other important parameters of language development are syntax (rules for combining and ordering words in phrases, as in rules of grammar) and morphology (meaningful parts of words and word tenses). Even before the age of 2, toddlers parse a speech stream into grammatical units (Hawthorne and Gerken, 2014). Long before preschool, most children join words together into sentences and begin to use the rules of grammar (i.e., syntax) to change the forms of words (e.g., adding s for plurals or ed for past tense). Along with these morphemic changes to words, understanding syntax helps children order the words and phrases in their sentences to convey and to change meaning. Before children learn to read, the rules of syntax help them derive meaning from what they hear and convey meaning through speech. Cunningham and Zibulsky (2014, p. 45) describe syntactic development as “the ability to understand the structure of a sentence, including its tense, subject, and object.”
Although syntactic understanding develops for most children through conversation with adults and older children, children also use these rules of syntax to extract meaning from printed words. This becomes an important reading skill after first grade, when text meaning is less likely to be supported with pictures. Construction of sentences with passive voice and other complex, decontextualized word forms are more likely to be found in books and stories than in directive conversations with young children. An experimental study illustrates the role of exposure to syntactic structures in the development of language comprehension (Vasilyeva et al., 2006). Four-year-olds listened to stories in active or passive voice. After listening to ten stories, their understanding of passages containing these syntactic structures was assessed. Although students in both groups understood and could use active voice (similar to routine conversation), those who listened to stories with passive voice scored higher on comprehension of this structure.
Children’s understanding of morphology—the meaningful parts of words—begins in preschool for most children, as they recognize and use inflected endings to represent verb tense (e.g., -ing, -ed, -s) and plurals, and continues in the primary grades as children understand and use prefixes and suffixes. By second and third grade, children’s use of morphemes predicts their reading comprehension (Nagy et al., 2006; Nunes et al., 2012).
Development of Literacy Skills
Literacy skills follow a developmental trajectory such that early skills and stages lead into more complex and integrated skills and stages (Adams, 1990). For example, phonemic awareness is necessary for decoding printed words (Ball and Blachman, 1991; Bradley and Bryant, 1983; O’Connor et al., 1995), but it is not sufficient. Students need to understand the alphabetic principle (that speech sounds can be represented by letters of the alphabet, which is how speech is captured in print) before they can use their phonemic awareness (the ability to hear and manipulate sounds in spoken words) to independently decode words they have never seen before (Byrne and Fielding-Barnsley, 1989; O’Connor and Jenkins, 1995). Thus, instruction that combines skill development for 4- to 6-year-old children in phonemic awareness, letter knowledge, and conceptual understanding and use of these skills is more effective than teaching the skills in isolation (Byrne and Fielding-Barnsley, 1989; O’Connor and Jenkins, 1995).
Seminal theories and studies of reading describe an inextricable link between language development and reading achievement (e.g., Byrne and Fielding-Barnsley, 1995; Gough and Tunmer, 1986; Hoover and Gough, 1990; Johnston and Kirby, 2006; Joshi and Aaron, 2000; Tunmer and Hoover, 1993; Vellutino et al., 2007). Early oral language competencies predict later literacy (Pearson and Hiebert, 2010). Not only do young children with stronger oral language competencies acquire new language skills faster than students with poorly developed oral language competencies (Dickinson and Porche, 2011), but they also learn key literacy skills faster, such as phonemic awareness and understanding of the alphabetic principle (Cooper et al., 2002). Both of these literacy skills in turn facilitate learning to read in kindergarten and first grade. By preschool and kindergarten listening and speaking abilities have long-term impacts on children’s reading and writing abilities in third through fifth grade (Lee, 2011; Nation and Snowling, 1999; Sénéchal et al., 2006).
Vocabulary development (a complex and integrative feature of language that grows continuously) and reading words (a skill that most children master by third or fourth grade) (Ehri, 2005) are reciprocally related, and both reading words accurately and understanding what words mean contribute to reading comprehension (Gough et al., 1996). Because comprehending and learning from text depend largely upon a deep understanding of the language used to communicate the ideas and concepts expressed, oral language skills (i.e., vocabulary, syntax, listening comprehension) are at the core of this relationship between language and reading (NICHD Early Child Care Research Network, 2005; Perfetti, 1985; Perfetti and Hart, 2002). For example, children with larger speaking vocabularies in preschool may have an easier time with phoneme awareness and the alphabetic
principle because they can draw on more words to explore the similarities among the sounds they hear in spoken words and the letters that form the words (Metsala and Walley, 1998). Each word a child knows can influence how well she or he understands a sentence that uses that word, which in turn can influence the acquisition of knowledge and the ability to learn new words. A stronger speaking and listening vocabulary provides a deeper and wider field of words students can attempt to match to printed words. Being bogged down by figuring out what a given word means slows the rate of information processing and limits what is learned from a sentence. Thus, differences in early vocabulary can have cascading, cumulative effects (Fernald et al., 2013; Huttenlocher, 1998). The transition from speaking and listening to reading and writing is not a smooth one for many children. Although a well-developed vocabulary can make that transition easier, many children also have difficulty learning the production and meanings of words. Longitudinal studies of reading disability have found that 70 percent of poor readers had a history of language difficulties (Catts et al., 1999).
Conclusion About the Development of Language and Literacy Skills
The oral language and vocabulary children learn through interactions with parents, siblings, and caregivers and through high-quality interactions with educators provide the foundation for later literacy and for learning across all subject areas, as well as for their socioemotional well-being. The language interactions children experience at home and in school influence their developing minds and their understanding of concepts and ideas.
Role of the Language-Learning Environment
Today’s science of reading development focuses more broadly than on teaching children to read the actual words on a page. As stressed throughout this report, young children’s development entails a back-and-forth process of social interactions with knowledgeable others in their environment (Bruner, 1978; NRC and IOM, 2000; Vygotsky, 1978, 1986), and research has focused on the language of these interactions, examining how children’s linguistic experiences influence aspects of their development over time, including their literacy development. The daily talk to which children are exposed and in which they participate is essential for developing their minds—a key ingredient for building their knowledge of the world and their understanding of concepts and ideas. In turn, this conceptual knowledge is a cornerstone of reading success.
The bulk of the research on early linguistic experiences has investigated language input in the home environment, demonstrating the features of
caregivers’ (usually the mother’s) speech that promote language development among young children. The evidence accumulated emphasizes the importance of the quantity of communicative input (i.e., the number of words and sentences spoken) as well as the quality of that input, as measured by the variety of words and syntactic structures used (for relevant reviews, see Rowe, 2012; Vasilyeva and Waterfall, 2011). Because children’s language development is sensitive to these inputs, variability in children’s language-based interactions in the home environment explains some of the variance in their language development.
A smaller but growing and compelling research base is focused on how children’s literacy skills are influenced by language use in early care and education settings and schools—for example, linguistic features of these settings or elementary school teachers’ speech and its relationship to children’s reading outcomes (Greenwood et al., 2011). This research has particularly relevant implications for educational practices (discussed further in Chapter 6).
The language environment of the classroom can function as a support for developing the kind of language that is characteristic of the school curriculum—for example, giving children opportunities to develop the sophisticated vocabulary and complex syntax found in texts, beginning at a very early age (Schleppegrell, 2003; Snow and Uccelli, 2009). Moreover, advances in cognitive science suggest that it is not enough to be immersed in environments that offer multiple opportunities for exposure to varied and rich language experiences. Rather, the process also needs to be socially mediated through more knowledgeable persons who can impart their knowledge to the learner; again, social interaction is a critical component of cognitive development and learning. Early childhood settings and elementary classrooms thus not only present opportunities for exposure to varied language- and literacy-rich activities (whether written or spoken), but also provide a person who is expert in mediating the learning process—the educator.
Research demonstrates that teachers’ use of high-quality language is linked to individual differences in language and literacy skills; this work likewise shows the substantial variation in the quality of teacher talk in early childhood classrooms (e.g., Bowers and Vasilyeva, 2011; Gámez and Levine, 2013; Greenwood et al., 2011; Huttenlocher et al., 2002). For example, Huttenlocher and colleagues (2002) found greater syntactic skills in preschoolers exposed to teachers who used more syntactically complex utterances. Another study found for monolingual English-speaking children that fourth-grade reading comprehension levels were predicted by exposure to sophisticated vocabulary in preschool. These effects were mediated by children’s vocabulary and literacy skills in kindergarten (Dickinson and Porche, 2011).
In classroom studies focused on the linguistic environment, the level of analysis has involved broad measures of language use, such as amount of talk (i.e., teacher–student interactions by minute: Connor et al., 2006), amount of instruction (i.e., in teacher-managed versus child-managed instruction: Connor et al., 2007), type of interaction style (i.e., didactic versus cognitively demanding talk or the amount of extended discourse: Dickinson and Smith, 1991; Jacoby and Lesaux, 2014; Smith and Dickinson, 1994), or instructional moves made by the teacher (e.g., modeling: see review in Lawrence and Snow, 2011). A commonly included measurement that has been linked to children’s literacy development is extended discourse, defined as talk that “requires participants to develop understandings beyond the here and now and that requires the use of several utterances or turns to build a linguistic structure, such as in explanations, narratives, or pretend” (Snow et al., 2001, p. 2). Children are better prepared to comprehend narrative texts they encounter in school if their early language environments provide more exposure to and opportunities to participate in extended discourse. This is because extended discourse and narrative texts share similar patterns for communicating ideas (Uccelli et al., 2006).
Engaging groups of children in effective extended discourse involves asking and discussing open-ended questions and encouraging turn taking, as well as monitoring the group to involve nonparticipating children (Girolametto and Weitzman, 2002). In addition to using interactive storybook and text reading as a platform for back-and-forth conversations (often referred to as interactive or dialogic reading, as described in the preceding section) (Mol et al., 2009; Zucker et al., 2013), engaging children in extended discourse throughout classroom activities (e.g., small-group learning activities, transitions and routines [van Kleek, 2004], dramatic play [Mages, 2008; Morrow and Schickedanz, 2006]) is fundamental to providing a high-quality language-learning environment (Jacoby and Lesaux, 2014).
In an example of the influence of the quantity and quality of teachers’ language input in linguistically diverse classrooms, Bowers and Vasilyeva (2011) found that the total number of words produced by teachers and the diversity of their speech (which was entirely in English) were related to vocabulary gains for children from both English-only households and households in which English was not the primary language, respectively. Thus, they found that preschool dual language learners benefited only from increased quantities of language exposure and showed a negative relationship between vocabulary growth and teachers’ syntactic complexity. By contrast, the English-only children—who presumably had more developed English language proficiency skills—benefited from the diversity of teachers’ vocabulary and syntactic complexity. These findings are consistent with the notion that to promote language learning, different inputs are needed at
different developmental stages (Dickinson and Freiberg, 2009; Gámez and Lesaux, 2012). Children benefit from hearing simplified speech during very early word learning (Furrow et al., 1979). With more exposure to language and more advanced vocabulary development, they benefit from speech input that is more complex (i.e., Hoff and Naigles, 2002). Hoff (2006) suggests that if input is too complex, children filter it out without negative consequences—as long as sufficient beneficial input is available to them. On the other hand, “children have no way to make up for input that is too simple” (Hoff, 2006, p. 75).
An important consideration in light of these findings is that recent research in early childhood classrooms serving children from low-income backgrounds suggests that daily high-quality language-building experiences may be rare for these children. For example, in a Head Start organization serving large numbers of Latino children a recent observational study found a preschool environment lacking in the frequent and high-quality teacher–child language interactions that are needed to support language and literacy development (Jacoby and Lesaux, 2014). Literacy instruction was highly routine based and with low-level language structures. Extended discourse was infrequently used; only 22 percent of observed literacy-based lessons included at least one instance of extended discourse between a teacher and a child or group of children. Instead, teachers asked questions that yielded short answers or linked only to the here and now (e.g., What day is it today? What is the weather today?). These features of infrequent extended discourse and predominantly routine-based literacy instruction were remarkably stable across teachers and classrooms. Other research investigating teacher talk in Head Start preschool classrooms has produced similar findings (e.g., Dickinson et al., 2008).
This is consistent with findings that there are sizable cultural and socioeconomic differences in high-quality language-promoting experiences in the home and in the classroom environment in early childhood (Dickinson, 2003; Dickinson and Porche, 2011; Dickinson and Tabors, 2001; Raikes et al., 2006), just as such differences have been found in the number of words children hear by the time they enter school (Bradley and Corwyn, 2002; Fernald et al., 2013; Hart and Risley, 1995; Schneidman et al., 2013; Weisleder and Fernald, 2013). At the same time, for children from low-resource backgrounds oral language skills show an even stronger connection to later academic outcomes than for children from high-resource backgrounds. Given these findings, rich linguistic experiences at early ages may therefore be especially important for these children. Even small improvements in the literacy environment can have especially strong effects for children who are raised in low-income households (Dearing et al., 2001; Dickinson and Porche, 2011).
In sum, the language environment has important effects on children’s learning, and children benefit from extensive opportunities to listen to and use complex spoken language (National Early Literacy Panel, 2008). Teachers’ use of high-quality language is linked to individual differences in language and literacy skills, and there is considerable variation in the quantity and quality of teachers’ language use across classrooms. The quality of the classroom language environment is a lever for lasting improvements in children’s language and literacy development, and it is important to tailor classroom talk to match the developmental stage of children’s language acquisition.
Creating a Rich Language Environment: Implications for Adults
Improving language environments for young children requires daily learning opportunities that focus on the diversity and complexity of language used with young children. Practically speaking, this can be achieved through extended discourse, with multiple exchanges or turns that go beyond the immediate “here and now” using explanations, narratives, or pretend. Extended discourse can take place throughout all activities and in specific interactions, especially using book reading as a platform for back-and-forth conversations.
Further research is needed to advance understanding of language-based classroom processes and how dynamic and ongoing interactions facilitate or impede children’s literacy. Such studies could advance existing research in at least two ways. In particular, it could further elucidate how language-based social processes in the classroom affect literacy development for the many students who enter schools and other care and education settings with limited proficiency in English. The majority of published studies focused on language-based interactions are focused on English-only learners, despite the fact that social processes can be experienced differently by different groups, even within the same setting (Rogoff and Angelillo, 2002; Tseng and Seidman, 2007). Gámez and Levine (2013) suggest that future research examine the influence of dual-language input on dual language learners’ language development; the nature of teacher talk during different parts of the instructional day, including joint book reading, and how these language experiences predict dual language learners’ language skills; and the impact of classroom talk interventions—those that aim to manipulate the frequency and complexity of teachers’ language—on both the language environment and dual language learners’ language development.
In addition, prior research has measured a two-way process in a largely unidirectional manner—measuring speech only from parent to child or educator to student. It would be more valuable going forward if research were guided by the notion that the language-based interactions between students and educators mediate instruction, and were therefore to explore how communicative feedback loops, both adult–child and child–peer interactions, influence children’s learning and development. Taking into account the student’s contribution to the classroom language environment is particularly important in light of evidence that teachers modify their speech to conform to their students’ limited language proficiency levels, potentially leading to a lower-quality language environment that impedes students’ language growth (Ellis, 2008; see Huttenlocher et al., 2010; Justice et al., 2013). More specifically, Justice and colleagues (2013) suggest that future research examine teacher–child language interactions in a multidimensional way to explore how syntactic complexity, cognitive demand, and even linguistic form (e.g., questions, comments) relate to each other; the links between children’s use of complex syntax in classroom-based interactions and their future general language ability; and interventions designed to enhance classroom language interactions, focusing on both proximal and distal outcomes for children. Finally, greater understanding is needed of the ways in which the classroom language processes described in this section might act as a foundational mediator of the efficacy of interventions focused on learning outcomes in other domains and subject areas.
Alongside student–educator interactions, studies show that peer-to-peer interactions in the classroom may also have positive impacts on children’s vocabulary and expressive language abilities. Children spend a significant amount of time interacting with other children in classroom settings, and a 2009 study examining the language growth and abilities of 4-year-olds in prekindergarten classrooms found that peers who have higher language abilities positively affect other children’s language development. This study also found that children with advanced language skills will receive greater benefits from interacting with peers who also have advanced language skills (Mashburn et al., 2009). These findings are similar to another study showing that peer interactions in the classroom, along with the ability level of the peers, have positive effects on the child’s cognitive, prereading, expressive language skills (Henry and Rickman, 2007). In order to achieve these benefits, however, the preschool classrooms need to be designed so that peers can interact with one another, and include activities such as reading books and engaging in play together. Children with teachers who organize the day with optimal amounts of time for peer-to-peer interactions may achieve greater language growth (Mashburn et al., 2009).
Language and Literacy Development in Dual Language Learners1
For children whose home language is not the predominant language of their school, educators and schools need to ensure the development of English proficiency. Both parents and preschool teachers can be particularly useful in improving these children’s depth of vocabulary (Aukrust, 2007; Roberts, 2008). At the same time, children can be helped to both build and maintain their first language while adding language and literacy skills in English (Espinosa, 2005). In support of this as a long-term goal are the potential advantages of being bilingual, including maintaining a cultural and linguistic heritage and conferring an advantage in the ability to communicate with a broader population in future social, educational, and work environments. Additionally, an emerging field of research, albeit with mixed results to date, explores potential advantages of being bilingual that are linked more directly to cognitive development, starting in early childhood and extending to preserving cognitive function and delaying the symptoms of dementia in the elderly (Bialystok, 2011; de Bruin et al., 2015).
Bilingual or multilingual children are faced with more communicative challenges than their monolingual peers. A child who frequently experiences failure to be understood or to understand may be driven to pay more attention to context, paralinguistic cues, and gestures in order to interpret an utterance, and thus become better at reading such cues. The result may be improved development of theory of mind and understanding of pragmatics (Yow and Markman, 2011a,b). In addition, the need to continually suppress one language for another affords ongoing practice in inhibitory or executive control, which could confer advantages on a range of inhibitory control tasks in children and helps preserve this fundamental ability in aging adults (Bialystok, 2011; Bialystok and Craik, 2010; Bialystok et al., 2009).
One challenge in the education of dual language learners is that they sometimes are classified along with children with special needs. One reason for this is the lack of good assessment tools to help distinguish the nature of the difficulties experienced by dual language learners—whether due to a learning disability or to the fact that learning a second language is difficult, takes time, and develops differently in different children (Hamayan et al., 2013).
Children’s early knowledge of mathematics is surprisingly important, and it strongly predicts later success in mathematics (Denton and West,
1 An ongoing study and forthcoming report of the Institute of Medicine and the National Research Council focuses on research, practice, and policy for young dual language learners. More information about this study can be found at www.iom.edu/English-DualLanguageLearners.
2002; Koponen et al., 2013; Passolunghi et al., 2007). Mathematics knowledge in preschool predicts mathematics achievement even into high school (National Mathematics Advisory Panel, 2008; NRC, 2009; Stevenson and Newman, 1986). Mathematics ability and language ability also are interrelated as mutually reinforcing skills (Duncan et al., 2007; Farran et al., 2005; Lerkkanen et al., 2005; O’Neill et al., 2004; Praet et al., 2013; Purpura et al., 2011). Indeed, mathematical thinking reaches beyond competence with numbers and shapes to form a foundation for general cognition and learning (Clements and Sarama, 2009; Sarama et al., 2012), and problems with mathematics are the best predictor of failure to graduate high school. Mathematics therefore appears to be a core subject and a core component of thinking and learning (Duncan and Magnuson, 2011; Duncan et al., 2007).
Given its general importance to academic success (Sadler and Tai, 2007), children need a robust foundation in mathematics knowledge in their earliest years. Multiple analyses suggest that mathematics learning should begin early, especially for children at risk for later difficulties in school (Byrnes and Wasik, 2009; Clements and Sarama, 2014). Well before first grade, children can learn the skills and concepts that support more complex mathematics understanding later. Particularly important areas of mathematics for young children to learn include number, which includes whole number, operations, and relations; geometry; spatial thinking; and measurement. Children also need to develop proficiency in processes for both general and specific mathematical reasoning (NRC, 2009).
If given opportunities to learn, young children possess a remarkably broad, complex, and sophisticated—albeit informal—knowledge of mathematics (Baroody, 2004; Clarke et al., 2006; Clements et al., 1999; Fuson, 2004; Geary, 1994; Thomson et al., 2005). In their free play, almost all preschoolers engage in substantial amounts of premathematical activity. They count objects; compare magnitudes; and explore patterns, shapes, and spatial relations. Importantly, this is true regardless of a child’s income level or gender (Seo and Ginsburg, 2004). Preschoolers can also, for example, learn to invent solutions to simple arithmetic problems (Sarama and Clements, 2009).
High-quality mathematics education can help children realize their potential in mathematics achievement (Doig et al., 2003; Thomson et al., 2005). However, without such education starting, and continuing throughout, the early years, many children will be on a trajectory in which they will have great difficulty catching up to their peers (Rouse et al., 2005). As discussed further in Chapter 6, early childhood classrooms typically are ill suited to helping children learn mathematics and underestimate their ability to do so. In some cases, children can even experience a regression on some mathematics skills during prekindergarten and kindergarten (Farran et al., 2007; Wright, 1994). Mathematics needs to be conceptualized as more than
skills, and its content as more than counting and simple shapes. Without building a robust understanding of mathematics in the early years, children too often come to believe that math is a guessing game and a system of rules without reason (Munn, 2006).
Both education and experience can make a difference, as evidenced by data from the latest international Trends in International Mathematics and Science Study, which added data collection on early mathematics education (Mullis et al., 2012). Students with higher mathematics achievement at fourth and sixth grades had parents who reported that they often engaged their children in early numeracy activities and that their children had attended preprimary education and started school able to do early numeracy tasks (e.g., simple addition and subtraction). Those children who had attended preschool or kindergarten had higher achievement, while the 13 percent who had attended no preprimary school had much lower average mathematics achievement (Mullis et al., 2012).
Developmental Progression of Learning Mathematics
Children move through a developmental progression in specific mathematical domains, which informs learning trajectories as important tools for supporting learning and teaching. Recent work based on empirical research and emphasizing a cognitive science perspective conceptualizes learning trajectories for mathematics as “descriptions of children’s thinking and learning in a specific mathematical domain, and a related, conjectured route through a set of instructional tasks designed to engender those mental processes or actions hypothesized to move children through a developmental progression of levels of thinking, created with the intent of supporting children’s achievement of specific goals in that mathematical domain” (Clements and Sarama, 2004, p. 83).
Box 4-5 illustrates the concept of a developmental progression through the example of subitizing, an oft-neglected mathematical goal for young children. Research shows that subitizing, the rapid and accurate recognition of the number in a small group, is one of the main abilities very young children should develop (Palmer and Baroody, 2011; Reigosa-Crespo et al., 2013). Through subitizing, children can discover critical properties of number, such as conservation and compensation (Clements and Sarama, 2014; Maclellan, 2012) and develop such capabilities as unitizing and arithmetic. Subitizing is not the only way children think and learn about number. Counting is the other method of quantification. It is the first and most basic mathematical algorithm and one of the more critical early mathematics competencies (Aunola et al., 2004; National Mathematics Advisory Panel, 2008). Chapter 6 includes examples from a complete learning trajectory—goal, developmental progression, and instructional activities—for counting (Clements and Sarama, 2014).
Subitizing: A Developmental Progression
A quantitative, or numerical, “sense” is innate or develops early. For example, very young children possess approximate number systems (ANSs) that allow them to discriminate large and small sets, determining, for example, whether there are more white or gray dots in the figure below. Six-month-olds can discriminate a 1:2 ratio, and by 9 months of age, they can also distinguish sets in a 2:3 ratio (e.g., 12 compared with 18).
Subitizing involves determining and explicitly identifying the exact number of items in a small set. Subitizing ability develops in a stepwise fashion. In laboratory settings, children can initially differentiate 1 from “more than 1” at about 33 months of age (Wynn, 1992b). Between 35 and 37 months, they differentiate between 1 and 2, but not larger numbers. A few months later, at 38 to 40 months, they can identify 3 as well. After about 42 months, they can identify all numbers that they can count, 4 and higher, at about the same time. However, research in natural, child-initiated settings shows that the development of these abilities can occur much earlier, with children working on 1 and 2 around their second birthday or earlier (Mix et al., 2005). Babies in the first 6 months of life, and even earlier, can discriminate 1 object from 2, and 2 objects from 3 (Antell and Keating, 1983; Wynn et al., 2002). Thus, even infants can discriminate among and match small configurations (1-3) of objects, only for these small numbers. Because children cannot discriminate 4 objects from 5 or 6 until the age of about 3 years, some researchers have suggested that infants use an automatic perceptual process that people, including adults, can apply only to small collections up to around 4 objects (Chi and Klahr, 1975).
A developmental progression moves from foundational but pre-explicit quantification to explicit naming of small quantities. This initially involves only perceptual subitizing (Clements, 1999; Kaufman et al., 1949). From their second to third birthdays, most children can name sets of 1 and 2, and then 3 soon thereafter (Mix et al., 2005; Wynn, 1992b). Larger sets are perceived, quantified, and quickly named as the child gains experience. Perceptual subitizing also plays the role of unit-
izing, or making single things to count out of the stream of perceptual sensations (Glasersfeld, 1995). Then a qualitative advance is made as conceptual subitizing develops. This involves similarly quantifying 2 parts (separately) and then combining them, again, quickly, accurately, and without being explicitly aware of the cognitive processing (Clements, 1999; see empirical evidence for such processes in Trick and Pylyshyn, 1994). That is, one might perceive each side of a domino as composed of 4 individual dots and as “one 4” and phenomenologically experience the domino as “an 8.” With appropriate experiences, children can become competent in this type of subitizing with totals from 5 to 10 at ages 4 and 5.
Many theories have been advanced to explain the subitizing process (Baroody et al., 2006; Huttenlocher et al., 1994; Jordan et al., 2003; Mix et al., 2002). A synthesis suggests the following model. The ANS serves as a transition between general, approximate notions of number and one based on an exact, abstract, mental model. Infants quantify collections of rigid objects (not sequences of sounds or materials that are nonrigid and noncohesive such as water) (Huntley-Fenner et al., 2002). These quantifications begin as an undifferentiated, innate notion of amount of objects. Object individuation, which occurs early in preattentive processing (and is a general, not numerical-only, process), helps lay the groundwork for differentiating discrete from continuous quantity. For example, by about 6 months of age, infants may represent very small numbers (1 or 2) as individuated objects. To compare quantities, they process correspondences. Initially, these are inexact estimates, depending on the ratio between the sets (Johnson-Pynn et al., 2005). Once children can represent objects mentally, they also can make exact correspondences between these nonverbal representations and eventually develop a quantitative notion of that comparison (e.g., not just that ••• is more than ••, but also that it contains one more •) (Baroody et al., 2005). Even these correspondences, however, do not imply a cardinal representation of the collection.
To complete the subitizing process, children must make word–word mappings between numbers (e.g., “How many?”) and number words, which they do only after they have learned several number words (Sandhofer and Smith, 1999). They then label small number situations with the corresponding number word, mapping the number word to the numerosity property of the collection. They begin this phase even before the age of 2 years, but for some time, this mapping applies mainly to the word “two,” a bit less to “one,” and with considerable less frequency, “three” and “four” (Fuson, 1992; Wagner and Walters, 1982). Only after many such experiences do children abstract the numerosities from the specific situations and begin to understand that the situations named by “three” correspond. That is, they begin to establish what mathematicians call a numerical equivalence class. Both
subitizing- and counting-based verbal systems are then more frequently used and integrated, eventually leading to explicit, verbal mathematical abstractions.
The construction of such schemes probably depends on guiding frameworks and principles developed through interactions with others, such as parents and educators. Part of a learning trajectory is instructional tasks and strategies that promote children’s developmental progression. A quasi-experimental study (Hannula, 2005) showed that it is possible to enhance 3-year-old children’s spontaneous focusing on numerosity, and thus catalyze their deliberate practice in numerical skills (cf. Ericsson et al., 1993). Research indicates that teachers often do not do sufficient subitizing work, which results in their students’ regression in subitizing from the beginning to the end of kindergarten (Wright, 1994). Instead, directing children’s attention to patterns through perceptual and especially conceptual subitizing helps children develop abstract number and arithmetic strategies. Activities such as teachers challenging students to name the number of dots in a display shown only for 1-2 seconds have resulted in substantial growth in this ability (Baroody et al., 2008; Clements and Sarama, 2008; Clements et al., 2011, 2013b; Hannula, 2005; Nes, 2009; Van Luit and Van de Rijt, 1998).
Subitizing ability is not merely a low-level, innate process, but develops considerably and combines with other mental processes. Even though they are limited, subitizing capabilities appear to form a foundation for later connection to culturally based cognitive tools such as number words and the number word sequence and the development of exact and extended number concepts and skills. Functional magnetic resonance imaging and other studies have shown that a neural component of numerical cognition present in the early years may be the foundation for later symbolic numerical development (Cantlon et al., 2006; Eimeren et al., 2007; Masataka et al., 2006; Piazza et al., 2004). Subitizing appears to precede and support the development of counting ability and arithmetic skills (Eimeren et al., 2007; Hannula, 2005; Hannula et al., 2007; Le Corre et al., 2006). Children who cannot subitize conceptually are handicapped in learning such arithmetic processes. Those who can subitize may be limited to doing so with small numbers at first, but such actions are useful stepping stones to the construction of more sophisticated procedures with larger numbers. Indeed, lack of this competence may underlie mathematics learning disabilities and difficulties (Ashkenazi et al., 2013; Berch and Mazzocco, 2007; Butterworth, 2010; Chu et al., 2013). Children from low-resource communities and those with special needs often lag in subitizing ability, hindering their mathematical development (Butterworth, 2010; Chu et al., 2013; Clements and Sarama, 2014; Le Corre et al., 2006).
SOURCE: Adapted with permission from Clements and Sarama, 2014, and Sarama and Clements, 2009.
Children with Special Needs
Children with special needs in learning mathematics fall into two categories. Those with mathematical difficulties struggle to learn mathematics for any reason; this category may apply to as many as 35-40 percent of students (Berch and Mazzocco, 2007). Those with specific mathematics learning disabilities are more severe cases; these students have a memory or cognitive deficit that interferes with their ability to learn math (Geary, 2004). This category may apply to about 6-7 percent (Berch and Mazzocco, 2007; Mazzocco and Myers, 2003). In one study, this classification persisted in third grade for 63 percent of those classified as having mathematics learning disabilities in kindergarten (Mazzocco and Myers, 2003).
Mathematics learning disabilities, while assumed to have a genetic basis, currently are defined by students’ behaviors—yet with ongoing debate among experts about what those behaviors are. One consistent finding is that students with mathematics learning disabilities have difficulty retrieving basic arithmetic facts quickly. This has been hypothesized to be the result of an inability to store or retrieve facts and impairments in visual-spatial representation. As early as kindergarten, limited working memory and speed of cognitive processing may be problems for these children (Geary et al., 2007). Many young children with learning disabilities in reading show a similar rapid-naming deficit for letters and words (Siegel and Mazabel, 2013; Steacy et al., 2014). Another possibility is that a lack of higher-order, or executive, control of verbal material causes difficulty learning basic arithmetic facts or combinations. For example, students with mathematics learning disabilities may have difficulty inhibiting irrelevant associations. An illustration of this would be hearing “5 + 4” and saying “6” because it follows 5.
One explanation for the difficulty students with mathematics learning disabilities have learning basic arithmetic combinations might be delays in understanding counting. These students may not fully understand counting nor recognize errors in counting as late as second grade. They persist in using immature counting strategies, such as counting “one-by-one” on their fingers, throughout elementary school (Geary et al., 1992; Ostad, 1998). Other experts, however, claim that a lack of specific competencies, such as subitizing, is more important (Berch and Mazzocco, 2007).
Some evidence suggests that it is possible to predict which kindergartners are at risk for mathematics learning disabilities based on skill including reading numerals, number constancy, magnitude judgments of one-digit numbers, or mental addition of one-digit numbers (Mazzocco and Thompson, 2005). However, until more is known, students should be classified as having mathematics learning disabilities only with great caution and
after good mathematics instruction has been provided. Such labeling in the earliest years could do more harm than good (Clements and Sarama, 2012).
Interrelationships Between Mathematics and Language
It can appear that language is less of a concern in mathematics compared to other subjects because it is assumed to be based on numbers or symbols, but this is not the case (Clements et al., 2013a). In fact, children learn math mainly from oral language, rather than from mathematical symbolism or textbooks (Janzen, 2008). In addition, “talking math” is more than just using mathematics terms (Clements and Sarama, 2014). Therefore, both oral language and literacy in general, as well as the “language of mathematics,” are important for learning (Vukovic and Lesaux, 2013). Vocabulary and knowledge of print are both predictors of later numeracy (Purpura et al., 2011). Similarly, growth in mathematics from kindergarten to third grade is related to both early numerical skills and phonological processing (Vukovic, 2012). In one study of linguistically and ethnically diverse children aged 6-9 years, language ability predicted gains in geometry, probability, and data analysis but not in arithmetic or algebra (controlling for reading ability, visual–spatial working memory, and gender) (Vukovic and Lesaux, 2013). Thus, language may affect how children make meaning of mathematics but not its complex arithmetic procedures.
Moreover, there is an important bidirectional relationship between learning in mathematics and language (Sarama et al., 2012). Each has related developmental milestones. Children learn number words at the same time as other linguistic labels. Most children recognize by the age of 2 which words are for numbers and use them only in appropriate contexts (Fuson, 1988). Each also has related developmental patterns, with learning progressing along similar paths. In both, children recognize the whole before its parts. In learning language, this is word before syllable, syllable before rime-onset, and rime-onset before phoneme (see also Anthony et al., 2003; Ziegler and Goswami, 2005). Similarly in mathematics, numbers are first conceptualized as unbreakable categories and then later as composites (e.g., 5 is composed of 3 and 2) (Butterworth, 2005; Sarama and Clements, 2009). By 6 years old in most cultures, children have been exposed to symbol representations that are both alphabetic and numerical, and they begin to be able to segment words into phonemes and numbers into singletons (e.g., understanding that 3 is 1 and 1 and 1) (Butterworth, 2005; Sarama and Clements, 2009; Wagner et al., 1993). The ability to identify the component nature of words and numbers predicts the ability to read (Adams, 1990; Stanovich and Siegel, 1994) and to compute (Geary, 1990, 1993). In addition to these similarities in typical developmental pathways, many children with learn-
ing disabilities experience deficits in competencies related to both language/literacy and numeracy (Geary, 1993; Hecht et al., 2001; NRC, 1998).
Furthermore, there appear to be shared competencies between the two subject areas. For example, preschoolers’ narrative abilities (i.e., their abilities to convey all the main events of a story and offer a perspective on its events) have been shown to predict mathematics achievement 2 years later (O’Neill et al., 2004). Beginning mathematics scores have been shown to be highly predictive of subsequent achievement in both reading and mathematics although beginning reading skills (such as letter recognition, word identification, and word sounds) were shown to be highly predictive of later reading (advanced competencies such as evaluation) but not mathematics learning (Duncan et al., 2007).
A causal relationship between rich mathematics learning and developing language and literacy skills is supported by a randomized study of the effects of a math curriculum called Building Blocks on prekindergarten children’s letter recognition and oral language skills. Building Blocks children performed the same as the children in the control group on letter recognition and on three oral language subscales but outperformed them on four subscales: ability to recall key words, use of complex utterances, willingness to reproduce narratives independently, and inference (Sarama et al., 2012). These skills had no explicit relation to the math curriculum. Similarly, a study of 5- to 7-year-olds showed that an early mathematics and logical-mathematical intervention increased later scores in English by 14 percentile points (Shayer and Adhami, 2010).
Time on task (or time on instruction) does affect learning, which naturally leads to consideration of potential conflicts or tradeoffs between time spent on different subjects (e.g., Bodovski and Farkas, 2007). Indeed, a frequent concern is that introducing a mathematics curriculum may decrease the time devoted to language and literacy, impeding children’s development in those areas, which are heavily emphasized in early learning goals (see Clements and Sarama, 2009; Farran et al., 2007; Lee and Ginsburg, 2007; Sarama and Clements, 2009). However, this assumes that mathematics activities will not have a positive effect on language and literacy. Yet as described here, evidence from both educational and psychological research suggests the potential for high-quality instruction in each to have mutual benefits for learning in both subjects. Rich mathematical activities, such as discussing multiple solutions and solving narrative story problems, can help lay the groundwork for literacy through language development, while rich literacy activities can help lay the groundwork for mathematics development (Sarama et al., 2012).
Children Who Are Dual Language Learners
For mathematics learning in children who are dual language learners, the language, not just the vocabulary, of mathematics need to be addressed (Clements and Sarama, 2014). Challenges for dual language learners include both technical vocabulary, which can range in how similar or distinct terms are from everyday language, and the use of complex noun phrases. On the other hand, bilingual children often can understand a mathematical idea more readily because, after using different terms for it in different languages, they comprehend that the mathematical idea is abstract, and not tied to a specific term (see Secada, 1992).
There is evidence that the best approach is to teach these young children in their first language (Celedón-Pattichis et al., 2010; Espada, 2012). At a minimum, their teachers need to connect everyday language with the language of math (Janzen, 2008). It is also essential to build on the resources that bilingual children bring to learning mathematics—all cultures have “funds of knowledge” (culturally developed and historically accumulated bodies of knowledge and skills) that can be used to develop mathematical contexts and understandings (Moll et al., 1992). Instructional practices for teaching mathematics with dual language learners are discussed further in Chapter 6.
Conclusions About Learning Specific Subjects
For subject-matter content knowledge and proficiency, children learn best when supported along a trajectory with three components: (1) their understanding of the subject-matter content itself, (2) their progress through predictable developmental levels and patterns of thinking related to their understanding of the content, and (3) instructional tasks and strategies that adults who work with children can employ to promote that learning at each level. For example:
- Almost all topics in mathematics follow predictable learning trajectories that include number counting and subitizing, number relationships and magnitude comparison, arithmetic operations, geometry and spatial sense, and measurement.
- Learning trajectories in literacy include specific developmental sequences in children’s learning of phonological awareness and phonics (letter-sound correspondences), which together contribute to children’s understanding of how spoken words are captured in reading and writing and thus to their advancement through broader levels of early literacy.
Some principles of how children learn along a trajectory hold across subject-matter domains, but there are also substantive differences among subjects in the specific skills children need and in the learning trajectories. Both generalizable principles and subject-specific distinctions have implications for the knowledge and competencies needed to work with children.
An important factor in children’s learning of subject-matter content is how each of the components of learning trajectories both requires and develops aspects of learning that are not content specific, such as critical reasoning, executive function, self-regulation, learning skills, positive dispositions toward learning, and relationships.
Educators, developmental scientists, and economists have long known that academic achievement is a result of both the growth of specific knowledge and the development of general learning competencies that regulate how children enlist cognitive resources when they encounter learning challenges, motivate advances in learning, and strengthen children’s self-confidence as learners.
These general learning competencies have been labeled and categorized in various ways. Considerable recent research on some of these learning competencies has been conducted using the concept of “executive function,” which generally refers to a set of supervisory functions that regulate and control cognitive activity that affects learning (Vitiello et al., 2011) and allow children to persevere with tasks, including learning tasks, even when facing fatigue, distraction, or decreased motivation. In the field of human development “mastery motivation” in infancy typically is indexed by the baby’s persistence, focus, and curiosity in exploration and problem solving (Morgan et al., 1990; Wang and Barrett, 2013). In preschool-age children, these skills often are conceptualized as the quality of the child’s “approaches to learning,” which include motivation, engagement, and interest in learning activities. Heckman (2007) has used the term “noncognitive skills” to refer to many of these learning competencies, including self-control, persistence, self-discipline, motivation, and self-esteem, as well as future orientedness (i.e., the capacity to substitute long-term goals for immediate satisfactions). This label is used in contrast to the “cognitive skills” that are more often measured to predict children’s later success, although there is considerable research that the “noncognitive skills” also support learning and achievement (see, e.g., Cunha and Heckman, 2010; Heckman, 2007), and they are highly relevant to cognitive skills in such areas as language, mathematics, science, and other traditional academic fields.
Here the alternative conceptualizations for these important aspects of child development and early learning are grouped as “learning competencies” to reflect their importance for early learning. Individual differences in these competencies are important determinants of learning and academic motivation, and children’s experiences at home and in the classroom contribute to some of these differences. This section examines these competencies as well as their interrelationships with the previously discussed subject-matter domains of language and literacy and mathematics.
General Cognitive Skills
Several cognitive control processes are important for planning and executing goal-directed activity, which is needed for successful learning (e.g., Blair, 2002; Lyon and Krasnegor, 1996). These processes include, for example, short-term and working memory, attention control and shifting, cognitive flexibility (changing thinking between different concepts and thinking about multiple concepts simultaneously), inhibitory control (suppressing unproductive responses or strategies), and cognitive self-regulation. These processes also are closely related to emotion regulation, which is discussed later in the section on socioemotional development, and which also contributes to children’s classroom success.
As noted previously, many general cognitive processes often are referred to collectively as “executive function,” although not everyone defines this construct in the same way (e.g., Miyake et al., 2000; Raver, 2013), and different disciplines and researchers differ as to which cognitive skills it includes. Other theoretical frameworks exist as well. For example, cognitive control and complexity theory postulates that executive function is an outcome, not an explanatory construct, and is the result of children’s creation and application of rules (driven perhaps by an increase in reflection afforded by experience-dependent maturation of the prefrontal cortex) (Müller et al., 2008; Zelazo and Carlson, 2012; Zelazo and Lyons, 2012). As with the overall domains of development displayed earlier in Figure 4-1, the committee did not attempt to reconcile those different perspectives.
This variation in perspectives makes it difficult to parse the literature produced by different fields of research and practice. In general, however, executive function appears to improve most rapidly in young children (Best et al., 2011; Blair, 2002; Hughes and Ensor, 2011; Romine and Reynolds, 2005; Schoemaker et al., 2014; Zelazo and Carlson, 2012). Executive function processes appear to be partially dependent on the development of the prefrontal cortex (the site of higher-order cognitive processes), notably through the preschool and kindergarten age range (Bassett et al., 2012; Blair, 2002).
Short-Term and Working Memory
Short-term memory is the ability for short-term recall, such as of a sentence or important details from conversation and reading. Working memory allows children to hold in their memory information from multiple sources, whether heard or read, so they can use and link that information. Updating working memory is the ability to keep and use relevant information while engaging in another cognitively demanding task (Conway et al., 2003; DeYoung, 2011).
Attention Control and Shifting
Attention control is the ability to focus attention and disregard distracting stimuli (e.g., a continuous performance task that requires a child to identify when some familiar object appears onscreen and ignore other objects that appear, or a task that requires ignoring extraneous information in a mathematics word problem). Attention shifting is a related process of switching a “mental set” while simultaneously ignoring distractions (e.g., counting by different units—tens and ones). Attention shifting and cognitive flexibility are often grouped.
Cognitive flexibility capacities develop gradually throughout early childhood and have significant influences on children’s social and academic competence. Cognitive flexibility is important, for example, for reading (Duke and Block, 2012). Children who are better able to consider, at the same time, both letter-sound and semantic (meaning) information about words have better reading comprehension (Cartwright, 2002; Cartwright et al., 2010). Reading comprehension also appears to improve when children are taught about words with multiple meanings (e.g., spell or plane), and sentences with multiple meanings (e.g., “The woman chased the man on a motorcycle.”) (Yuill, 1996; Zipke et al., 2009). In addition, interventions in young children that focus on cognitive flexibility have shown significant benefits for reading comprehension (Cartwright, 2008).
Inhibitory control involves controlling a dominant response (e.g., the first answer that comes to mind) so as to think about better strategies or ideas. The skill of simple response inhibition (withholding an initial, sometimes impulsive, response) develops during infancy through toddlerhood. Infants also develop some control of cognitive conflict in tasks in which an
item of interest to them is first hidden in one location and then another, and the child must resist the response of searching in the first location (Diamond, 1991; Müller et al., 2008; Rothbart and Rueda, 2005) (see Marcovitch and Zelazo, 2009, for a model of possible mechanisms). Later in their first year, children can resolve conflict between their line of sight and their line of reaching (Diamond, 1991). By about 30 months, they can successfully complete a spatial conflict task (Rothbart and Rueda, 2005). From 3 to 5 years of age, complex response inhibition and response shifting develop, with attention shifting developing at about age 4 (Bassett et al., 2012). The most rapid increase in inhibitory control is between 5 and 8 years of age, although moderate improvements are seen up to young adulthood (Best et al., 2011).
Inhibitory control supports children’s learning across subject-matter areas. As one example of its importance for mathematics, when the initial reading of a problem is not the correct one, children need to inhibit their impulse to answer (incorrectly) and carefully examine the problem. Consider the following problem: “There were six birds in a tree. Three birds already flew away. How many birds were there from the start?” Children have to inhibit the immediate desire to subtract prompted by the words “flew away” and perform addition instead.
Cognitive self-regulation is what helps children plan ahead, focus attention, and remember past experiences. The construct of self-regulation and related concepts have a long history in psychology (e.g., Glaser, 1991; Markman, 1977, 1981; Piaget and Szeminska, 1952; Sternberg, 1985; Vygotsky, 1978; Zelazo et al., 2003) and education (e.g., McGillicuddy-De Lisi, 1982; Steffe and Tzur, 1994). Most recently, researchers and educators have used the broad term self-regulation to refer to the processes involved in intentionally controlling attention, thinking, impulses, emotions, and behavior. In this way, self-regulation can be thought of in relation to several aspects of development, including the cognitive processes discussed here and the social and emotional processes discussed later in this chapter. Developmental psychobiological research and neuroimaging indicate that these subclasses are both neurally and behaviorally distinct while also being related and correlated (Bassett et al., 2012; Hofmann et al., 2012; Hongwanishkul et al., 2005; Neuenschwander et al., 2012; Willoughby et al., 2011). Together, these types of self-regulation allow children to persevere with tasks even when facing difficulties in problem solving or learning, fatigue, distraction, or decreased motivation (Blair and Razza, 2007; Neuenschwander et al., 2012). It is thus unsurprising that
kindergarten teachers believe self-regulation is as important as academics (Bassok and Rorem, 2014).
Both cognitive self-regulation and emotional self-regulation (discussed later in this chapter) contribute to socioemotional development and also play a role in learning. Although the relationship between various features of cognitive self-regulation and academic achievement has been well documented for older students (e.g., Bielaczyc et al., 1995; Zimmerman, 2002), less was known until recently about how self-regulation developed in the early years contributes to the later development of cognitive and emotional self-regulation and academic achievement (NRC and IOM, 2000).
Children’s self-regulation and their ability to successfully function in school settings are related in two ways. First, emotional self-regulation enables children to benefit from learning in various social contexts, including their capacities to manage emotions in interactions with educators as well as peers (e.g., one-on-one, in cooperative pairs, in large and small groups). It also assists them in conforming to classroom rules and routines. Second, cognitive self-regulation enables children to develop and make use of cognitive processes that are necessary for academic learning (Anghel, 2010).
Although most studies have focused on specific effects of either cognitive or emotional self-regulation, evidence suggests that the two are interconnected. This link is probably due to the commonality of the neurological mechanisms governing both emotional and cognitive self-regulation. For example, children lacking emotion regulation are likely also to have problems with regulating cognitive processes, such as attention (Derryberry and Reed, 1996; LeDoux, 1996). Moreover, earlier patterns in the development of emotion control have been shown to be predictive of children’s later ability to exercise control over their cognitive functioning (Blair, 2002).
Several studies have shown positive correlations between self-regulation and achievement in young children (e.g., Bierman et al., 2008b; Blair and Razza, 2007; Blair et al., 2010; Bull et al., 1999; Cameron et al., 2012; Neuenschwander et al., 2012; Roebers et al., 2012; Welsh et al., 2010), although there are exceptions (Edens and Potter, 2013). Preschoolers’ cognitive self-regulation, including inhibitory control and attention shifting, were found to be related to measures of literacy and mathematics ability in kindergarten (Blair and Razza, 2007). In another study, children with higher self-regulation, including attention, working memory, and inhibitory control, achieved at higher levels in literacy, language, and mathematics (McClelland et al., 2007). Interventions in the area of self-regulation have shown positive effects for reading achievement (Best et al., 2011; Bierman et al., 2008a; Blair and Diamond, 2008; Blair and Razza, 2007; Diamond and Lee, 2011). Among struggling first graders in an effective reading intervention, those who were retained in grade showed significantly weaker self-regulation skills (Dombek and Connor, 2012). Cognitive self-regulation
appears to be strongly associated with academic learning (Willoughby et al., 2011), but emotional self-regulation also contributes through children’s adjustment to school and attitudes toward learning. In addition, both cognitive and emotional self-regulation contribute to variance in attention, competence motivation, and persistence (Bassett et al., 2012; Willoughby et al., 2011).
In addition, differences in self-regulation competencies raise important issues related to disparities in educational achievement. Children in poverty can have lower self-regulation competencies (e.g., Blair and Razza, 2007; Blair et al., 2010; Bull and Scerif, 2001; Hackman and Farah, 2009; Jenks et al., 2012; Kishiyama et al., 2009; Masten et al., 2012; Mazzocco and Hanich, 2010; McLean and Hitch, 1999; Raver, 2013). One reason is the effect of chronic stress on behavioral and biological capacities for self-control (see discussion of chronic stress and adversity later in this chapter). This risk is exacerbated for children who are also dual language learners (Wanless et al., 2011). Students with special needs are another population who may require focused interventions to develop self-regulation competencies (Harris et al., 2005; Jenks et al., 2012; Lyon and Krasnegor, 1996; Mazzocco and Hanich, 2010; McLean and Hitch, 1999; Raches and Mazzocco, 2012; Toll et al., 2010; Zelazo et al., 2002). Students who are gifted and talented may also have exceptional needs in this domain (e.g., Mooji, 2010).
Adults who work with children have the opportunity to provide environments, experiences, and curricula that can help develop the competencies needed, including for children whose skills were not optimally developed in the earliest years. Importantly, the goal of such interventions is not to “train” children to suppress behaviors and follow rules. Rather, effective educators and programs provide learning activities and environments that increase children’s capacity and disposition to set a goal (e.g., join a pretend play activity, complete a puzzle); develop a plan or strategy; and muster their social, emotional, and cognitive faculties to execute that plan. The science of how children develop and learn indicates that integrating academic learning and self-regulation is a sound approach.
Executive Functions and Learning in Specific Subjects
As already noted and shown in several examples, executive function processes are closely related to achievement in both language and literacy and mathematics (Best et al., 2011; Blair and Razza, 2007; Blair et al., 2010; Neuenschwander et al., 2012), and this has also been shown in science (Nayfeld et al., 2013). In some research, executive function has been correlated similarly with both reading and mathematics achievement across a wide age span (5 to 17 years), suggesting its significant role in academic
learning (Best et al., 2011; Blair and Razza, 2007; Neuenschwander et al., 2012). In contrast, some studies have found that executive function is more strongly associated with mathematics than with literacy or language (Barata, 2010; Blair et al., 2010; Ponitz et al., 2009; von Suchodoletz and Gunzenhauser, 2013). A strong relationship between executive function and mathematics may reflect that mathematics relies heavily on working memory and attention control, requiring the ability to inhibit an automatic response to a single aspect of a problem, to hold relevant information in mind, and to operate on it while shifting attention appropriately among different elements of a problem (Welsh et al., 2010). This relationship is especially important given that mathematics curricula increasingly require higher-order skills, which executive function competencies provides (Baker et al., 2010).
Some research indicates that most executive function competencies correlate significantly with mathematics achievement (Bull and Scerif, 2001), while other studies suggest a greater role for particular executive function competencies in the learning of mathematics for young children—especially inhibitory control (Blair and Razza, 2007) or working memory (Bull et al., 2008; Geary, 2011; see also, Geary et al., 2012; cf. Neuenschwander et al., 2012; Szűcs et al., 2014; Van der Ven et al., 2012). These latter two competencies have been shown to predict success in mathematics in primary school students (Toll et al., 2010). Working memory tasks have also been shown to predict mathematics learning disabilities, even more so than early mathematical abilities (Toll et al., 2010). Several studies have identified lack of inhibition and working memory as specific deficits for children of lower mathematical ability, resulting in difficulty with switching to and evaluating new strategies for dealing with a particular task (Bull and Scerif  and Lan and colleagues  found similar results). Persistence, another learning skill that is interrelated with cognitive processes, also has been linked to mathematics achievement for both 3- and 4-year-olds (Maier and Greenfield, 2008).
Executive function competencies may be differentially associated with distinct areas of mathematics. For example, executive function was found to be correlated more with solving word problems than with calculation (Best et al., 2011), and appears to play a role in acquiring new mathematics procedures and developing automatic access to arithmetic facts (LeFevre et al., 2013). Different aspects of working memory also may be related to different mathematical areas (Simmons et al., 2012). Parallel observations have been made for executive function and reading, with executive function playing a larger role in reading comprehension than in decoding.
In addition to the role of executive function in learning mathematics, mathematics activities also contribute to developing executive function. Some mathematics activities may require children to suppress prepotent
responses, manipulate abstract information, and remain cognitively flexible. Importantly, neuroimaging studies suggest that executive function may be developed through learning mathematics in challenging activities but not in exercising mathematics once learned (Ansari et al., 2005; Butterworth et al., 2011).
Cognitive Skills and Executive Function in Children with Special Needs
Some students with special needs may have a specific lack of certain executive function competencies (Harris et al., 2005; Jenks et al., 2012; Lyon and Krasnegor, 1996; McLean and Hitch, 1999; Raches and Mazzocco, 2012; Schoemaker et al., 2014; Toll et al., 2010; Zelazo et al., 2002). Most of the research on executive function deficits in relation to disabilities that affect young children has focused on specific disorders, particularly attention deficit hyperactivity disorder (ADHD). An early theory posited that ADHD is a lack of the behavioral inhibition required for proficiency with executive functions such as self-regulation of affect, motivation, and arousal; working memory; and synthesis analysis of internally represented information (Barkley, 1997). Research has found that children diagnosed with ADHD are more likely than children without ADHD to have two or more deficits in executive function (Biederman et al., 2004; cf. Shuai et al., 2011). A meta-analysis of studies of one measure of executive function, the Wisconsin Card Sorting Test, suggests that the performance of individuals with ADHD is fairly consistently poorer than that of individuals without clinical diagnoses (Romine et al., 2004). In another study, children with ADHD were found not to have learning problems but rather problems in a measure of inhibitory control, which affected arithmetic calculation (as well as written language) (Semrud-Clikeman, 2012). Other evidence suggests that children diagnosed with ADHD may have deficits not in executive processes themselves but in motivation or response to contingencies, that is, the regulation of effort allocation (Huang-Pollock et al., 2012).
Having ADHD with deficits in executive function, compared to ADHD alone, is associated with an increased risk for grade retention and a decrease in academic achievement (Biederman et al., 2004). The relationship between ADHD and executive functions may also depend on subtype. One study found that children with an inattention ADHD subtype showed deficits in several executive function competencies (Tymms and Merrell, 2011), whereas children with the hyperactive-impulsive ADHD subtype may have fewer executive function deficits (Shuai et al., 2011) and may even have strengths that could be developed in appropriate educational environments.
Deficits in executive function have been studied in other developmental disorders as well, albeit often in less detail. They include autism (Bühler et al., 2011; Hill, 2004; Zelazo et al., 2002); attention and disruptive behav-
ior problems (Fahie and Symons, 2003; Hughes and Ensor, 2011); intellectual disabilities (Nader-Grosbois and Lefèvre, 2011; Neece et al., 2011; Vieillevoye and Nader-Grosbois, 2008); cerebral palsy (Jenks et al., 2012); Turner syndrome (Mazzocco and Hanich, 2010); developmental dyslexia (Brosnan et al., 2002; cf. Romine and Reynolds, 2005); and mathematics learning disabilities (Toll et al., 2010).
Other Learning Skills and Dispositions
Other learning skills that are important to early academic achievement include persistence, curiosity, self-confidence, intrinsic motivation, time perspective (e.g., the willingness to prioritize long-term goals over immediate gratifications), and self-control. The growth of emotional and cognitive self-regulation is also fundamentally related to many of these developing learning skills. In addition, social experiences, discussed later in this chapter, are important for the growth of these learning skills. Note also that although these skills are referred to sometimes as dispositions, they are fostered through early experience and can be supported through intentional caregiving and instructional practices; they are not simply intrinsic traits in the child.
A capacity for focused engagement in learning is apparent from very early in life, although it is also true that these learning competencies develop significantly throughout early childhood as processes of neurobiological development interact with children’s social experiences to enable greater persistence, focused attention, delayed gratification, and other components of effective learning and problem solving. As a consequence, very young children are likely to approach new learning situations with enthusiasm and self-confidence but at young ages may not necessarily bring persistence or creativity in confronting and solving challenging problems. Older preschoolers, by contrast, are more self-regulated learners. They approach new learning opportunities with initiative and involvement, and they are more persistent and more likely to solve problems creatively, by proposing their own ideas (NRC, 2001).
Considerable research confirms the importance of these skills to early learning. Individual differences in infants’ “mastery motivation” skills—persistence, focus, and curiosity in exploration and problem solving—predict later cognitive abilities and achievement motivation (Busch-Rossnagel, 2005; Morgan et al., 1990; Wang and Barrett, 2013). In preschool-age children, learning skills that include motivation, engagement, and interest in learning activities have been found in longitudinal studies to predict children’s cognitive skills at school entry (Duncan et al., 2005, 2007). Similarly, these characteristics continue to be associated with reading and mathematics achievement in the early elementary grades (Alexander et al., 1993).
Differences in these learning skills are especially associated with academic achievement for children in circumstances of economic disadvantage who face various kinds of self-regulatory challenges (Blair and Raver, 2012; Howse et al., 2003a).
Much of school success requires that children prioritize longer-term rewards requiring current effort over immediate satisfactions. The classic demonstration of this skill comes from a series of studies led by Walter Mischel beginning in the 1960s. Young children were offered the option of choosing an immediate, smaller reward or a larger reward if they waited to receive it later. For several years developmental outcomes for these children were tracked, which revealed that children who were better able to delay gratification at age 4 scored higher on measures of language skills, academic achievement, planful behaviors, self-reliance, capacity to cope with stress and frustration, and social competence measured in adolescence and adulthood (Mischel et al., 1988). Other studies have reported consistent findings. Early development in the ability to prioritize future, long-term goals over short-term lesser gains improves children’s chances of academic achievement and securing and maintaining employment (Rachlin, 2000). Conversely, the inability to delay gratification is associated with young children’s aggressive behavior, conduct problems, poorer peer relationships, and academic difficulty during preschool and the transition to elementary school (Olson and Hoza, 1993) as well as later outcomes, including academic failure, delinquency, and substance abuse in adolescence (Lynam et al., 1993; Wulfert et al., 2002).
The ways that children view themselves as learners are also important. Young children’s self-perceived capability to master learning challenges develops early and exerts a continuing influence on their academic success. Early self-evaluations of competence are based on the positive and negative evaluations of children’s behavior and competence by parents (Stipek et al., 1992). Parent and educator expectations for children’s success remain important. High parent expectations for children’s school achievement are associated with children’s later academic performance, and this is also true of educator expectations. In one longitudinal study, teacher expectations for children’s math achievement in grades 1 and 3 directly predicted children’s scores on standardized achievement tests 2 years later, and expectations for reading achievement had indirect associations with later reading scores. There was also evidence in this study that expectations were especially influential for academically at-risk students (Hinnant et al., 2009).
Messages from parents and educators are also important in shaping how children attribute their own success and failure which, in turn, predicts their future effort and expectations of success. Children develop implicit theories in the early years about who they are as a person and what it means to be intelligent. Some children come to view intelligence as a fixed trait
(i.e., one is either smart or not), whereas others see it as a more malleable trait that can be changed through effort and persistence. Educators and parents who approach learning goals by promoting and rewarding effort, persistence, and willingness to take on challenging problems increase children’s motivation and their endorsement of effort as a path to success. In contrast, children receiving messages that intelligence is stable and cannot be improved through hard work are discouraged from pursuing difficult tasks, particularly if they view their abilities as low (Heyman and Dweck, 1992). These patterns of “helpless” versus “mastery-oriented” motivation are learned in the preschool years and remain stable over time (Smiley and Dweck, 1994).
These perceptions and patterns of motivation can be especially significant as children learn academic subjects, such as mathematics (Clements and Sarama, 2012). People in the United States have many negative beliefs and attitudes about mathematics (Ashcraft, 2006). One deeply embedded cultural belief is that achievement in mathematics depends mainly on native aptitude or ability rather than effort. Research shows that the belief in the primacy of native ability hurts students and, further, it is simply untrue.
Throughout their school careers, students who believe—or are helped to understand—that they can learn if they try working longer on tasks have better achievement than those who believe that either one “has it” (or “gets it”) or does not (McLeod and Adams, 1989; Weiner, 1986). Researchers have estimated that students should be successful about 70 percent of the time to maximize motivation (Middleton and Spanias, 1999). If students are directly assured that working hard to figure out problems, including making errors and being frustrated, are part of the learning process it can diminish feelings of embarrassment and other negative emotions at being incorrect. In contrast, students’ learning can be impeded if educators define success only as rapid, correct responses and accuracy only as following the educator’s example (Middleton and Spanias, 1999). In addition, students will build positive feelings about mathematics if they experience it as a sense-making activity. Most young students are motivated to explore numbers and shapes and have positive feelings about mathematics (Middleton and Spanias, 1999). However, after only a couple of years in typical schools, they begin to believe that only some people have the ability to do math.
A related pattern relating perceptions and emotions to learning is seen with students who experience mathematics anxiety. Primary grade students who have strong math anxiety, even alongside strong working memory, have been found to have lower mathematics achievement because working memory capacity is co-opted by math anxiety (Beilock, 2001; Ramirez et al., 2013). Research has shown that primary grade students who “feel panicky” about math have increased activity in brain regions that are associated with fear, which decreases activity in brain regions associated with
problem solving (Young et al., 2012). Early identification and treatment of math anxiety may prevent children with high potential from avoiding mathematics and mathematics courses (Ramirez et al., 2013).
The development of social and emotional competence is an important part of child development and early learning. Socioemotional competence has been described as a multidimensional construct that contributes to the ability to understand and manage emotions and behavior; to make decisions and achieve goals; and to establish and maintain positive relationships, including feeling and showing empathy for others. Although their importance is widely recognized, universal agreement is lacking on how to categorize and define these areas of development. The Collaborative for Academic, Social, and Emotional Learning offers a summary construct with five interrelated groups of competencies that together encompass the areas typically considered to be part of socioemotional competence (see Figure 4-2).
Socioemotional competence increasingly is viewed as important for a child’s early school adjustment and for academic success at both the preschool and K-12 levels (Bierman et al., 2008a,b; Denham and Brown, 2010; Heckman et al., 2013; La Paro and Pianta, 2000; Leerkes et al., 2008). A growing body of research addresses the relationship between dimensions of socioemotional competence and cognitive and other skills related to early learning and later academic achievement (Bierman et al., 2008a,b; Graziano et al., 2007; Howse et al., 2003b; Miller et al., 2006). Socioemotional development early in life also increasingly is understood to be critically important for later mental well-being, and for contributing to subsequent mental health problems when there are enduring disturbances in socioemotional functions (IOM and NRC, 2009; Leckman and March, 2011).
There are several reasons why socioemotional development is important to early learning and academic success. As discussed in detail later in this section, early learning is a social activity in which these skills are important to the interactions through which learning occurs and is collaboratively shared. Socioemotional competence gives children the capacity to engage in academic tasks by increasing their ability to interact constructively with teachers, work collaboratively with and learn from peers, and dedicate sustained attention to learning (Denham and Brown, 2010). Further, behavioral and emotional problems not only impede early learning but also pose other risks to long-term success. Substantial research has examined the relationship between delays and deficits in children’s social skills and challenging behavior, such as serious problems getting along with peers or cooperating with educators (Zins et al., 2007). When challenging behav-
FIGURE 4-2 Elements of socioemotional competence.
SOURCE: Collaborative for Academic, Social, and Emotional Learning (http://www.casel.org/social-and-emotional-learning/core-competencies, accessed March 24, 2015).
ior is not resolved during the early years, children with persistent early socioemotional difficulties experience problems in socialization, school adjustment, school success, and educational and vocational adaptation in adolescence and adulthood (e.g., Dunlap et al., 2006; Lane et al., 2008; Nelson et al., 2004). Thus attention to socioemotional competence also is important from the perspective of addressing early emerging behavior problems before they become more serious.
A variety of evidence-based approaches can be implemented to strengthen socioemotional competence for young children (Domitrovich et al., 2012; IOM and NRC, 2009). These approaches typically entail strategies designed to improve children’s emotion identification and understanding combined with the development of social problem-solving skills; practice in simple emotion regulation strategies; and coaching in prosocial behavior through strategies that can involve role playing, modeling, and reinforcement of socially competent behavior. Importantly, as discussed further in Chapter 6, these strategies can be incorporated into daily classroom practice to provide children with everyday socioemotional learning.
Relational Security and Emotional Well-Being
As noted earlier in the discussion of self-regulation, socioemotional competencies contribute to the development of relationships with parents, educators, and peers. The development of positive relationships enables young children to participate constructively in learning experiences that are inherently social. The emotional support and security provided by positive relationships contributes in multifaceted ways to young children’s learning success. Research on the security of attachment between young children and their parents illustrates this point, and provides a basis for considering the nature of children’s relationships with educators and peers.
A secure parent–child attachment is widely recognized as foundational for healthy development, and the evolving understanding of the importance of attachment encompasses research in developmental psychology and developmental neuroscience (as discussed in Chapter 3) (Schore and Schore, 2008; Thompson, 2013). Research has shown that securely attached children receive more sensitively responsive parental care, and in turn develop greater social skills with adults and peers and greater social and emotional understanding of others, show more advanced moral development, and have a more positive self-concept (see Thompson, 2013, for a review). Securely attached children also have been found to be more advanced in cognitive and language development and to show greater achievement in school (de Ruiter and van IJzendoorn, 1993; van Ijzendoorn et al., 1995; West et al., 2013). This association has been found for infants, preschool-age children, and older children, suggesting that it is fairly robust.
Most researchers believe that the association between attachment security and cognitive competence derives not from a direct link between the two, but from a number of processes mediating a secure attachment and the development of cognitive and language skills (O’Connor and McCartney, 2007). The mediators that have been studied include the following:
- Early confidence and competence at exploration—One of the functions of a secure attachment is to enable infants and young children to better explore the environment, confident in the caregiver’s support and responsiveness if things go awry. An extensive research literature, focused primarily on young children, confirms this expectation (van Ijzendoorn et al., 1995). Early in life, exploratory interest is likely to lead to new discoveries and learning.
- Maternal instruction and guidance—Consistent with the sensitivity that initially contributes to a secure attachment, considerable research has shown that the mothers of securely attached children continue to respond supportively in ways that promote the child’s social and cognitive achievements (Thompson, in press). In particular, these mothers talk more elaboratively with their children in ways that foster the children’s deeper understanding and in so doing help support the children’s cognitive growth (Fivush et al., 2006). Furthermore, increased mother–child conversation is likely to foster the child’s linguistic skills.
- Children’s social competence with adults and peers—Securely attached children develop enhanced social skills and social understanding that enhance their competence in interactions with peers and adults in learning environments. In this light, their greater cognitive and language competencies may derive, at least in part, from more successful interactions with social partners in learning contexts. (See the detailed discussion of social interaction as a forum for cognitive growth later in this section.)
- Self-regulatory competence—Several studies suggest that securely attached children are more skilled in the preschool and early grade school years at self-regulation, especially as it is manifested in greater social competence and emotion regulation. Self-regulatory competence also may extend to children’s greater attentional focus, cognitive self-control, and persistence in learning situations. In one recent report, the association of attachment security with measures of school engagement in the early primary grades was mediated by differences in children’s social self-control; attentional impulsivity also varied with the security of attachment (Drake et al., 2014; Thompson, 2013).
- Stress management—One of the functions of a secure attachment is that it supports the social buffering of stress by providing children with an adult who regularly assists them in challenging circumstances. The social buffering of stress may be an especially important aspect of how a secure attachment contributes to cognitive competence for children in disadvantaged circumstances when stress is likely to be chronic and potentially overwhelming (see Gunnar and Donzella, 2002, for a review; Nachmias et al., 1996) (see also the discussion of chronic stress and adversity later in this chapter).
In addition to the substantial research on parent–child attachment and the development of cognitive competence, a smaller but significant research literature focuses on the development of attachments between children and educators and how those attachments contribute to children’s success in structured learning environments (e.g., Ahnert et al., 2006; Birch and Ladd, 1998; Howes and Hamilton, 1992; Howes et al., 1998; Ladd et al., 1999; Mitchell-Copeland et al., 1997; Pianta and Stuhlman, 2004a,b). In some respects, the processes connecting children’s learning achievement with the supportive, secure relationships they develop with educators are similar to those observed with parent–child attachments. As with their parents and other caregivers, children develop attachments to their educators, and the quality of those relationships has a significant and potentially enduring influence on their classroom success (Hamre and Pianta, 2001). Secure, warm relationships with educators facilitate young children’s self-confidence when learning and assist in their self-regulatory competence, and there is evidence that children with such relationships in the classroom learn more than those who have more difficult relationships with educators (NICHD Early Child Care Research Network, 2003; Pianta and Stuhlman, 2004b).
In one study, preschoolers identified as academically at risk based on demographic characteristics and reports of problems by their kindergarten teachers were followed to the end of first grade (Hamre and Pianta, 2005). The children with first-grade teachers who provided high amounts of instructional and emotional support had achievement scores comparable to those of their low-risk peers. Support was measured by teacher behaviors such as verbal comments promoting effort, persistence, and mastery; conversations using open-ended questions; encouragement of child responsibility; sensitivity; and a positive classroom climate. O’Connor and McCartney (2007) likewise found that positive educator–child relationships from preschool through third grade were associated with higher third-grade achievement, and that much of this achievement derived from how positive relationships promoted children’s classroom engagement.
Positive educator–child relationships are especially important during the transition to school, when children’s initial expectations about school and adjustment to its social demands take shape (Ladd et al., 1999; Silver et al., 2005). Children who develop more positive relationships with their teachers in kindergarten are more positive about attending school, more excited about learning, and more self-confident. In the classroom they achieve more compared with children who experience more conflicted or troubled relationships with their teachers (Birch and Ladd, 1997; NICHD Early Child Care Research Network, 2003; Pianta and Stuhlman, 2004b). A positive relationship with educators may be especially important for children who are at risk of academic difficulty because such a relationship can provide support for self-confidence and classroom involvement (Pianta et al., 1995).
A similar association is seen for peer relationships. Children who experience greater friendship and peer acceptance tend to feel more positive about coming to school, participate more in activities in the classroom, and achieve more in kindergarten (Ladd et al., 1996, 1997). Peer rejection is associated with less classroom participation, poorer academic performance, and a desire to avoid school (Buhs and Ladd, 2001).
Taken together, research documenting the association between the security of attachment and the development of cognitive and language competence, as well as the stronger academic performance of securely attached children, highlights the multiple ways in which supportive relationships contribute to early learning. In particular, such relationships with parents, educators, and even peers provide immediate support that helps children focus their energies on learning opportunities, and they also foster the development of social and cognitive skills that children enlist in learning.
Emotion Regulation and Self-Management
Another element of socioemotional competence, touched on earlier in the section on general learning competencies, is self-regulation of emotion, or emotion regulation, which can affect learning behaviors and relationships with adults and peers. As noted in that earlier discussion, emotion regulation is closely intertwined with cognitive self-regulation and executive function. Emotion regulation processes include emotional and motivational responses to situations involving risk and reward (e.g., Kerr and Zelazo, 2004). They are frequently inhibitory; that is, they include the ability to suppress one response (e.g., grabbing a toy from another) so as to respond in a better way (asking for or sharing the toy). The development of emotion regulation and other forms of self-management in the early years is based on slowly maturing regions of the prefrontal cortex that continue to develop throughout adolescence and even early adulthood. Thus, early
learners are maturationally challenged to manage their attention, emotions, and behavioral impulses effectively in a care setting or classroom.
Because they have difficulty cooperating or resolving conflicts successfully, children who lack effective self-regulation do not participate in a productive way in classroom activities—including learning activities (Broidy et al., 2003; Ladd et al., 1999; Saarni et al., 1998). Children with poor emotion regulation skills may act disruptively and aggressively; they then receive less support from their peers, which in turn may undermine their learning (Valiente et al., 2011). Poor emotion regulation also diminishes positive educator–child interactions, which, as discussed in the previous section, has been shown to predict poor academic performance and behavior problems (Hamre and Pianta, 2001; Neuenschwander et al., 2012; Raver and Knitzer, 2002).
Coupled with joint attention and delay of gratification, self-regulation skills are linked to social competence and ease the transition to kindergarten (Huffman et al., 2000; McIntyre et al., 2006). Children with difficulty regulating emotion in preschool and kindergarten often display inappropriate behavior, fail to pay attention (affecting whether they recall and process information), and have difficulty following instructions, all of which contribute to learning problems (Eisenberg et al., 2010). Unfortunately, these difficulties tend to be common in preschool and kindergarten. They are an important determinant of whether educators and parents regard young children as “ready for school” (Rimm-Kaufman et al., 2000).
Some researchers also suggest that emotion regulation in preschool and kindergarten serves as an early indicator of later academic success (Graziano et al., 2007; Howse et al., 2003b; Trentacosta and Izard, 2007). In preschool, McClelland and colleagues (2007) found not only that emotion regulation predicted early skills in literacy and mathematics but also that growth in emotion regulation in 4-year-olds over a 1-year period was linked to greater gains in literacy, vocabulary, and math compared with children showing less growth. Reading disability and problem behavior may be a “chicken or egg” problem: students who have behavior problems in first grade are more likely to have reading difficulties in third grade and students who have reading difficulties in first grade are more likely to exhibit behavior problems in third grade (Morgan et al., 2008). Thus a particularly effective learning environment may be one that provides both effective reading instruction and support for behavioral self-regulation (Connor et al., 2014).
Young children are better enabled to exercise self-regulation in the company of educators who have developmentally appropriate expectations for their self-control, provide predictable routines, and offer guidance that scaffolds their developing skills of self-management, especially in the context of carefully designed daily practices in a well-organized setting (Bodrova and Leong, 2012). Indeed, in an intervention for academically
at-risk young children, the Chicago School Readiness Project gave Head Start teachers specialized training at the beginning of the year in classroom management strategies to help lower-income preschoolers better regulate their own behavior. At the end of the school year, these children showed less impulsiveness, fewer disruptive behaviors, and better academic performance compared with children in classrooms with teachers who received a different training regimen (Raver et al., 2009, 2011).
Conclusion About the Ability to Self-Regulate
The ability to self-regulate both emotion and cognitive processes is important for learning and academic achievement, affecting children’s thinking, motivation, self-control, and social interactions. Children’s progress in this ability from birth through age 8 is influenced by the extent to which relationships with adults, learning environments, and learning experiences support this set of skills, and their progress can be impaired by stressful and adverse circumstances.
Social and Emotional Understanding
As described earlier in this chapter, even infants and toddlers have an implicit theory of mind for understanding how certain mental states are associated with people’s behavior. From their simple and straightforward awareness that people act intentionally and are goal directed; that people have positive and negative feelings in response to things around them; and that people have different perceptions, goals, and feelings, young children develop increasingly sophisticated understanding of the mental experiences that cause people to act as they do (Wellman, 2011). They realize, for example, that people’s beliefs about reality can be accurate or may be mistaken, and this realization leads to their understanding that people can be deceived, that the child’s own thoughts and feelings need not be disclosed, and that not everybody can be believed (Lee, 2013; Mills, 2013). They appreciate that people’s thinking may be biased by expectations, prior experiences, and desires that cause them to interpret the same situation in very different ways (Lalonde and Chandler, 2002). They also begin to appreciate how personality differences among people can cause different individuals to act in the same situation in very different ways (Heyman and Gelman, 1999).
These remarkable advances in social understanding are important to children’s developing socioemotional skills for interacting with educators and peers. These advances also are fostered by children’s classroom experiences. Children learn about how people think and feel from directly observing; asking questions; and conversing about people’s mental states with trusted informants, such as parents (Bartsch and Wellman, 1995; Dunn, 2002; Thompson
et al., 2003). Similarly, interactions with educators and peers provide young children with apt lessons in mutual understanding and perspective taking, cooperation, conflict management, personality differences and similarities, and emotional understanding in an environment where these skills are developing. This is especially so when educators can use children’s experiences as forums for developing social and emotional understanding, such as when they explain why peers are feeling the way they do, suggest strategies for resolving conflict over resources or a point of view, or involve children in collective decision making involving different opinions.
Self-Awareness and Early Learning
How young children think of themselves as learners, and in particular their self-perceived efficacy in mastering new understanding, is an early developing and continuously important influence on their academic success. Young children become increasingly sensitive to the positive and negative evaluations of their behavior by parents, which serve as the basis for their self-evaluations (Stipek et al., 1992). In one study, mothers who provided positive evaluations, gentle guidance, and corrective feedback during teaching tasks with their 2-year-olds had children who, 1 year later, were more persistent and less likely to avoid difficult challenges. By contrast, mothers who were intrusively controlling of their toddlers had children who, 1 year later, responded with shame when they had difficulty (Kelley et al., 2000). Gunderson and colleagues (2013) found that 14- to 38-month-old children whose parents praised their efforts during unstructured home observations were more likely, as third graders, to believe that abilities are malleable and can be improved.
An extensive research literature documents the effects of parents’ and educators’ performance feedback on children’s self-concept and motivation to succeed. Most of this research was conducted with older children and adolescents because of their more sophisticated understanding of differences in ability (see Wigfield et al., 2006, for a review); however, preschoolers and early primary grade students are also sensitive to success and failure and to their imputed causes. In a study by Cimpian and colleagues (2007), for example, 4-year-old children were represented by puppets whose performance was praised by a teacher using either generic feedback (“You are a good drawer.”) to imply trait-based (ability-centered) success or nongeneric feedback (“You did a good job drawing.”) to imply situation-based (effort-centered) success. The children did not differ in their self-evaluations after hearing praise of either kind, but when their puppet subsequently made a mistake and was criticized for it, the 4-year-olds who had heard generic feedback evaluated their performance and the situation more negatively than did children hearing nongeneric feedback, suggesting
that they interpreted criticism as reflecting deficits in their ability. Similar results have been reported with kindergarteners by Kamins and Dweck (1999) and by Zentall and Morris (2010), with the latter indicating that task persistence as well as self-evaluation were strengthened by the use of nongeneric performance feedback.
Parent and educator expectations for children’s academic success also are important influences. High parental expectations for children’s school achievement are associated with children’s later academic performance, and this association often is mediated by the greater involvement of parents in the preschool or school program and other practices that support children’s school success (Baroody and Dobbs-Oates, 2009; Englund et al., 2004; Mantzicopoulos, 1997). The role of educator expectations in children’s success is illustrated by a longitudinal study in which teacher expectations for children’s math achievement in grades 1 and 3 directly predicted children’s scores on standardized achievement tests 2 years later; teacher expectations for reading achievement had indirect associations with later reading scores. The results of this study also suggest that teacher expectations were especially influential for academically at-risk students (Hinnant et al., 2009).
Social Interaction as a Forum for Cognitive Growth
A wider perspective on the importance of socioemotional skills for academic success is gained by considering the importance of social experiences for early learning. Contemporary research has led developmental scientists to understand the mind’s development as deriving jointly from the child’s naturally inquisitive activity and the catalysts of social experience. Sometimes these social experiences are in formal teaching and other pedagogical experiences, but often they take the form of adults and children sharing in activities that provide the basis for early learning, in a kind of “guided participation” (e.g., Rogoff, 1991). These activities can be as simple as the one-sided “conversation” parents have with their infant or toddler from which language skills develop, or the shared sorting of laundry into piles of similar color, or labeling of another child’s feelings during an episode of peer conflict. In short, considerable early learning occurs in the course of a young child’s ordinary interactions with a responsive adult.
Social experiences provide emotional security and support that enables learning and can also contribute to the development of language, number skills, problem solving, and other cognitive and learning skills that are foundational for school readiness and academic achievement. Through their interactions with children, adults provide essential stimulation that provides rapidly developing mental processes with catalysts that provoke further learning. Conversely, the lack of these catalysts contributes to learning disparities by the time that children become preschoolers. These processes
are well illustrated by considering the growth of language and literacy skills and of mathematical understanding.
Language and Literacy
It is difficult to think of any child developing language apart from social interactions with others. As discussed earlier in this chapter, variability in these experiences, beginning in infancy, helps account for socioeconomic disparities in language and mathematical skills that are apparent by the time children enter school. In a widely cited study, Hart and Risley (1995) recorded 1 hour of naturally occurring speech in the homes of 42 families at monthly intervals beginning when children were 7-9 months old and continuing until they turned 3 years. They found that by age 3, children living in the most socioeconomically advantaged families had a working vocabulary that was more than twice the size of that of children growing up in the most disadvantaged families. The latter group of children also was adding words more slowly than their advantaged counterparts. The differences in children’s vocabulary size were associated, in part, with how many words were spoken to them during the home observations, with a much richer linguistic environment being characteristic of the most advantaged homes. In addition, words were used in functionally different ways, with a much higher ratio of affirmative-to-prohibitive language being used in the most advantaged homes and a much lower ratio (i.e., below 1) being characteristic of the most disadvantaged homes. Differences in the language environment in which children grew up were, in other words, qualitative as well as quantitative in nature. Further research with a subset of 29 families in this sample showed that 3-year-olds’ vocabulary size significantly predicted their scores on standardized tests of language skill in third grade (Hart and Risley, 1995).
A later study by Fernald and colleagues (2013) confirms and extends these findings. A sample of 48 English-learning infants from families varying in socioeconomic status was followed from 18 to 24 months. At 18 months, significant differences between infants from higher- and lower-income families were already seen in vocabulary size and in real-time language processing efficiency. By 24 months, a 6-month gap was found between the two groups in processing skills related to language development. A companion study by Weisleder and Fernald (2013) with 29 lower-income Spanish-speaking families found that infants who experienced more child-directed speech at 19 months had larger vocabularies and greater language processing efficiency at 24 months. But adult speech that was simply overheard by infants (i.e., not child directed) at 19 months had no association with later language (Schneidman et al., 2013). These studies indicate that child-directed speech, and perhaps the social interaction that accompanies it, is
what strengthens infants’ language processing efficiency. As in the Hart and Risley (1995) study, differences in family language environments were both qualitative and quantitative in nature. These findings are important in light of the association between the socioeconomic status of children’s families and their language skills (Bradley and Corwyn, 2002).
The findings of these studies are consistent with those of studies of the social experiences in and outside the home that promote language learning in early childhood. (See also the section on language and literacy under “Learning Specific Subjects” earlier in this chapter.) According to one longitudinal study, language and literacy skills in kindergarten were predicted by several aspects of the language environment at home and in classrooms in the preschool years. The characteristics of adult language that stimulated young children’s language development included adult use of varied vocabulary during conversations with children; extended discourse on a single topic (rather than frequent topic switching); and diversity of language-related activities, including storybook reading, conversation related to children’s experiences and interests, language corrections, and pretend play (Dickinson, 2003; Dickinson and Porche, 2011; Dickinson and Tabors, 2001). These elements of the early childhood social environment predicted both kindergarten language skills and fourth-grade language and reading abilities. Other studies show that extensive use of descriptive language (e.g., labeling and commenting on people’s actions) related to the child’s current experience contributes to the quality of children’s language development. Shared storybook reading also has been found to enhance the language skills of young children in lower-income homes (Raikes et al., 2006). Stated differently, what matters is not just how much language young children are exposed to but the social and emotional contexts of language shared with an adult.
Language and literacy development is a major focus of instruction in prekindergarten and K-3 classrooms, and the instructional strategies used by teachers are both more formal and more sophisticated than those used in early childhood classrooms. Duke and Block (2012) have noted that in primary grade classrooms, vocabulary, reading comprehension, and conceptual and content knowledge are not adequately emphasized. The practices that would enhance early reading skills are embedded in children’s social experiences with educators and peers in the classroom. They involve children interacting with partners throughout reading activity, and teachers explaining and discussing vocabulary terms and encouraging children to make personal connections with the concepts in the text.
Number Concepts and Mathematics
Language and literacy skills are the best-studied area in which early social experiences are influential, but they are not the only skills for which social interactions are important. Social experiences also are important for mathematics, such as for developing an understanding of numbers as well as early number and spatial/geometric language. Infants have an approximate number system that enables them to distinguish different quantities provided that the numerical ratio between them is not small, and this discrimination ability improves with increasing age (see Box 4-5 earlier in this chapter). There is some evidence that early individual differences in this ability are consistent during the first year and predict later mathematical abilities, although the reason for this remains unclear (Libertus and Brannon, 2010; Starr et al., 2013). Toddlers also are beginning to comprehend certain number principles, such as one-to-one correspondence (Slaughter et al., 2011). How adults talk about number is important. In one study, everyday parent–child discourse was recorded for 90 minutes every 4 months when the child was between 14 and 30 months old. The amount of parents’ spontaneous “number talk” in these conversations (e.g., counting objects, references to time) was predictive of children’s cardinal number knowledge (i.e., the knowledge that “four” refers to sets with four items) at 46 months (Levine et al., 2010). Particularly important was when parents counted or labeled fairly large sets of objects within the child’s view, providing concrete referents for parent–child interaction over number (Gunderson and Levine, 2011).
Klibanoff and colleagues (2006) found that in early childhood, teachers’ “math language”—that is, the frequency of their verbal references to number and geometric concepts—varied greatly for different teachers, but it significantly predicted progress in preschoolers’ mathematical knowledge over the course of the school year. Similarly, another study found that parents’ number-related activities at home with their young children were highly variable, but parents who engaged in more of these activities had children with stronger mathematical skill on standardized tests (Blevins-Knabe and Musun-Miller, 1996). These practices in the classroom and at home help explain the significant socioeconomic disparities in number understanding by the time children arrive at school (Klibanoff et al., 2006; Saxe et al., 1987). In addition to spoken references to numerical and geometric concepts, adults stimulate developing mathematical understanding when they incorporate these concepts into everyday activities, including games and other kinds of play; prompt children’s explanations for numerical inferences; probe their understanding; and relate mathematical ideas to everyday experience (NRC, 2009). Unfortunately, the quality of mathematical instruction is highly variable in preschool and early primary grades (discussed further in Chapter 6).
Taken together, these studies suggest the diverse ways in which social experiences provide catalysts for children’s developing language and number skills that are the focus of later academic work. In these domains, adult practices provide essential cognitive stimulants beginning in infancy. Similar practices—adapted to young children’s developing skills—remain important as children proceed through the primary grades.
Relationships and Early Learning: Implications for Adults
The relationship of an adult to a child—the emotional quality of their interaction, the experiences they have shared, the adult’s beliefs about the child’s capabilities and characteristics—helps motivate young children’s learning, inspire their self-confidence, and provide emotional support to engage them in new learning.
Commonplace interactions provide contexts for supporting the development of cognitive and learning skills and the emotional security in which early learning thrives. Applauding a toddler’s physical skills or a second-grader’s writing skills, counting together the leaves on the sidewalk or the ingredients of a recipe, interactively reading a book, talking about a sibling’s temper tantrum or an episode of classroom peer conflict—these and other shared experiences contribute to young children’s cognitive development and early learning.
Conclusion About Socioemotional Development
Socioemotional development contributes to the growth of emotional security that enables young children to fully invest themselves in new learning and to the growth of cognitive skills and competencies that are important for learning. These capacities are essential because learning is inherently a social process. Young children’s relationships—with parents, teachers, and peers—thus are central to the learning experiences that contribute to their later success.
PHYSICAL DEVELOPMENT AND HEALTH
Child development and early learning are closely intertwined with child health. Indeed, each is a foundation for outcomes in the other: health is a foundation for learning, while education is a determinant of health (Zimmerman and Woolf, 2014). The Center on the Developing Child at Harvard University (2010) has described three foundational areas of child health and development that contribute to physical and mental well-being:
- Stable and responsive relationships—Such relationships provide young children with consistent, nurturing, and protective interactions with adults that enhance their learning and help them develop adaptive capacities that promote well-regulated stress response systems.
- Safe and supportive physical, chemical, and built environments—Such environments provide physical and emotional spaces that are free from toxins and fear, allow active exploration without significant risk of harm, and offer supports for families raising young children.
- Sound and appropriate nutrition—Such nutrition includes health-promoting food intake as well as eating habits, beginning with the future mother’s nutritional status even before conception.
This section examines interrelated topics of physical development, child health, nutrition, and physical activity and then touches on partnerships between the health and education sectors (also discussed in Chapter 5).
Physical development goes hand-in-hand with cognitive development in young children, and progress in one domain often relies on progress in the other. Similar to cognitive development, typical physical development follows a common trajectory among children but with individual differences in the rate of development. A child’s physical development encompasses healthy physical growth; the development of sensory systems, including vision and hearing; and development of the ability to use the musculoskeletal system for gross motor skills that involve large body movements as well as fine motor skills that require precision and the controlled production of sound for speaking. Sensory and motor development are critical for both everyday and classroom activities that contribute to cognitive development, early learning, and eventually academic achievement.
Young children’s growth in gross and fine motor skills develops throughout the birth through age 8 continuum—early on from holding their head up; rolling over; standing, crawling, and walking; to grasping cereal, picking up blocks, using a fork, tying shoelaces, and writing. A number of recent studies have focused on the relationships among the development of fine and gross motor skills in infants and young children, cognitive development, and school readiness. For example, one study found that students showing deficiencies in fine motor skills exhibited lower math and verbal scores (Sandler et al., 1992), and more recent studies have also shown that fine motor skills were strongly linked to later achievement
(Grissmer et al., 2010a; Pagani and Messier, 2012). Some of the same neural infrastructure in the brain that controls the learning process during motor development are also involved in the control of learning in cognitive development (Grissmer et al., 2010a). The evidence of the impact of motor skills on cognitive development and readiness for school calls for a shift in curricula to include activities that focus on fine motor skills, to include the arts, physical education, and play (Grissmer et al., 2010b).
Health has an important influence on early learning and academic achievement. Hair and colleagues (2006) found that poor health can be as important in contributing to struggles with academic performance in first grade as language and cognitive skills, along with lack of social skills. Not only are healthy children better prepared to learn, but participation in high-quality early childhood programs leads to improved health in adulthood, setting the stage for intergenerational well-being. Data from Head Start and from the Carolina Abecedarian Project indicate that high-quality, intensive interventions can prevent, or at least delay, the onset of physical and emotional problems from adolescence into adulthood (Campbell et al., 2014; Carneiro and Ginja, 2012). Data from a national longitudinal survey show that involvement in Head Start was associated with fewer behavior problems and serious health problems, such as 29 percent less obesity in males at 12 and 13 years of age. In addition, Head Start participants had less depression and obesity as adolescents and 31 percent less involvement in criminal activity as young adults. Similarly, long-term follow-up of adults who were enrolled in the Carolina Abecedarian Project revealed that males in their mid-30s in the project had lower rates of hypertension, obesity, and metabolic syndrome than controls. None of the males in the project had metabolic syndrome, compared with 25 percent of the control group. Further analysis of growth parameters indicated that those who were obese in their mid-30s were on that trajectory by 5 years of age, indicating the need for emphasis on healthy nutrition and regular physical activity beginning in early childhood. These studies suggest that the impact of early care and education programs on physical and emotional health is long term.
Sufficient, high-quality dietary intake is necessary for children’s health, development, and learning. Support for providing healthy nutrition for children and their families, including pregnant and expectant mothers, is vital. Adequate protein, calories, and nutrients are needed for brain development and function. While the rapid brain growth and development that occurs
in infants and toddlers may make children in this age group particularly vulnerable to dietary deficiencies, nutrition remains important as certain brain regions continue to develop through childhood into adolescence.
Nutrients, Cognitive Development, and Academic Performance
Deficiencies in protein, energy, and micronutrients such as iron, zinc, selenium, iodine, and omega-3 fatty acids have been linked to adverse effects on cognitive and emotional functioning (Bryan et al., 2004). Research has shown that iron-deficiency anemia (IDA) is associated with lower cognitive and academic performance (Bryan et al., 2004; Nyaradi et al., 2013; Taras, 2005). Children at an early school age who had IDA as an infant were found to have lower test scores than those who did not have IDA. Effects of severe IDA in infancy have been seen in adolescence. These effects include lower scores in motor functioning; written expression; arithmetic achievement; and some specific cognitive processes, such as spatial memory and selective recall (NRC and IOM, 2000). However, it is not clear whether children with iron deficiency but no anemia have similar outcomes (Taras, 2005). A review of daily iron supplementation in children aged 5-12 years studied in randomized and quasi-randomized controlled trials showed improvement in measures of attention and concentration, global cognitive scores, and, for children with anemia, intelligence quotient (IQ) scores (Low et al., 2013).
IDA in infancy also has been associated with impaired inhibitory control and executive functioning. Altered socioemotional behavior and affect have been seen in infants with iron deficiency regardless of whether anemia is present (Lozoff, 2011). One study found an association between iron supplementation in infancy and increased adaptive behavior at age 10 years, especially in the areas of affect and response to reward, which may have beneficial effects on school performance, mental health, and personal relationships (Lozoff et al., 2014).
Folate and iodine also have been shown to be important for brain development and cognitive performance (Bougma et al., 2013; Bryan et al., 2004; Nyaradi et al., 2013), although iodine deficiency is rare in the United States. While there is some evidence that zinc, vitamin B12, and omega-3 polyunsaturated fatty acids also may be important for cognitive development, the research on these associations is inconclusive (Bougma et al., 2013; Bryan et al., 2004; Taras, 2005).
Food Insecurity, Diet Quality, and Healthful Eating
Food insecurity and diet quality in children have both been linked to impaired academic performance and cognitive and socioemotional develop-
ment. Food insecurity refers to circumstances in which households do not have adequate food to eat, encompassing both inadequate quantity and nutritional quality of food (ERS, 2014). Food insecurity affects development not only by compromising nutrition but also by contributing to a factor in family stress (Cook and Frank, 2008). In 2012, 48 million Americans were food insecure, a fivefold increase from the 1960s and a 57 percent increase from the late 1990s. One in six Americans reported being short of food at least once per year. More than half of affected households were white, and more than half lived outside cities. Indeed, hunger in the suburbs has more than doubled since 2007. Two-thirds of food-insecure households with children have at least one working adult, typically in a full-time job (McMillan, 2014).
A recent review indicates that food insecurity is a “prevalent risk to the growth, health, cognitive, and behavioral potential of low-income children” (Cook and Frank, 2008, p. 202). Studies found that children in food-insufficient families were more likely than those in households with adequate food to have fair/poor health; iron deficiency; and behavioral, emotional, and academic problems. Infants and toddlers are at particular risk from food insecurity even at its least severe levels (Cook and Frank, 2008). Cross-sectional studies of children from developing countries have shown an association among general undernutrition and stunting, IQ scores, and academic performance (Bryan et al., 2004). Alaimo and colleagues (2001) found that food insecurity was linked to poorer academic and psychosocial outcomes in children ages 6 to 11 years. Similarly, Florence and colleagues (2008) observed that students with lower overall diet quality were significantly more likely to fail a literacy assessment. Subsequent research has shown that while food insecurity experienced earlier in childhood was associated with emotional problems that appeared in adolescence, cognitive and behavioral problems could be accounted for by differences in the home environments, such as family income and the household’s sensitivity to children’s needs (Belsky et al., 2010).
Eating breakfast, which can be related to food insecurity, diet quality, and healthful eating habits, has been associated with improved cognitive function, academic performance, and school attendance (Basch, 2011; Hoyland et al., 2009; Mahoney et al., 2005; Nyaradi et al., 2013; Rampersaud et al., 2005). According to two reviews of the effect of consuming breakfast in children and adolescents, the evidence suggests that children who consume breakfast—particularly those children whose nutritional status is compromised—may have improved cognitive function, test grades, and school attendance. The positive effects of school breakfast programs may be explained in part by their effect of increasing school attendance (Hoyland et al., 2009; Rampersaud et al., 2005). The composition of the breakfast meal may also be important to cognitive performance; a
breakfast meal with a low glycemic index, such as oatmeal, has been shown to improve cognitive function (Cooper et al., 2012; Mahoney et al., 2005).
In 2011, the Centers for Disease Control and Prevention (CDC) published a report documenting the relationship between healthy eating and increased life expectancy; improved quality of life; and fewer chronic diseases, including cardiovascular disease, obesity, metabolic syndrome, diabetes, and inadequate bone health (CDC, 2011). The report documents the high rate of iron deficiency among obese children and emphasizes the link between dental caries and unhealthy diet. Children are unlikely to follow recommendations for the number of servings of various food groups and they consume higher-than-recommended amounts of saturated fats, sodium, and foods with added sugar. Children’s eating behavior and food choices are influenced not only by taste preferences but also by the home environment and parental influences, including household eating rules, family meal patterns, and parents’ lifestyles. The school environment influences children’s eating behavior as well. The availability of unhealthy options in schools leads to poor choices by children, whereas research has shown that efforts to reduce the availability of sugar-sweetened beverages in the schools can have a positive impact on children’s choices (AAP Committee on School Health, 2004). There are also rising concerns about food insecurity in association with obesity; inexpensive foods tend not to be nutritious, and contribute to increasing rates of obesity (IOM, 2011; McMillan, 2014).
A recent Institute of Medicine (IOM) study linked increasing physical activity and enhancing physical fitness to improved academic performance, and found that this can be facilitated by physical activity built into children’s days through physical education, recess, and physical classroom activity (IOM, 2013). Likewise, the American Academy of Pediatrics recently highlighted the crucial role of recess as a complement to physical education, suggesting that recess offers cognitive, social, emotional, and physical benefits and is a necessary component of a child’s development (AAP Council on School Health, 2013). However, fewer than half of youth meet the current recommendation of at least 60 minutes of vigorous- or moderate-intensity physical activity per day, and recent years have seen a significant downward trend in the offering of daily physical education in schools at all levels (CDC, 2012; GAO, 2012). Positive support from friends and family encourages children to engage in physical activity, as do physical environments that are conducive to activity. However, the school environment plays an especially important role. The IOM report recommends that schools provide access to a minimum of 60 minutes of vigorous- or moderate-intensity physical activity per day, including an average of
30 minutes per day in physical education class for students in elementary schools (IOM, 2013).
Partnerships Between Health and Education
Each of the domains of child development and early learning discussed in this chapter can be supported through interventions that involve both the health and education sectors (see also the discussion of continuity among sectors in Chapter 5). Specific activities include coordinating vision, hearing, developmental, and behavioral screening to facilitate early identification of children with special needs; completing daily health checks; making appropriate referrals and collaborating with the child’s medical home and dental health services; ensuring that immunizations for the entire family and for the early care and education workforce are up to date; modifying and adapting services to meet the individual needs of the child; and providing support to the early care and education workforce to promote more inclusive practices for children with special needs. In addition, teaching and modeling skills in sanitation and personal hygiene will contribute to preventing illness. Furthermore, pediatric health care professionals can make an important contribution by promoting literacy. Extensive research documents the positive impact on early language and literacy development when a pediatric professional gives advice to parents about reading developmentally appropriate books with children as early as 6 months of age (AAP Council on Early Childhood et al., 2014).
There is evidence that coordinated efforts between educational settings and health care services lead to improved health. Head Start, the Infant Health and Development Program, and the Carolina Abecedarian Project are examples of early care and education programs that have integrated health care services into the intervention design, leading to positive health outcomes. Schools also can partner with pediatric health care professionals in their communities to identify opportunities to enhance physical activity in the school setting (AAP Committee on Sports Medicine and Fitness and AAP Committee on School Health, 2000). CDC (2011) has offered recommendations for promoting healthful eating and physical activity that include the following and, if placed in an appropriate developmental context, can be applied to care and education settings for children aged 0-8:
- Use a coordinated approach to develop, implement, and evaluate healthful eating and physical activity policies and practices.
- Establish school environments that support healthy eating and activity.
- Provide a quality school meal program and ensure that students have only appealing, healthy food and beverage choices offered outside of the school meal program.
- Implement a comprehensive physical activity program with quality physical education as the cornerstone.
- Implement health education that provides students with the knowledge, attitudes, skills, and experiences needed for healthy eating and physical activity.
- Provide students with health, mental health, and social services to address healthy eating, physical activity, and related chronic disease prevention.
- Partner with families and community members in the development and implementation of healthy eating and physical activity policies, practices, and programs.
- Provide a school employee wellness program that includes healthy eating and physical activity services for all school staff members.
- Employ qualified persons and provide them with professional development opportunities in staffing physical education; health education; nutrition services; health, mental health, and social services; and supervision of recess, cafeteria time, and out-of-school-time programs.
School-based health centers are another approach to partnering between health and education. They have been associated with improved immunization rates, better adherence to scheduled preventive examinations, and more treatment for illnesses and injuries, as well as fewer emergency room visits. For example, King and colleagues (2006) found that a school-based vaccination program significantly reduced influenza symptoms in the entire school. School-based mental health services also have been shown to be effective in addressing a wide range of emotional and behavioral issues (Rones and Hoagwood, 2000). School-based health centers have been shown to reduce nonfinancial barriers to health care (Keyl et al., 1996), and families also report more satisfaction with their care than in community or hospital settings (Kaplan et al., 1999).
Conclusion About Health, Nutrition, and Early Learning
Safe physical and built environments, health, and nutrition are essential to early learning and academic achievement. Food security and adequate nutrition are important to support cognitive development and participation in education, and food insecurity and poor nutrition can contribute to early learning difficulties. Care and education settings
provide an opportunity to promote healthful eating and physical activity in learning environments. Providing appropriate health and developmental screenings and follow-up care and services also is important in supporting development and early learning.
Health and Early Learning: Implications for Adults
Healthy children supported by healthy adults are better prepared to learn. Child health begins prior to conception and extends through pregnancy and throughout childhood. Therefore, the early care and education workforce must be prepared to work across generations to provide education, support, and community linkages to ensure that children grow up poised for success. Ongoing federal support for evidence-based home visiting programs for high-risk families that begin early in pregnancy and continue through early childhood is essential. Professionals working in family childcare, early childhood education centers, preschools, and early elementary schools need to have working knowledge of the relationship between health and children’s learning and development. Guidance related to nutrition, physical activity, oral health, immunizations, and preventive health care is essential across all early care and education settings. These professionals also need to be provided with supports and opportunities for close collaboration with health care services and their potential integration into or strengthened linkages with the early care and education setting.
EFFECTS OF CHRONIC STRESS AND ADVERSITY
As detailed in Chapter 3, one of the most important advances in developmental science in recent years has been the recognition that the brain incorporates experience into its development. Although experience is important at any age, early experiences are especially formative in the development of the brain’s structure and function. Human development is the result of the continuous interaction of genetics and experience. This interplay is true not just of brain development but of other aspects of human development as well. Research in this area encourages developmental scientists as well as parents and practitioners to consider how positive early experiences and enrichment, in formal and informal ways, may have a beneficial influence on the developing brain and in turn on the growth of thinking and learning. The brain’s openness to experience is, however, a double-edged sword—adverse early experiences can have potentially significant negative consequences for brain development and early learning.
As discussed in Chapter 3, evidence indicates that experiences of stress and adversity are biologically embedded and that individual differences exist in the health and developmental consequences of stress. A substantial
body of evidence now shows that adversity and stress in early life are associated with higher rates of childhood mental and physical morbidities, more frequent disturbances in developmental trajectories and educational achievement, and lifelong risks of chronic disorders that compromise health and well- being (Boyce et al., 2012; Hertzman and Boyce, 2010; Shonkoff et al., 2009). Children respond to stress differently. Many exhibit withdrawal, anger and irritability, difficulty paying attention and concentrating, disturbed sleep, repeated and intrusive thoughts, and extreme distress triggered by things that remind them of their traumatic experiences. Some develop psychiatric conditions such as depression, anxiety, posttraumatic stress disorder, and a variety of behavioral disorders (NCTSN, 2005).
What are the circumstances that contribute to chronic adversity and stress for children? All children can experience forms of chronic stress and adversity, but exposures to stress and adversity are socioeconomically layered. Poverty, discussed in more detail below, has been the best studied and is a highly prevalent source of early chronic stress (Blair and Raver, 2012; Evans and Kim, 2013; Jiang et al., 2014). Young children in the United States also suffer high levels of victimization through child abuse and exposure to domestic violence. The U.S. Department of Health and Human Services reported for the year 2012 that of all child abuse victims, approximately 60 percent were age 8 or younger (Children’s Bureau, 2013). The highest rates of child abuse and neglect, including fatalities related to child abuse, were reported for children in the first year of life. Comparable biological and behavioral effects of chronic stress have been studied in children in foster care, in those who experience significant or prolonged family conflict, in those who have a depressed parent, and in those who are abused or neglected (see Thompson, 2014, for a review).
It is noteworthy that these circumstances include not only those that most people would regard as sources of extreme stress for children (e.g., child abuse), but also those that an adult might regard as less significant because they may be less severe although persistent (e.g., parents’ chronic marital conflict, poverty). This broader range of circumstances that children experience as stressful is consistent with the view that, in addition to situations that are manifestly threatening and dangerous, children are stressed by the denial or withdrawal of supportive care, especially when they are young.
Culture also is closely interrelated with stress and adversity. Culture affects the meaning that a child or a family attributes to specific types of traumatic events as well as the ways in which they respond. Because culture also influences expectations regarding the self, others, and social institutions, it can also influence how children and families experience and express distress, grieve or mourn losses, provide support to each other, seek help, and disclose personal information to others. Historical or multigenerational
trauma also can influence cultural differences in responses to trauma and loss (NCTSN Core Curriculum on Childhood Trauma Task Force, 2012).
Building on the discussion in Chapter 3 of the biology of chronic stress and adversity, the following sections describe more broadly some of the contributing circumstances and consequences for young children, including the stressors associated with economic adversity; social buffering of stress; and the relationships among stress, learning, and mental health.
The Stressors of Economic Adversity
Children in any economic circumstances can experience stress and adversity, but considerable research on the effects of chronic stress on children’s development has focused on children living in families in poverty or with low incomes. The number of children in these conditions of economic adversity is considerable. In 2012, nearly half the children under age 6 lived in poverty or low-income families (defined as up to 200 percent of the federal poverty level,2 which remains a meagre subsistence) (Jiang et al., 2014). During that same year, more than half the children living with their families in homeless shelters were under the age of 6 (Child Trends, 2015).
The research is clear that poverty as a form of early chronic adversity is a risk factor to long-term physical and mental health, and that for children it can be a significant threat to their capacities to cope with stress, socialize constructively with others, and benefit from the cognitive stimulating opportunities of an early childhood classroom. Socioeconomic disparities in children’s experiences of socioemotional adversity and challenging physical environments are well documented (see, e.g., Evans et al., 2012). Factors other than economic status itself contribute to the challenges and stresses for children living in low-income families (Fernald et al., 2013). Poverty often is accompanied by the confluence of multiple sources of chronic stress, such as food insufficiency, housing instability (and sometimes homelessness), exposure to violence, environmental noise and toxins, dangerous neighborhoods, poor childcare and schools, family chaos, parents with limited capacity (e.g., resources, education, knowledge/information, time, physical or mental energy) to be supportive and nurturing, parents who are anxious or depressed, parents who are harsh or abusive caregivers, impoverished parent–child communication, and home environments lacking cognitively stimulating activities (Evans et al., 2012; Fernald et al., 2013).
As discussed in detail in Chapter 3, the perturbed biological processes that often accompany economic adversity include changes in the structure and function of children’s brain circuitry and dysregulation of their central
2 The 2012 federal poverty threshold was $23,364 for a family of four with two children, $18,480 for a family of three with one child, $15,825 for a family of two with one child.
stress response systems. For these children, therefore, the effects of the chronic stresses associated with economic adversity are likely to contribute to academic, social, and behavioral problems. These problems affect not only early learning and the development of cognitive skills (with impacts on the development of language being best documented) but also the development of learning skills associated with self-regulation and persistence, as well as coping ability, health, and emotional well-being (Blair and Raver, 2012; Evans and Kim, 2013).
In addition, developmental consequences related to socioeconomic status are not seen exclusively in children from severely impoverished families. Rather, evidence shows a graded effect of deprivation and adversity across the entire spectrum of socioeconomic status, with even those children from the second-highest social class showing poorer health and development compared with those from families of the very highest socioeconomic status (Adler et al., 1994; Hertzman and Boyce, 2010). Moreover, as discussed in Chapter 3, children are not equally affected by early adverse experiences. Genetic and epigenetic influences may have a role in whether some children are more resilient to early adversity than others (Rutter, 2012).
Detrimental prenatal influences may also be important (Farah et al., 2008; Hackman et al., 2010). Although this report focuses on children beginning at birth, child development and early learning also are affected by what a child is exposed to before birth, including influences of family disadvantage. Box 4-6 highlights major research findings on the relationships among family disadvantage, fetal health, and child development.
Social Buffering of Stress
The neuroscience of stress has yielded greater understanding of how the effects of stress may be buffered through social support. In behavioral and neurobiological studies of humans and animals, researchers have shown how individuals in adversity show diminished behavioral reactivity and better-regulated cortisol response, among other effects, in the company of people who provide them with emotional support. For children, these individuals often are attachment figures in the family or outside the home.
In health psychology, the benefits of social support for the development and maintenance of healthy practices and the control of disease pathology and healing have been studied since the 1970s (e.g., Cassel, 1976; Cobb, 1976). Social support also has been recognized as a contributor to psychological well-being for children and youth in difficult circumstances (Thompson and Goodvin, in press). In recent years, research on the neurobiology of the social buffering of stress has contributed to a better understanding of why social support has these benefits (Hostinar et al., 2014). In human and animal studies, social companionship in the context of adver-
Family Disadvantage, Fetal Health, and Child Development
Children from different family backgrounds—affected by systemic inequities and disadvantage—start life with starkly different health endowments. As but one example, having a low-birth-weight child (i.e., less than 2,500 grams) is more than twice as prevalent among African American mothers as white mothers, and the same differential is seen for white mothers with less than a high school degree compared with those with a college degree (Aizer and Currie, 2014). These differences in neonatal health have lasting effects on children’s development: studies of twins, sibling pairs, and singleton births indicate that a 10 percent improvement in birth weight is associated with around one-twentieth of a standard deviation increase in children’s test scores—a relationship that holds steady from kindergarten readiness through middle school (Bharadwaj et al., 2013; Figlio et al., 2014) and affects educational attainment and labor market success (Oreopoulos et al., 2008; Royer, 2009). Comparing twins and siblings is important because doing so reduces the likelihood of confounding factors (such as environmental exposures or maternal behavior) that affect both fetal health and children’s cognitive development. When researchers compare one twin with another, they are explicitly comparing the outcomes of two children who shared the same fetal environment.
Moreover, the existing evidence suggests that postnatal investments are somewhat more effective in improving outcomes for children with better fetal health. For instance, the Infant Health and Development Program, which randomly assigned low-birth-weight infants to preschool programs with varying intensities, had significant effects on child development for relatively high-birth-weight infants but no appreciable effects on the development of relatively low-birth-weight infants (McCormick et al., 2006). In addition, the relationship between fetal health and children’s outcomes is stronger for advantaged families (which tend to make greater postnatal investments) than for less advantaged families, suggesting that the efficacy of families’ postnatal investments is affected in part by birth weight (Figlio et al., 2014). This suggests that attention needs to be paid to improving birth outcomes rather than assuming that postnatal interventions will be widely and equitably effective.
There are a number of potential mechanisms through which disadvantage can affect children’s outcomes both directly and indirectly by way of in utero development and fetal health. For example, mothers exposed to higher levels of pollution tend to bear children who have poorer developmental outcomes compared with the children of equally disadvantaged mothers who have lower degrees of this exposure. This is a research question that is challenging to study because women who are exposed to these types of stressors tend to be particularly disadvantaged and potentially likely to have children with poorer outcomes regardless of the nature of in utero stressors (Almond and Currie, 2011). However, several recent
studies have made use of “natural experiments” to obtain causal evidence of the effects of a number of these stressors on children’s health and development. For instance, Currie and Walker (2009) made use of the fact that once states moved to electronic toll collection, the rate of engine idling near toll plazas was dramatically reduced, and found substantial improvements in early outcomes for children in families living proximate to toll plazas. Other studies have shown that exposure to air pollution, water pollution, and other environmental toxins contributes to diminished child outcomes (Currie, 2011; Currie et al., 2013; Isen et al., 2014; Sanders, 2012).
Disadvantaged women also face greater degrees of stress in their lives, and maternal exposure to stress is another potential mechanism leading to poorer subsequent developmental outcomes (Sandman et al., 2012; Thayer and Kuzawa, 2014). For example, mothers stressed by exposure to meteorological shocks such as hurricanes and extreme temperatures have children with worse outcomes (Currie and Rossin-Slater, 2013; Deschenes et al., 2009). Children of American women with Arabic names born in the period following the September 2001 terrorist attacks experienced considerably worse outcomes than those born just before the attacks, suggesting an important role of maternal psychological stress in children’s development (Lauderdale, 2006). A pronounced stressor for mothers is domestic violence, which is a particular risk for disadvantaged mothers (Vest et al., 2002). The evidence suggests that reducing domestic violence leads to improved outcomes for children in the household (Aizer, 2011).
Another potential mechanism is that women from less advantaged backgrounds have worse health and nutrition during pregnancy than their more advantaged counterparts, and maternal health and nutrition during pregnancy affect children’s early outcomes and development. For example, reduced maternal nutrition during pregnancy can result in poorer outcomes for children (Almond and Mazumder, 2011), while there is some evidence to indicate that improved nutrition through supplemental nutrition programs during pregnancy results in better birth outcomes (Colman et al., 2012; Figlio et al., 2014; Hoynes et al., 2011; Rossin-Slater, 2013). Likewise, nutrition supplementation in low-income countries has been shown to improve birth weight and educational outcomes (Abu-Saad and Fraser, 2010; Field et al., 2009).
In addition, disadvantaged expectant mothers are more likely to engage in behaviors detrimental to their health that could disadvantage their children. Disadvantaged expectant mothers have dramatically higher rates of smoking, prepregnancy hypertension, prepregnancy obesity, and prepregnancy diabetes compared with more advantaged mothers (Aizer and Currie, 2014), and at least some of these maternal behaviors are associated with differential outcomes for children. For example, there is causal evidence showing that maternal smoking leads to worse children’s outcomes (Bharadwaj et al., 2012; Currie et al., 2009; Lien and Evans, 2005).
sity appears to have effects on the biological regulators of hypothalamic–pituitary–adrenal (HPA) activity, contributing to greater regulation of stress reactivity through cortical and limbic influences. Social support also appears to stimulate the down-regulation of the proinflammatory tendencies induced by chronic stress, as well as processes driven by neurohormones, including oxytocin, that have other positive benefits (Kiecolt-Glaser et al., 2010). Stated differently, social support not only counters the negative effects of chronic stress reactivity but also stimulates constructive influences that contribute independently to greater self-regulation and well-being (Hostinar et al., 2014). This research is still at an early stage, and establishing reliable associations between brain and behavioral functioning in this area is a work in progress, but research findings are providing increasing support for these processes. In one study, for example, greater maternal support measured when children were preschoolers predicted children’s larger hippocampus volume at school age (Luby et al., 2012).
The potential benefits of social support as a buffer of chronic stress reactivity underscore the plasticity of developing behavioral and biological systems. Children in adversity need not suffer long-term harms arising from the effects of chronic stress exposure. In a study of families living in rural poverty, for example, toddlers’ chronic exposure to domestic violence was associated with elevated cortisol reactivity. However, this effect was buffered when mothers were observed to respond sensitively to their children (Hibel et al., 2011). Experimental interventions designed to change stressful circumstances and promote positive relationships have yielded similar findings. For example a program aimed at easing young children’s transition to new foster care placements and promoting warm, responsive, and consistent relationships with new foster parents provided individualized sessions with child therapists, weekly playgroup sessions, and support for foster parents. This program resulted in a normalization of the children’s HPA hyporesponsiveness (an effect of stress discussed in Chapter 3) (Fisher et al., 2007, 2011). Another example comes from an intervention based on attachment theory, which trained caregivers to better interpret and respond affectionately to infants and toddlers in foster care and similarly resulted in a normalization of HPA activity and lower cortisol reactivity (Dozier et al., 2006, 2008). There may be limits to these potential ameliorative effects, depending on the severity and duration of the exposure to adversity. Children who lived for an extended period in profoundly depriving Romanian orphanages, for example, did not show recovery of dysregulated cortisol reactivity, even after a prolonged period of supportive adoptive care (Gunnar et al., 2001).
Because interventions that can help children recover from the effects of chronic adversity can be expensive and time-consuming, however, it appears
sensible to try to prevent these effects from occurring. This can be accomplished by reducing exposure to influences that cause significant stress for children, and by strengthening supportive relationships that can buffer its effects. The development of warm, secure attachments between parents and children illustrates the latter approach. As discussed earlier in this chapter, attachment theorists argue that the reliable support provided by a secure attachment relationship enables infants and children to explore and learn from their experiences confidently with the assurance that a trusted adult is available to assist if difficulty ensues. In this view, secure attachments buffer stress and significantly reduce the child’s need to be vigilant for threat or danger. As noted previously, attachment research documents a range of benefits associated with secure parent–child relationships in childhood, including greater language skill, academic achievement, and social competence (see Thompson, 2008, for a review; West et al., 2013). The view that these accomplishments are explained, at least in part, by how secure attachments buffer stress for children is supported by studies documenting the better-regulated cortisol reactivity of young children with secure attachments in challenging situations (see Gunnar and Donzella, 2002, for a review; Nachmias et al., 1996).
Viewed in this light, it appears that the contributions adults make to children’s learning extend significantly beyond their reading, conversing, counting, and providing other direct forms of cognitive stimulation. An essential contribution is the safety and security they provide that not only buffers children against significant stress when this occurs, but also enables children to invest themselves in learning opportunities with confidence that an adult will assist them when needed. Such confidence not only enables children to learn more from the opportunities afforded them in the family and outside the home but also fosters their developing self-confidence, curiosity, and other learning skills that emerge in the context of secure relationships (Thompson, 2008). This is a benefit of secure, warm adult–child relationships for all children, not just those in adverse circumstances. This phenomenon is perhaps analogous to that seen in studies in which rat pups with nurturant mothers show enhanced learning and memory in low-stress contexts, whereas pups with nonnurturant mothers show greater proficiency in fear conditioning (Champagne et al., 2008).
One problem, however, is that children in adverse circumstances usually have parents and other caregivers who are affected by the same conditions of adversity. Thus, their parents may not be able to provide them with the support they need. This realization has led to the growth of two-generation interventions that are designed to assist children by providing support to their parents in difficult circumstances (Chase-Lansdale and Brooks-Gunn, 2014).
Stress, Learning, and Mental Health
Children learn readily in contexts of social support and emotional well-being, which derive from positive relationships with those who care for and educate them in the family and outside the home. In these contexts, adults can support and encourage developing competencies, convey positive values about learning and school, and help instill curiosity and self-confidence in children. By contrast, learning and cognitive achievement are hindered when children are troubled. This is the case for children from infancy through adolescence who are living in homes with significant marital conflict, when mothers are chronically depressed, when parents are hostile and coercive, or in other circumstances of family turmoil (e.g., Bascoe et al., 2009; Brennan et al., 2013; Canadian Paediatric Society, 2004; Davies et al., 2008).
Socioemotional hindrances to learning and cognitive achievement are apparent very early, before children have begun school, and continue to be important as children move into the primary grades. In educational settings, the emotional effects of problems in educator–child relationships can undermine children’s performance and their academic success (Hamre and Pianta, 2004; Jeon et al., 2014; Pianta, 1999; Pianta and Stuhlman, 2004b; Skinner and Belmont, 1993). As discussed in Chapter 3, when children are in circumstances of chronic or overwhelming stress, stress hormones affect multiple brain regions, including those relevant to learning, attention, memory, and self-regulation (McEwen, 2012; Ulrich-Lai and Herman, 2009). Over time and with continued exposure to stressful circumstances, these neurocognitive processes become altered as a result of the progressive wear and tear of stress hormones on biological systems as they adapt to this chronic stress. As a consequence, immunologic capacities become weakened (contributing to more frequent acute and chronic illness), self-regulation is impaired (contributing to poorer emotion regulation and impulse control), and cognitive and attentional capabilities are blunted (Danese and McEwen, 2012; Lupien et al., 2009; Miller et al., 2011). For children, these effects can help account for problems in following instructions, paying attention, managing impulsivity, focusing thinking, and controlling emotions in social encounters—each of which can impair classroom performance and academic achievement.
Young children’s vulnerability to stress and their reliance on the support of adults are two central considerations in understanding the foundations for childhood mental health (IOM and NRC, 2009). This relationship among stress, early development, and mental health is relevant to understanding the influences that can threaten the socioemotional well-being of younger children—and to understanding why behavior problems can undermine learning and cognitive growth. One illustration of these effects
is the high rates of preschool and prekindergarten children being expelled from their classrooms because of disruptive behavior problems—by one report at a rate more than three times the rate of children in the K-12 grades (Gilliam, 2005; see also U.S. Department of Education Office for Civil Rights, 2014). In this study, the likelihood of expulsion decreased significantly when educators were provided access to early childhood mental health consultants who could assist them in managing behavior problems.
Another illustration is reports by kindergarten teachers that social, emotional, and self-regulatory problems are a common impediment to children’s readiness to achieve in their classrooms (Lewit and Baker, 1995; Rimm-Kaufman et al., 2000). Other studies have shown that children’s conduct problems and internalizing (anxious, depressed) behavior in the classroom can undermine the development of constructive educator–child relationships and foreshadow later social and academic difficulties (Berry and O’Connor, 2010; Koles et al., 2009; Ladd and Burgess, 2001).
Consistent with the research concerning the biological and behavioral effects of chronic stress, there is increasing evidence that even very young children show clear evidence of traumatization and posttraumatic stress, anxious and depressive symptomatology, behavioral and conduct problems, and other serious psychological problems (Egger and Angold, 2006; Lieberman et al., 2011; Luby, 2006; Zeanah, 2009). Sometimes these symptom patterns overlap, such as in the comorbidity in which depressive symptomatology appears along with oppositional behavior in preschoolers (Egger and Angold, 2006). The origins of these problems are multifaceted, but certainly include interaction of environmental stresses with genetic factors that heighten or reduce children’s vulnerability to these stresses. Often these environmental stresses undermine the social support that would otherwise buffer the effects of stress on children. Diagnosing these disorders in young children is a challenge because the behaviors associated with early mental health problems in young children can be different from those observed in adults and adolescents (Egger and Emde, 2011). But progress has been made in developing reliable diagnostic criteria for preschoolers (e.g., Egger and Angold, 2006; Keenan et al., 1997; Lavigne et al., 2009) and even infants and toddlers (Zero to Three, 2005). This work provides a foundation for further study of the developmental origins of early mental health challenges and therapeutic interventions that might help these children.
Connecting the Socioemotional Health of Children and Adults
The preceding discussion makes clear that children’s socioemotional health is linked to the socioemotional well-being of the adults in their lives. Consistent with the research on the social buffering of stress discussed ear-
lier, when parents and other caregivers are managing well, they can help children cope more competently with the ordinary stresses that inevitably occur. When caregivers are stressed, by contrast, they cannot provide this buffering and are instead more often a source of stress for children. When parents are depressed, for example, they can be unpredictably sad, hostile, critical, and/or disengaged (NRC and IOM, 2009). This constellation of behaviors constitutes a difficult combination of threat and withdrawal of support for children. Young children with a depressed mother are more likely, therefore, to exhibit heightened stress reactivity to moderate challenges; to have an insecure attachment to the parent; to show lower levels of cognitive performance and, later, poorer academic achievement; and to be at greater risk of becoming depressed themselves.
The adult’s emotional well-being is important in the classroom as well. Using data from the Fragile Families and Child Wellbeing study, Jeon and colleagues (2014) measured the depressive symptomatology of 761 home- and center-based care providers, as well as overall observed classroom quality, and obtained independent measures of the behavior problems of the 3-year-olds in their classrooms. They found that educator depression was linked to higher levels of behavior problems in children, attributable to the poorer quality of the classroom environment. Notably, this study was conducted with a sample of families in economic stress, with the educators often sharing the same financial difficulties. Nevertheless, the association of educator depression with child behavior problems remained even when family influences, including maternal depression and family poverty status, were controlled for. Similar associations of educator well-being with the quality of the classroom environment and children’s learning have been found in studies of children in the early primary grades (e.g., Pianta, 1999; Pianta and Stuhlman, 2004b).
Conclusions About Chronic Stress and Adversity
Chronic stress and adversity constitute fundamental risks to learning and academic success as well as to emotional well-being for many young children. The biological and behavioral effects of stress and adversity can disrupt brain circuitry and stress response systems, affect fundamental cognitive skills, undermine focused thinking and attention, diminish self-regulation, and imperil mental and physical health. Trauma, adversity, and chronic stress can arise from many sources, such as poverty, family conflict, parental depression, abuse, neglect, or exposure to violence in the community. Supportive and stable relationships with adults can help develop children’s adaptive capacities and provide them with a significant stress buffer. It is important for adults who work with children to recognize and appreciate the effects of
adversity and to have the capacity to employ strategies for preventing or mitigating them, as well as for promoting cognitive, social, and emotional strengths for coping with adverse and stressful experiences.
Given the importance of stable and responsive relationships that provide consistent and nurturing interactions, the well-being of the adults who care for young children contributes to their healthy development and early learning.
The stresses of economic disadvantage are manifested not only in differences in children’s early experiences in the family and the community but also in the quality and stability of the out-of-home care and education families can access and afford and the quality of the schools children later attend. Socioeconomic differences in the quality of early learning opportunities place large numbers of children at a learning disadvantage and undermine their potential for academic success. These differences begin early and have a cumulative effect over time. Strengthening early learning and developing competencies requires serious and sustained attention to these socioeconomic disparities in opportunity.
AAP (American Academy of Pediattics) Committee on School Health. 2004. Soft drinks in schools. Pediatrics 113(1 Pt. 1): 152-154.
AAP Committee on Sports Medicine and Fitness and AAP Committee on School Health. 2000. Physical fitness and activity in schools. Pediatrics 105(5):1156-1157.
AAP Council on Early Childhood, P. C. High, and P. Klass. 2014. Literacy promotion: An essential component of primary care pediatric practice. Pediatrics 134(2):404-409.
AAP Council on School Health. 2013. The crucial role of recess in school. Pediatrics 131(1): 183-188.
Abu-Saad, K., and D. Fraser. 2010. Maternal nutrition and birth outcomes. Epidemiologic Reviews 32(1):5-25.
Adams, M. J. 1990. Beginning to read: Thinking and learning about print. Cambridge, MA: MIT Press.
Adler, N. E., T. Boyce, M. A. Chesney, S. Cohen, S. Folkman, R. L. Kahn, and S. L. Syme. 1994. Socioeconomic status and health. The challenge of the gradient. American Psychologist 49(1):15-24.
Ahnert, L., M. Pinquart, and M. E. Lamb. 2006. Security of children’s relationships with nonparental care providers: A meta-analysis. Child Development 77(3):664-679.
Aizer, A. 2011. Poverty, violence, and health: The impact of domestic violence during pregnancy on newborn health. Journal of Human Resources 46(3):518-538.
Aizer, A., and J. Currie. 2014. The intergenerational transmission of inequality: Maternal disadvantage and health at birth. Science 344(6186):856-861.
Alaimo, K., C. M. Olson, and E. A. Frongillo, Jr. 2001. Food insufficiency and American school-aged children’s cognitive, academic, and psychosocial development. Pediatrics 108(1):44-53.
Alexander, K. L., D. R. Entwisle, and S. L. Dauber. 1993. First-grade classroom behavior: Its short- and long-term consequences for school performance. Child Development 64(3):801-814.
Almond, D., and J. Currie. 2011. Killing me softly: The fetal origins hypothesis. Journal of Economic Perspectives 25(3):153-172.
Almond, D., and B. Mazumder. 2011. Health capital and the prenatal environment: The effect of Ramadan observance during pregnancy. American Economic Journal: Applied Economics 3(4):56-85.
Aloise-Young, P. A. 1993. The development of self-presentation: Self-promotion in 6- to 10-year-old children. Social Cognition 11(2):201-222.
Anghel, D. 2010. Executive function in preschool children: Working memory as a predictor of mathematical ability at school age. Revista Romaneasca pentru Educatie Multidimensionala 2(4):5-16.
Ansari, D., N. Garcia, E. Lucas, K. Hamon, and B. Dhital. 2005. Neural correlates of symbolic number processing in children and adults. Neuroreport 16:1769-1775.
Antell, S. E., and D. P. Keating. 1983. Perception of numerical invariance in neonates. Child Development 54:695-701.
Anthony, J. L., C. J. Lonigan, K. Driscoll, B. M. Phillips, and S. R. Burgess. 2003. Phonological sensitivity: A quasi-parallel progression of word structure units and cognitive operations. Reading Research Quarterly 38(4):470-487.
Ashcraft, M. H. 2006. Math performance, working memory, and math anxiety; some possible directions for neural functioning work. Paper read at The Neural Basis of Mathematical Development, November, Nashville, TN.
Ashkenazi, S., N. Mark-Zigdon, and A. Henik. 2013. Do subitizing deficits in developmental dyscalculia involve pattern recognition weakness? Developmental Science 16(1):35-46.
Au, T. K., C. K. Chan, T. K. Chan, M. W. Cheung, J. Y. Ho, and G. W. Ip. 2008. Folkbiology meets microbiology: A study of conceptual and behavioral change. Cognitive Psychology 57(1):1-19.
Aukrust, V. G. 2007. Young children acquiring second language vocabulary in preschool group-time: Does amount, diversity, and discourse complexity of teacher talk matter? Journal of Research in Childhood Education 22(1):17-37.
Aunola, K., E. Leskinen, M.-K. Lerkkanen, and J.-E. Nurmi. 2004. Developmental dynamics of math performance from pre-school to grade 2. Journal of Educational Psychology 96:699-713.
Baillargeon, R., D. Wu, S. Yuan, J. Li, and Y. Luo. 2009. Young infants’ expectations about self-propelled objects. In The origins of object knowledge, edited by B. M. Hood and L. Santos. Oxford, UK: Oxford University Press.
Baillargeon, R., R. M. Scott, and Z. He. 2010. False-belief understanding in infants. Trends in Cognitive Sciences 14(3):110-118.
Baker, D., H. Knipe, J. Collins, J. Leon, E. Cummings, C. Blair, and D. Gramson. 2010. One hundred years of elementary school mathematics in the United States: A content analysis and cognitive assessment of textbooks from 1900 to 2000. Journal for Research in Mathematics Education 41(4):383-423.
Baldwin, D. A. 1991. Infants’ contribution to the achievement of joint reference. Child Development 62(5):875-890.
Baldwin, D. A., and L. J. Moses. 2001. Links between social understanding and early word learning: Challenges to current accounts. Social Development 10(3):309-329.
Baldwin, D. A., and M. Tomasello. 1998. Word learning: A window on early pragmatic understanding. In The Proceedings of the Twenty-ninth Annual Child Language Research Forum. Vol. 29, edited by E. V. Clark. Chicago, IL: University of Chicago Press. Pp. 3-24.
Ball, E. W., and B. A. Blachman. 1991. Does phoneme awareness training in kindergarten make a difference in early word recognition and developmental spelling? Reading Research Quarterly 26(1):49-66.
Barata, M. C. 2010. Executive functions in Chilean preschool children: Investigating the associations of early executive functions with emergent mathematics and literacy skills. PhD diss., Harvard Graduate School of Education.
Barkley, R. A. 1997. Attention-deficit/hyperactivity disorder, self-regulation, and time: Toward a more comprehensive theory. Journal of Developmental and Behavioral Pediatrics 18(4):271-279.
Baroody, A. E., and J. Dobbs-Oates. 2009. Child and parent characteristics, parental expectations, and child behaviours related to preschool children’s interest in literacy. Early Child Development and Care 181(3):345-359.
Baroody, A. J. 2004. The developmental bases for early childhood number and operations standards. In Engaging young children in mathematics: Standards for early childhood mathematics education, edited by D. H. Clements, J. Sarama, and A.-M. DiBiase. Mahwah, NJ: Lawrence Erlbaum Associates. Pp. 173-219.
Baroody, A. J., M.-L. Lai, and K. S. Mix. 2005. Changing views of young children’s numerical and arithmetic competencies. Paper read at National Association for the Education of Young Children, December, Washington, DC.
———. 2006. The development of young children’s number and operation sense and its implications for early childhood education. In Handbook of research on the education of young children, edited by B. Spodek and O. N. Saracho. Mahwah, NJ: Lawrence Erlbaum Associates. Pp. 187-221.
Baroody, A. J., X. Li, and M.-l. Lai. 2008. Toddlers’ spontaneous attention to number. Mathematical Thinking and Learning 10:240-270.
Bartsch, K., and H. M. Wellman. 1995. Children talk about the mind. New York: Oxford University Press.
Basch, C. E. 2011. Breakfast and the achievement gap among urban minority youth. Journal of School Health 81(10):635-640.
Bascoe, S. M., P. T. Davies, M. L. Sturge-Apple, and E. M. Cummings. 2009. Children’s representations of family relationships, peer information processing, and school adjustment. Developmental Psychology 45(6):1740-1751.
Bassett, H. H., S. Denham, T. M. Wyatt, and H. K. Warren-Khot. 2012. Refining the preschool self-regulation assessment for use in preschool classrooms. Infant and Child Development 21(6):596-616.
Bassok, D., and A. Rorem. 2014. Is kindergarten the new first grade? The changing nature of kindergarten in the age of accountability. Charlottesville, VA: University of Virginia.
Beilock, S. L. 2001. Learning and performing math: Self-concept, self-doubt, and self-fulfilling prophesy. Journal of Experimental Psychology: General 130:224-237.
Belsky, D. W., T. E. Moffitt, L. Arseneault, M. Melchior, and A. Caspi. 2010. Context and sequelae of food insecurity in children’s development. American Journal of Epidemiology 172(7):809-818.
Bennett, N., C. Desforges, A. Cockburn, and B. Wilkinson. 1984. The quality of pupil learning experiences. Hillsdale, NJ: Lawrence Erlbaum Associates.
Berch, D. B., and M. M. M. Mazzocco, eds. 2007. Why is math so hard for some children? The nature and origins of mathematical learning difficulties and disabilities. Baltimore, MD: Paul H. Brookes Publishing Co.
Berry, D., and E. O’Connor. 2010. Behavioral risk, teacher–child relationships, and social skill development across middle childhood: A child-by-environment analysis of change. Journal of Applied Developmental Psychology 31(1):1-14.
Best, J. R., P. H. Miller, and J. A. Naglieri. 2011. Relations between executive function and academic achievement from ages 5 to 17 in a large, representative national sample. Learning and Individual Differences 21(4):327-336.
Bharadwaj, P., J. V. Johnsen, and K. V. Løken. 2012. Smoking bans, maternal smoking and birth outcomes. IZA Institute for the Study of Labor Discussion Paper No. 7006:72-93.
Bharadwaj, P., J. Eberhard, and C. Neilson. 2013 (unpublished). Health at birth, parental investments and academic outcomes. San Diego: University of California, San Diego.
Bialystok, E. 2011. Reshaping the mind: The benefits of bilingualism. Canadian Journal of Experimental Psychology 65(4):229-235.
Bialystok, E., and F. I. M. Craik. 2010. Cognitive and linguistic processing in the bilingual mind. Current Directions in Psychological Science 19(1):19-23.
Bialystok, E., F. I. M. Craik, D. W. Green, and T. H. Gollan. 2009. Bilingual minds. Psychological Science in the Public Interest 10(3):89-129.
Biederman, J., M. C. Monuteaux, A. E. Doyle, L. J. Seidman, T. E. Wilens, F. Ferrero, C. L. Morgan, and S. V. Faraone. 2004. Impact of executive function deficits and attention-deficit/hyperactivity disorder (ADHD) on academic outcomes in children. Journal of Consulting and Clinical Psychology 72(5):757-766.
Bielaczyc, K., P. L. Pirolli, and A. L. Brown. 1995. Training in self-explanation and self-regulation strategies: Investigating the effects of knowledge acquisition activities on problem solving. Cognition and Instruction 13:221-252.
Bierman, K. L., R. L. Nix, M. T. Greenberg, C. Blair, and C. E. Domitrovich. 2008a. Executive functions and school readiness intervention: Impact, moderation, and mediation in the Head Start REDI program. Development and Psychopathology 20(3):821-843.
Bierman, K. L., C. E. Domitrovich, R. L. Nix, S. D. Gest, J. A. Welsh, M. T. Greenberg, C. Blair, K. E. Nelson, and S. Gill. 2008b. Promoting academic and social-emotional school readiness: The Head Start REDI program. Child Development 79(6):1802-1817.
Birch, S. H., and G. W. Ladd. 1997. The teacher–child relationship and children’s early school adjustment. Journal of School Psychology 35(1):61-79.
———. 1998. Children’s interpersonal behaviors and the teacher–child relationship. Developmental Psychology 34(5):934-946.
Blair, C. 2002. School readiness: Integrating cognition and emotion in a neurobiological conceptualization of children’s functioning at school entry. American Psychologist 57(2): 111-127.
Blair, C., and A. Diamond. 2008. Biological processes in prevention and intervention: The promotion of self-regulation as a means of preventing school failure. Development and Psychopathology 20(3):899-911.
Blair, C., and C. C. Raver. 2012. Child development in the context of adversity: Experiential canalization of brain and behavior. American Psychologist 67(4):309-318.
Blair, C., and R. P. Razza. 2007. Relating effortful control, executive function, and false belief understanding to emerging math and literacy ability in kindergarten. Child Development 78:647-663.
Blair, C., J. Protzko, and A. Ursache. 2010. Self-regulation and early literacy. In Handbook of early literacy research. Vol. 3, edited by S. B. Neuman and D. K. Dickinson. New York: Guilford Press. Pp. 20-35.
Blevins-Knabe, B., and L. Musun-Miller. 1996. Number use at home by children and their parents and its relationship to early mathematical performance. Early Development and Parenting 5:35-45.
Bloom, P. 2013. Just babies: The origins of good and evil. New York: Crown Publishers.
Bodovski, K., and G. Farkas. 2007. Mathematics growth in early elementary school: The roles of beginning knowledge, student engagement, and instruction. Elementary School Journal 108(2):115-130.
Bodrova, E., and D. J. Leong. 2012. Scaffolding self-regulated learning in young children: Lessons from tools of the mind. In Handbook of early childhood education, edited by R. C. Pianta, W. S. Barnett, L. M. Justice, and S. M. Sheridan. New York: Guilford Press. Pp. 352-369.
Bougma, K., F. E. Aboud, K. B. Harding, and G. S. Marquis. 2013. Iodine and mental development of children 5 years old and under: A systematic review and meta-analysis. Nutrients 5(4):1384-1416.
Bowers, E. P., and M. Vasilyeva. 2011. The relation between teacher input and lexical growth of preschoolers. Applied Psycholinguistics 32(1):221-241.
Boyce, W. T., M. B. Sokolowski, and G. E. Robinson. 2012. Toward a new biology of social adversity. Proceedings of the National Academy of Sciences of the United States of America 109(Suppl. 2):17143-17148.
Bradley, L., and P. E. Bryant. 1983. Categorizing sounds and learning to read—a causal connection. Nature 301(5899):419-421.
Bradley, R. H., and R. F. Corwyn. 2002. Socioeconomic status and child development. Annual Review of Psychology 53:371-399.
Brennan, L. M., E. C. Shelleby, D. S. Shaw, F. Gardner, T. J. Dishion, and M. Wilson. 2013. Indirect effects of the family check-up on school-age academic achievement through improvements in parenting in early childhood. Journal of Educational Psychology 105(3): 10.1037/a0032096.
Broidy, L. M., D. S. Nagin, R. E. Tremblay, B. Brame, K. A. Dodge, D. Fergusson, J. Horwood, R. Loeber, R. Laird, D. Lyname, T. F. Moffit, J. E. Bates, G. S. Pettit, and F. Vitaro. 2003. Developmental trajectories of childhood disruptive behaviors and adolescent delinquency: A six-site, cross-national study. Developmental Psychology 30(2):222-245.
Brosnan, M., J. Demetre, S. Hamill, K. Robson, H. Shepherd, and G. Cody. 2002. Executive functioning in adults and children with developmental dyslexia. Neuropsychologia 40(12):2144-2155.
Bruner, J. 1978. The role of dialogue in language acquisition. In The child’s conception of language, edited by A. Sinclair, R. Jarvella, and W. J. M. Levelt. New York: Springer. Pp. 241-256.
Bryan, J., S. Osendarp, D. Hughes, E. Calvaresi, K. Baghurst, and J. W. van Klinken. 2004. Nutrients for cognitive development in school-aged children. Nutrition Reviews 62(8): 295-306.
Bühler, E., C. Bachmann, H. Goyert, M. Heinzel-Gutenbrunner, and I. Kamp-Becker. 2011. Differential diagnosis of autism spectrum disorder and attention deficit hyperactivity disorder by means of inhibitory control and “theory of mind.” Journal of Autism and Developmental Disorders 41:1718-1726.
Buhs, E. S., and G. W. Ladd. 2001. Peer rejection as an antecedent of young children’s school adjustment: An examination of mediating processes. Developmental Psychology 37(4):550-560.
Bull, R., and G. Scerif. 2001. Executive functioning as a predictor of children’s mathematics ability: Inhibition, switching, and working memory. Developmental Neuropsychology 19(3):273-293.
Bull, R., R. S. Johnston, and J. A. Roy. 1999. Exploring the roles of the visual-spatial sketch pad and central executive in children’s arithmetical skills: Views from cognition and developmental neuropsychology. Developmental Neuropsychology 15(3):421-442.
Bull, R., K. A. Espy, and S. A. Wiebe. 2008. Short-term memory, working memory, and executive functioning in preschoolers: Longitudinal predictors of mathematical achievement at age 7 years. Developmental Neuropsychology 33:205-228.
Busch-Rossnagel, N. A. 2005. Mastery motivation, preschool and early childhood. In Encyclopedia of applied developmental science, edited by C. Fisher and R. Lerner. Thousand Oaks, CA: Sage Publications, Inc. Pp. 679-681.
Butler, L. P., and E. M. Markman. 2012a. Finding the cause: Verbal framing helps children extract causal evidence embedded in a complex scene. Journal of Cognition and Development 13(1):38-66.
———. 2012b. Preschoolers use intentional and pedagogical cues to guide inductive inferences and exploration. Child Development 83(4):1416-1428.
———. 2014. Preschoolers use pedagogical cues to guide radical reorganization of category knowledge. Cognition 130(1):116-127.
Butterworth, B. 2005. The development of arithmetical abilities. Journal of Child Psychology and Psychiatry 46:3-18.
———. 2010. Foundational numerical capacities and the origins of dyscalculia. Trends in Cognitive Sciences 14:534-541.
Butterworth, B., S. Varma, and D. Laurillard. 2011. Dyscalculia: From brain to education. Science 332:1049-1053.
Byrne, B., and R. Fielding-Barnsley. 1989. Phonemic awareness and letter knowledge in the child’s acquisition of the alphabetic principle. Journal of Educational Psychology 81(3):313-321.
———. 1995. Evaluation of a program to teach phonemic awareness to young children: A 2- and 3-year follow-up and a new preschool trial. Journal of Educational Psychology 87:488-503.
Byrnes, J. P., and B. A. Wasik. 2009. Factors predictive of mathematics achievement in kindergarten, first and third grades: An opportunity–propensity analysis. Contemporary Educational Psychology 34:167-183.
Cameron, C. E., L. L. Brock, W. M. Murrah, L. H. Bell, S. L. Worzalla, D. Grissmer, and F. J. Morrison. 2012. Fine motor skills and executive function both contribute to kindergarten achievement. Child Development 83(4):1229-1244.
Campbell, F., G. Conti, J. J. Heckman, S. H. Moon, R. Pinto, E. Pungello, and Y. Pan. 2014. Early childhood investments substantially boost adult health. Science 343(6178): 1478-1485.
Canadian Paediatric Society. 2004. Maternal depression and child development. Paediatrics & Child Health 9(8):575-583.
Cantlon, J. F., E. M. Brannon, E. J. Carter, and K. A. Pelphrey. 2006. Functional imaging of numerical processing in adults and 4-y-old children. PLoS Biology 4(5):e125.
Carey, S. 2009. The origin of concepts. Oxford and New York: Oxford University Press.
Carneiro, P., and R. Ginja. 2012. Long term impacts of compensatory preschool on health and behavior: Evidence from Head Start, IZA Discussion Paper No. 6315. Bonn, Germany: Institute for the Study of Labor.
Cartwright, K. B. 2002. Cognitive development and reading: The relation of reading-specific multiple classification skill to reading comprehension in elementary school children. Journal of Educational Psychology 94(1):56-63.
———. 2008. Cognitive flexibility and reading comprehension: Relevance to the future. In Comprehension instruction: Research-based best practices, 2nd ed., edited by C. C. Block and S. R. Parris. New York: Guilford Press. Pp. 50-64.
Cartwright, K. B., T. R. Marshall, K. L. Dandy, and M. C. Isaac. 2010. The development of graphophonological-semantic cognitive flexibility and its contribution to reading comprehension in beginning readers. Journal of Cognition and Development 11(1):61-85.
Cassel, J. 1976. The contribution of the social environment to host resistance: The fourth Wade Hampton Frost lecture. American Journal of Epidemiology 104(2):107-123.
Catts, H. W., and A. G. Kamhi. 1999. Language and reading disabilities. Boston, MA: Allyn and Bacon.
Catts, H. W., M. E. Fey, X. Zhang, and J. B. Tomblin. 1999. Language basis of reading and reading disabilities: Evidence from a longitudinal investigation. Scientific Studies of Reading 3(4):331-361.
CDC (Centers for Disease Control and Prevention). 2011. School health guidelines to promote healthy eating and physical activity. Morbidity and Mortality Weekly Report 60(RR05):1-71.
———. 2012. Youth risk behavior surveillance—United States, 2011. Morbidity and Mortality Weekly Report: Surveillance Summaries 61(4):1-162.
———. 2014. Middle childhood (6-8 years of age): Developmental milestones. http://www.cdc.gov/ncbddd/childdevelopment/positiveparenting/middle.html (accessed September 15, 2014).
Celedón-Pattichis, S., S. I. Musanti, and M. E. Marshall. 2010. Bilingual elementary teachers’ reflections on using students’ native language and culture to teach mathematics. In Mathematics teaching & learning in K-12: Equity and professional development, edited by M. Q. Foote. New York: Palgrave Macmillan. Pp. 7-24.
Center on the Developing Child at Harvard University. 2010. The foundations of lifelong health are built in early childhood. http://www.developingchild.harvard.edu (accessed January 22, 2015).
Champagne, D. L., R. C. Bagot, F. van Hasselt, G. Ramakers, M. J. Meaney, E. R. de Kloet, M. Joels, and H. Krugers. 2008. Maternal care and hippocampal plasticity: Evidence for experience-dependent structural plasticity, altered synaptic functioning, and differential responsiveness to glucocorticoids and stress. Journal of Neuroscience 28(23):6037-6045.
Chase-Lansdale, L., and J. Brooks-Gunn. 2014. Two-generation programs in the twenty-first century. Future of Children 24(1):13-39.
Chi, M. T. H., and D. Klahr. 1975. Span and rate of apprehension in children and adults. Journal of Experimental Child Psychology 19:434-439.
Child Trends. 2015. Homeless children and youth. http://www.childtrends.org/?indicators=homeless-children-and-youth (accessed January 27, 2015).
Children’s Bureau. 2013. Child maltreatment 2012. http://www.acf.hhs.gov/programs/cb/research-data-technology/statistics-research/child-maltreatment (accessed January 27, 2015).
Chu, F. W., K. Vanmarle, and D. C. Geary. 2013. Quantitative deficits of preschool children at risk for mathematical learning disability. Frontiers in Psychology 4:195.
Cimpian, A. 2010. The impact of generic language about ability on children’s achievement motivation. Developmental Psychology 46(5):1333-1340.
Cimpian, A., and E. M. Markman. 2009. Information learned from generic language becomes central to children’s biological concepts: Evidence from their open-ended explanations. Cognition 113(1):14-25.
———. 2011. The generic/nongeneric distinction influences how children interpret new information about social others. Child Development 82(2):471-492.
Cimpian, A., H. C. Arce, E. M. Markman, and C. S. Dweck. 2007. Subtle linguistic cues affect children’s motivation. Psychological Science 18(4):314-316.
Claessens, A., M. Engel, and F. C. Curran. 2014. Academic content, student learning, and the persistence of preschool effects. American Educational Research Journal 51(2):403-434.
Clarke, B. A., D. M. Clarke, and J. Cheeseman. 2006. The mathematical knowledge and understanding young children bring to school. Mathematics Education Research Journal 18(1):81-107.
Clements, D. H. 1999. Subitizing: What is it? Why teach it? Teaching Children Mathematics 5:400-405.
Clements, D. H., and J. Sarama. 2004. Learning trajectories in mathematics education. Mathematical Thinking & Learning 6(2):81-89.
———. 2008. Experimental evaluation of the effects of a research-based preschool mathematics curriculum. American Educational Research Journal 45:443-494.
———. 2009. Learning and teaching early math: The learning trajectories approach. New York: Routledge.
———. 2012. Learning and teaching early and elementary mathematics. In Instructional strategies for improving students’ learning: Focus on early reading and mathematics, edited by J. S. Carlson and J. R. Levin. Charlotte, NC: Information Age Publishing.
———. 2014. Learning and teaching early math: The learning trajectories approach, 2nd ed. New York: Routledge.
Clements, D. H., S. Swaminathan, M. A. Z. Hannibal, and J. Sarama. 1999. Young children’s concepts of shape. Journal for Research in Mathematics Education 30:192-212.
Clements, D. H., J. Sarama, M. E. Spitler, A. A. Lange, and C. B. Wolfe. 2011. Mathematics learned by young children in an intervention based on learning trajectories: A large-scale cluster randomized trial. Journal for Research in Mathematics Education 42(2):127-166.
Clements, D. H., A. J. Baroody, and J. Sarama. 2013a (unpublished). Background research on early mathematics. National Governor’s Association (NGA) Center Project on Early Mathematics.
Clements, D. H., J. Sarama, C. B. Wolfe, and M. E. Spitler. 2013b. Longitudinal evaluation of a scale-up model for teaching mathematics with trajectories and technologies: Persistence of effects in the third year. American Educational Research Journal 50(4):812-850.
Cobb, S. 1976. Social support as a moderator of life stress. Psychosomatic Medicine 38(5): 300-314.
Colman, S., I. P. Nichols-Barrer, J. E. Redline, B. L. Devaney, S. V. Ansell, and T. Joyce. 2012. Effects of the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC): A review of recent research (summary). Alexandria, VA: U.S. Department of Agriculture, Food and Nutrition Service, Office of Research and Analysis.
Connor, C. M., F. J. Morrison, and L. Slominski. 2006. Preschool instruction and children’s emergent literacy growth. Journal of Educational Psychology 98(4):665-689.
Connor, C. M., F. J. Morrison, and P. S. Underwood. 2007. A second chance in second grade: The independent and cumulative impact of first- and second-grade reading instruction and students’ letter-word reading skill growth. Scientific Studies of Reading 11(3):199-233.
Connor, C. M., P. A. Alberto, D. L. Compton, and R. E. O’Connor. 2014. Improving reading outcomes for students with or at risk for reading disabilities: A synthesis of the contributions from the Institute of Education Sciences Research Centers. NCSER 2014-3000. Washington, DC: National Center for Special Education Research.
Conway, A. R. A., M. J. Kane, and R. W. Engle. 2003. Working memory capacity and its relation to general intelligence. Trends in Cognitive Sciences 7(12):547-552.
Cook, J. T., and D. A. Frank. 2008. Food security, poverty, and human development in the United States. Annals of the New York Academy of Sciences 1136(1):193-209.
Cooper, D. H., F. P. Roth, D. L. Speece, and C. Schatschneider. 2002. The contribution of oral language skills to the development of phonological awareness. Applied Psycholinguistics 23:399-416.
Cooper, S. B., S. Bandelow, M. L. Nute, J. G. Morris, and M. E. Nevill. 2012. Breakfast glycaemic index and cognitive function in adolescent school children. British Journal of Nutrition 107(12):1823-1832.
Copple, C., S. Bredekamp, D. G. Koralek, and K. Charner. 2013. Developmentally appropriate practice. Focus on preschoolers. Washington, DC: National Association for the Education of Young Children.
Coyne, M. D., D. B. McCoach, S. Loftus, R. Zipoli, Jr., and S. Kapp. 2009. Direct vocabulary instruction in kindergarten: Teaching for breadth versus depth. Elementary School Journal 110(1):1-18.
Csibra, G. 2010. Recognizing communicative intentions in infancy. Mind & Language 25(2): 141-168.
Csibra, G., and G. Gergely. 2009. Natural pedagogy. Trends in Cognitive Sciences 13(4): 148-153.
Cunha, F., and J. J. Heckman. 2010. Investing in our young people. Working paper 1620. Cambridge, MA: National Bureau of Economic Research.
Cunningham, A. E., and J. Zibulsky. 2014. Book smart: How to develop and support successful, motivated readers. New York: Oxford University Press.
Currie, J. 2011. Inequality at birth: Some causes and consequences. American Economic Review 101(3):1-22.
Currie, J., and M. Rossin-Slater. 2013. Weathering the storm: Hurricanes and birth outcomes. Journal of Health Economics 32(3):487-503.
Currie, J., and W. R. Walker. 2009. Traffic congestion and infant health: Evidence from E-ZPass. National Bureau of Economic Research Working Paper Series No. 15413.
Currie, J., M. Neidell, and J. F. Schmieder. 2009. Air pollution and infant health: Lessons from New Jersey. Journal of Health Economics 28(3):688-703.
Currie, J., J. Graff Zivin, K. Meckel, M. Neidell, and W. Schlenker. 2013. Something in the water: Contaminated drinking water and infant health. Canadian Journal of Economics 46(3):791-810.
Danese, A., and B. S. McEwen. 2012. Adverse childhood experiences, allostasis, allostatic load, and age-related disease. Physiology & Behavior 106(1):29-39.
Davies, P. T., M. J. Woitach, M. A. Winter, and E. M. Cummings. 2008. Children’s insecure representations of the interparental relationship and their school adjustment: The mediating role of attention difficulties. Child Development 79(5):1570-1582.
de Bruin, A., B. Treccani, and S. Della Sala. 2015. Cognitive advantage in bilingualism: An example of publication bias? Psychological Science 26(1):99-107.
de Ruiter, C., and M. H. van IJzendoorn. 1993. Attachment and cognition: A review of the literature. International Journal of Educational Research (19):525-540.
Dearing, E., K. McCartney, and B. A. Taylor. 2001. Change in family income-to-needs matters more for children with less. Child Development 72(6):1779-1793.
Denham, S. A., and C. Brown. 2010. “Plays nice with others”: Social–emotional learning and academic success. Early Education and Development 21(5):652-680.
Denton, K., and J. West. 2002. Children’s reading and mathematics achievement in kindergarten and first grade. Washington, DC: U.S. Department of Education, National Center for Education Statistics.
Derryberry, D., and M. Reed. 1996. Regulatory processes and the development of cognitive representations. Development and Psychopathology 8(1):215-234.
Deschenes, O., M. Greenstone, and J. Guryan. 2009. Climate change and birth weight. American Economic Review 99(2):211-217.
DeYoung, C. G. 2011. Intelligence and personality. In The Cambridge handbook of intelligence, edited by R. J. Sternberg and S. B. Kaufman. Cambridge, MA, and New York: Cambridge University Press. Pp. 711-737.
Diamond, A. 1991. Neuropsychological insights into the meaning of object concept development. In The epigenesis of mind: Essays on biology and cognition, edited by S. E. Carey and R. Gelman. Hillsdale, NJ: Lawrence Erlbaum Associates. Pp. 67-110.
Diamond, A., and K. Lee. 2011. Interventions shown to aid executive function development in children 4 to 12 years old. Science 333(6045):959-964.
Diamond, K. E., L. M. Justice, R. S. Siegler, and P. A. Snyder. 2013. Synthesis of IES research on early intervention and early childhood education. Washington, DC: National Center for Special Education Research, Institute of Education Sciences, U.S. Department of Education.
Dickinson, D. K. 2003. Why we must improve teacher–child conversations in preschools and the promise of professional development. In Enhancing caregiver language facilitation in childcare settings, edited by L. Girolametto and E. Weitzman. Toronto: The Hanen Institute. Pp. 41-48.
Dickinson, D. K., and J. Freiberg. 2009. Environmental factors affecting language acquisition from birth–five: Implications for literacy development and intervention efforts. Paper presented at Workshop on the Role of Language in School Learning: Implications for Closing the Achievement Gap, Hewlett Foundation, Menlo Park, CA.
Dickinson, D. K., and M. V. Porche. 2011. Relation between language experiences in preschool classrooms and children’s kindergarten and fourth-grade language and reading abilities. Child Development 82(3):870-886.
Dickinson, D. K., and M. W. Smith. 1991. Preschool talk: Patterns of teacher–child interaction in early childhood classrooms. Journal of Research in Childhood Education 6(1):20-29.
Dickinson, D. K., and P. O. Tabors. 2001. Beginning literacy with language: Young children learning at home and school. Baltimore, MD: Paul H. Brookes Publishing Co.
Dickinson, D. K., C. L. Darrow, and T. A. Tinubu. 2008. Patterns of teacher–child conversations in Head Start classrooms: Implications for an empirically grounded approach to professional development. Early Education and Development 19(3):396-429.
Doig, B., B. McCrae, and K. Rowe. 2003. A good start to numeracy: Effective numeracy strategies from research and practice in early childhood. Canberra ACT, Australia: Australian Council for Educational Research.
Dombek, J. L., and C. M. Connor. 2012. Preventing retention: First grade classroom instruction and student characteristics. Psychology in the Schools 49(6):568-588.
Domitrovich, C. E., J. E. Moore, R. A. Thompson, and the CASEL Preschool to Elementary School Social and Emotional Learning Assessment Workgroup. 2012. Interventions that promote social-emotional learning in young children. In Handbook of early childhood education, edited by R. C. Pianta, W. S. Barnett, L. M. Justice, and S. M. Sheridan. New York: Guilford Press. Pp. 393-415.
Dozier, M., E. Peloso, O. Lindhiem, M. K. Gordon, M. Manni, S. Sepulveda, J. Ackerman, A. Bernier, and S. Levine. 2006. Developing evidence-based interventions for foster children: An example of a randomized clinical trial with infants and toddlers. Journal of Social Issues 62(4):767-785.
Dozier, M., E. Peloso, E. Lewis, J. P. Laurenceau, and S. Levine. 2008. Effects of an attachment-based intervention on the cortisol production of infants and toddlers in foster care. Development and Psychopathology 20(3):845-859.
Drake, K., J. Belsky, and R. M. Fearon. 2014. From early attachment to engagement with learning in school: The role of self-regulation and persistence. Developmental Psychology 50(5):1350-1361.
Duke, N. K., and M. K. Block. 2012. Improving reading in the primary grades. The Future of Children 22(2):55-72.
Duncan, G. J., and K. Magnuson. 2011. The nature and impact of early achievement skills, attention skills, and behavior problems. In Whither opportunity? Rising inequality and the uncertain life chances of low-income children, edited by G. J. Duncan and R. Murnane. New York: Russell Sage Press. Pp. 47-70.
Duncan, G. J., A. Claessens, and M. Engel. 2005. The contributions of hard skills and socioemotional behavior to school readiness. http://www.ipr.northwestern.edu/publications/docs/workingpapers/2005/IPR-WP-05-01.pdf (accessed January 20, 2015).
Duncan, G. J., C. J. Dowsett, A. Claessens, K. Magnuson, A. C. Huston, P. Klebanov, L. Pagani, L. Feinstein, M. Engel, J. Brooks-Gunn, H. Sexton, K. Duckworth, and C. Japel. 2007. School readiness and later achievement. Developmental Psychology 43(6):1428-1446.
Dunlap, G., P. S. Strain, L. Fox, J. J. Carta, M. Conroy, B. J. Smith, L. Kern, M. L. Hemmeter, M. A. Timm, A. McCart, W. Sailor, U. Markey, D. J. Markey, S. Lardieri, and C. Sowell. 2006. Prevention and intervention with young children’s challenging behavior: Perspectives regarding current knowledge. Behavioral Disorders 32(1):29-45.
Dunn, J. 2002. Mindreading, emotion understanding, and relationships. In Growing points in developmental science: An introduction, edited by W. W. Hartup and R. K. Silbereisen. Hove, NY: Psychology Press. Pp. 167-176.
Edens, K. M., and E. F. Potter. 2013. An exploratory look at the relationships among math skills, motivational factors and activity choice. Early Childhood Education Journal 41(3):235-243.
Egger, H. L., and A. Angold. 2006. Common emotional and behavioral disorders in preschool children: Presentation, nosology, and epidemiology. Journal of Child Psychology and Psychiatry and Allied Disciplines 47(3-4):313-337.
Egger, H. L., and R. N. Emde. 2011. Developmentally sensitive diagnostic criteria for mental health disorders in early childhood: The Diagnostic and Statistical Manual of Mental Disorders-IV, the research diagnostic criteria-preschool age, and the diagnostic classification of mental health and developmental disorders of infancy and early childhood-revised. American Psychologist 66(2):95-106.
Ehri, L. C. 2005. Learning to read words: Theory, findings, and issues. Scientific Studies of Reading 9(2):167-188.
Eimeren, L. V., K. D. MacMillan, and D. Ansari. 2007. The role of subitizing in children’s development of verbal counting. Paper read at Society for Research in Child Development, April, Boston, MA.
Eisenberg, N., C. Valiente, and N. D. Eggum. 2010. Self-regulation and school readiness. Early Education and Development 21(5):681-698.
Ellis, R. 2008. The study of second language acquisition. Oxford and New York: Oxford University Press.
Englund, M. M., A. E. Luckner, G. J. L. Whaley, and B. Egeland. 2004. Children’s achievement in early elementary school: Longitudinal effects of parental involvement, expectations, and quality of assistance. Journal of Educational Psychology 96(4):723-730.
Ericsson, K. A., R. T. Krampe, and C. Tesch-Römer. 1993. The role of deliberate practice in the acquisition of expert performance. Psychological Review 100:363-406.
ERS (Economic Research Service). 2014. Definitions of food security. http://www.ers.usda.gov/topics/food-nutrition-assistance/food-security-in-the-us/definitions-of-food-security.aspx (accessed March 23, 2015).
Espada, J. P. 2012. The native language in teaching kindergarten mathematics. Journal of International Education Research 8(4):359-366.
Espinosa, L. M. 2005. Curriculum and assessment considerations for young children from culturally, linguistically, and economically diverse backgrounds. Psychology in the Schools 42(8):837-853.
Evans, G. W., and P. Kim. 2013. Childhood poverty, chronic stress, self-regulation, and coping. Child Development Perspectives 7(1):43-48.
Evans, G. W., E. Chen, G. Miller, and T. Seeman. 2012. How poverty gets under the skin: A life-course perspective. In The Oxford handbook of poverty and child development, edited by V. Maholmes and R. B. King. New York: Oxford University Press. Pp. 13-36.
Fahie, C. M., and D. K. Symons. 2003. Executive functioning and theory of mind in children clinically referred for attention and behavior problems. Applied Developmental Psychology 24:51-73.
Farah, M. J., L. Betancourt, D. M. Shera, J. H. Savage, J. M. Giannetta, N. L. Brodsky, E. K. Malmud, and H. Hurt. 2008. Environmental stimulation, parental nurturance and cognitive development in humans. Developmental Science 11(5):793-801.
Farran, D. C., C. Aydogan, S. J. Kang, and M. Lipsey. 2005. Preschool classroom environments and the quantity and quality of children’s literacy and language behaviors. In Handbook of early literacy research, edited by D. Dickinson and S. Neuman. New York: Guilford Press. Pp. 257-268.
Farran, D. C., M. W. Lipsey, B. Watson, and S. Hurley. 2007. Balance of content emphasis and child content engagement in an early reading first program. Paper presented at American Educational Research Association, April, Chicago, IL.
Feigenson, L., M. E. Libertus, and J. Halberda. 2013. Links between the intuitive sense of number and formal mathematics ability. Child Development Perspectives 7(2):74-79.
Fernald, A., V. A. Marchman, and A. Weisleder. 2013. SES differences in language processing skill and vocabulary are evident at 18 months. Developmental Science 16(2):234-248.
Field, E., O. Robles, and M. Torero. 2009. Iodine deficiency and schooling attainment in Tanzania. American Economic Journal: Applied Economics 1(4):140-169.
Figlio, D., J. Guryan, K. Karbownik, and J. Roth. 2014. The effects of poor neonatal health on children’s cognitive development? American Economic Review 104(12):4205-4230.
Fisher, P. A., M. Stoolmiller, M. R. Gunnar, and B. O. Burraston. 2007. Effects of a therapeutic intervention for foster preschoolers on diurnal cortisol activity. Psychoneuroendocrinology 32(8-10):892-905.
Fisher, P. A., M. J. Van Ryzin, and M. R. Gunnar. 2011. Mitigating HPA axis dysregulation associated with placement changes in foster care. Psychoneuroendocrinology 36(4):531-539.
Fivush, R., C. A. Haden, and E. Reese. 2006. Elaborating on elaborations: Role of maternal reminiscing style in cognitive and socioemotional development. Child Development 77(6):1568-1588.
Florence, M. D., M. Asbridge, and P. J. Veugelers. 2008. Diet quality and academic performance. Journal of School Health 78(4):209-215; quiz 239-241.
Furrow, D., K. Nelson, and H. Benedict. 1979. Mothers’ speech to children and syntactic development: Some simple relationships. Journal of Child Language 6(03):423-442.
Fuson, K. C. 1988. Children’s counting and concepts of number. New York: Springer-Verlag.
———. 1992. Research on learning and teaching addition and subtraction of whole numbers. In Handbook of research on mathematics teaching and learning, edited by G. Leinhardt, R. Putman, and R. A. Hattrup. Mahwah, NJ: Lawrence Erlbaum Associates. Pp. 53-187.
———. 2004. Pre-K to grade 2 goals and standards: Achieving 21st century mastery for all. In Engaging young children in mathematics: Standards for early childhood mathematics education, edited by D. H. Clements, J. Sarama, and A.-M. DiBiase. Mahwah, NJ: Lawrence Erlbaum Associates. Pp. 105-148.
Fuson, K. C., and Y. Kwon. 1992. Korean childen’s understanding of multidigit addition and subtraction. Child Development 63:491-506.
Gámez, P. B., and N. K. Lesaux. 2012. The relation between exposure to sophisticated and complex language and early-adolescent English-only and language minority learners’ vocabulary. Child Development 83(4):1316-1331.
Gámez, P. B., and S. C. Levine. 2013. Oral language skills of spanish-speaking English language learners: The impact of high-quality native language exposure. Applied Psycholinguistics 34(4):673-696.
GAO (U.S. Government Accountability Office). 2012. K-12 education school-based physical education and sports programs: Report to congressional requesters. Washington, DC: GAO.
Geary, D. C. 1990. A componential analysis of an early learning deficit in mathematics. Journal of Experimental Child Psychology 49:363-383.
———. 1993. Mathematical disabilities: Cognitive, neuropsychological, and genetic components. Psychological Bulletin 114:345-362.
———. 1994. Children’s mathematical development: Research and practical applications. Washington, DC: American Psychological Association.
———. 2004. Mathematics and learning disabilities. Journal of Learning Disabilities 37:4-15.
———. 2011. Cognitive addition: A short longitudinal study of strategy choice and speed-of-processing differences in normal and mathematically disabled children. Developmental Psychology 47:1539-1552.
Geary, D. C., C. C. Bow-Thomas, and Y. Yao. 1992. Counting knowledge and skill in cognitive addition: A comparison of normal and mathematically disabled children. Journal of Experimental Child Psychology 54:372-391.
Geary, D. C., M. K. Hoard, J. Byrd-Craven, L. Nugent, and C. Numtee. 2007. Cognitive mechanisms underlying achievement deficits in children with mathematical learning disability. Child Development 78:1343-1359.
Geary, D. C., M. K. Hoard, and L. Nugent. 2012. Independent contributions of the central executive, intelligence, and in-class attentive behavior to developmental change in the strategies used to solve addition problems. Journal of Experimental Child Psychology 113(1):49-65.
Gelman, S. A. 2003. The essential child origins of essentialism in everyday thought. New York: Oxford University Press.
Gelman, S. A., and E. M. Markman. 1987. Young children’s inductions from natural kinds: The role of categories and appearances. Child Development 58(6):1532-1541.
Gergely, G., H. Bekkering, and I. Kiraly. 2002. Rational imitation in preverbal infants. Nature 415(6873):755.
Gerson, S. A., and A. L. Woodward. 2014. Learning from their own actions: The unique effect of producing actions on infants’ action understanding. Child Development 85(1): 264-277.
Gilliam, W. S. 2005. Prekindergarteners left behind: Expulsion rates in state prekindergarten systems. New York: Foundation for Child Development.
Girolametto, L., and E. Weitzman. 2002. Responsiveness of child care providers in interactions with toddlers and preschoolers. Language, Speech, and Hearing Services in Schools 33(4):268-281.
Glaser, R. 1991. The maturing of the relationship between the science of learning of learning and cognition and educational practice. Learning and Instruction 1:129-144.
Glasersfeld, E. V. 1995. Sensory experience, abstraction, and teaching. In Constructivism in education, edited by L. P. Steffe and J. Gale. Mahwah, NJ: Lawrence Erlbaum Associates. Pp. 369-383.
Gopnik, A., and H. M. Wellman. 2012. Reconstructing constructivism: Causal models, Bayesian learning mechanisms, and the theory theory. Psychological Bulletin 138(6):1085-1108.
Gopnik, A., D. M. Sobel, L. E. Schulz, and C. Glymour. 2001. Causal learning mechanisms in very young children: Two-, three-, and four-year-olds infer causal relations from patterns of variation and covariation. Developmental Psychology 37(5):620-629.
Gough, P. B., and W. E. Tunmer. 1986. Decoding, reading, and reading disability. Remedial and Special Education (RASE) 7(1):6-10.
Gough, P. B., W. A. Hoover, and C. L. Peterson. 1996. Some observations on a simple view of reading. In Reading comprehension difficulties, edited by C. Cornoldi and J. Oakhill. Mahwah, NJ: Lawrence Erlbaum Associates. Pp. 1-13.
Graham, S. A., C. S. Kilbreath, and A. N. Welder. 2004. Thirteen-month-olds rely on shared labels and shape similarity for inductive inferences. Child Development 75(2):409-427.
Graziano, P. A., R. D. Reavis, S. P. Keane, and S. D. Calkins. 2007. The role of emotion regulation and children’s early academic success. Journal of School Psychology 45(1):3-19.
Greenwood, C., J. Buzhardt, D. Walker, W. Howard, and R. Anderson. 2011. Program-level influences on the measurement of early communication for infants and toddlers in early Head Start. Journal of Early Intervention 33(2):110-134.
Gripshover, S. J., and E. M. Markman. 2013. Teaching young children a theory of nutrition: Conceptual change and the potential for increased vegetable consumption. Psychological Science 24(8):1541-1553.
Grissmer, D., K. J. Grimm, S. M. Aiyer, W. M. Murrah, and J. S. Steele. 2010a. Fine motor skills and early comprehension of the world: Two new school readiness indicators. Developmental Psychology 46(5):1008-1017.
———. 2010b. New school readiness indicators. Research brief. Charlottesville: University of Virginia, Center for Advanced Study of Teaching and Learning.
Gunderson, E. A., and S. C. Levine. 2011. Some types of parent number talk count more than others: Relations between parents’ input and children’s cardinal-number knowledge. Developmental Science 14(5):1021-1032.
Gunderson, E. A., S. J. Gripshover, C. Romero, C. S. Dweck, S. Goldin-Meadow, and S. C. Levine. 2013. Parent praise to 1- to 3-year-olds predicts children’s motivational frameworks 5 years later. Child Development 84(5):1526-1541.
Gunnar, M. R., and B. Donzella. 2002. Social regulation of the cortisol levels in early human development. Psychoneuroendocrinology 27(1-2):199-220.
Gunnar, M. R., S. J. Morison, K. Chisholm, and M. Schuder. 2001. Salivary cortisol levels in children adopted from Romanian orphanages. Development and Psychopathology 13(3):611-628.
Gweon, H., and L. Schulz. 2011. 16-month-olds rationally infer causes of failed actions. Science 332(6037):1524.
Hackman, D. A., and M. J. Farah. 2009. Socioeconomic status and the developing brain. Trends in Cognitive Sciences 13(2):65-73.
Hackman, D. A., M. J. Farah, and M. J. Meaney. 2010. Socioeconomic status and the brain: Mechanistic insights from human and animal research. Nature Reviews: Neuroscience 11(9):651-659.
Hair, E., T. Halle, E. Terry-Humen, B. Lavelle, and J. Calkins. 2006. Children’s school readiness in the ECLS-K: Predictions to academic, health, and social outcomes in first grade. Early Childhood Research Quarterly 21(4):431-454.
Hamayan, E. V., B. Marler, C. Sánchez López, and J. Damico. 2013. Special education considerations for English language learners: Delivering a continuum of services, 2nd ed. Philadelphia, PA: Caslon Publishing.
Hamlin, J. K., K. Wynn, and P. Bloom. 2007. Social evaluation by preverbal infants. Nature 450(7169):557-559.
Hamre, B. K. 2014. Teachers’ daily interactions with children: An essential ingredient in effective early childhood programs. Child Development Perspectives 8(4):223-230.
Hamre, B. K., and R. C. Pianta. 2001. Early teacher–child relationships and the trajectory of children’s school outcomes through eighth grade. Child Development 72:625-638.
———. 2004. Self-reported depression in nonfamilial caregivers: Prevalence and associations with caregiver behavior in child-care settings. Early Childhood Research Quarterly 19(2):297-318.
———. 2005. Can instructional and emotional support in the first-grade classroom make a difference for children at risk of school failure? Child Development 76(5):949-967.
Hannula, M. M. 2005. Spontaneous focusing on numerosity in the development of early mathematical skills. Turku, Finland: University of Turku.
Hannula, M. M., P. Räsänen, and E. Lehtinen. 2007. Development of counting skills: Role of spontaneous focusing on numerosity and subitizing-based enumeration. Mathematical Thinking and Learning 9:51-57.
Harris, K. R., B. D. Friedlander, B. Saddler, R. Frizzelle, and S. Graham. 2005. Self-monitoring of attention versus self-monitoring of academic performance: Effects among students with ADHD in the general education classroom. Journal of Special Education 39(3):145-156.
Harris, P. L. 2012. Trusting what you’re told: How children learn from others. Cambridge, MA: Belknap Press of Harvard University Press.
Hart, B., and T. R. Risley. 1995. Meaningful differences in the everyday experience of young American children. Baltimore, MD: Paul H. Brookes Publishing Co.
Hawthorne, K., and L. Gerken. 2014. From pauses to clauses: Prosody facilitates learning of syntactic constituency. Cognition 133(2):420-428.
Hecht, S. A., J. K. Torgesen, R. K. Wagner, and C. A. Raschotte. 2001. The relations between phonological processing abilities and emerging individual differences in mathematical computation skills: A longitudinal study from second to fifth grades. Journal of Experimental Child Psychology 79:192-227.
Heckman, J. J. 2007. The economics, technology, and neuroscience of human capability formation. Proceedings of the National Academy of Sciences of the United States of America 104(33):13250-13255.
Heckman, J. J., R. Pinto, and P. Savelyev. 2013. Understanding the mechanisms through which an influential early childhood program boosted adult outcomes. American Economic Review 103(6):2052-2086.
Henry, G. T., and D. K. Rickman. 2007. Do peers influence children’s skill development in preschool? Economics of Education Review 26(1):100-112.
Hertzman, C., and W. T. Boyce. 2010. How experience gets under the skin to create gradients in developmental health. Annual Review of Public Health 31:329-347.
Heyman, G. D., and C. S. Dweck. 1992. Achievement goals and intrinsic motivation: Their relation and their role in adaptive motivation. Motivation and Emotion 16(3):231-247.
Heyman, G. D., and S. A. Gelman. 1999. The use of trait labels in making psychological inferences. Child Development 70(3):604-619.
Hibel, L. C., D. A. Granger, C. Blair, and M. J. Cox. 2011. Maternal sensitivity buffers the adrenocortical implications of intimate partner violence exposure during early childhood. Development and Psychopathology 23(2):689-701.
Hill, E. L. 2004. Executive dysfunction in autism. Trends in Cognitive Sciences 8(1):26-32.
Hindman, A. H., C. M. Connor, A. M. Jewkes, and F. J. Morrison. 2008. Untangling the effects of shared book reading: Multiple factors and their associations with preschool literacy outcomes. Early Childhood Research Quarterly 23(3):330-350.
Hinnant, J. B., M. O’Brien, and S. R. Ghazarian. 2009. The longitudinal relations of teacher expectations to achievement in the early school years. Journal of Educational Psychology 101(3):662-670.
Hoff, E. 2006. How social contexts support and shape language development. Developmental Review 26(1):55-88.
Hoff, E., and L. Naigles. 2002. How children use input to acquire a lexicon. Child Development 73(2):418-433.
Hofmann, W., B. J. Schmeichel, and A. D. Baddeley. 2012. Executive functions and self-regulation. Trends in Cognitive Sciences 16(3):174-180.
Hongwanishkul, D., K. R. Happaney, W. S. C. Lee, and P. D. Zelazo. 2005. Assessment of hot and cool executive function in young children: Age-related changes and individual differences. Developmental Neuropsychology 28(2):617-644.
Hoover, W. A., and P. B. Gough. 1990. The simple view of reading. Reading and Writing: An Interdisciplinary Journal 2(2):127-160.
Hostinar, C. E., R. M. Sullivan, and M. R. Gunnar. 2014. Psychobiological mechanisms underlying the social buffering of the hypothalamic–pituitary–adrenocortical axis: A review of animal models and human studies across development. Psychological Bulletin 140(1):256-282.
Howes, C., and C. E. Hamilton. 1992. Children’s relationships with child care teachers: Stability and concordance with parental attachments. Child Development 63(4):867-878.
Howes, C., C. E. Hamilton, and L. C. Phillipsen. 1998. Stability and continuity of child-caregiver and child-peer relationships. Child Development 69(2):418-426.
Howse, R. B., G. Lange, D. C. Farran, and C. D. Boyles. 2003a. Motivation and self-regulation as predictors of achievement in economically disadvantaged young children. Journal of Experimental Education 71(2):151-174.
Howse, R. B., S. D. Calkins, A. D. Anastopoulos, S. P. Keane, and T. L. Shelton. 2003b. Regulatory contributors to children’s kindergarten achievement. Early Education and Development 14(1):101-120.
Hoyland, A., L. Dye, and C. L. Lawton. 2009. A systematic review of the effect of breakfast on the cognitive performance of children and adolescents. Nutrition Research Reviews 22(2):220-243.
Hoynes, H., M. Page, and A. H. Stevens. 2011. Can targeted transfers improve birth outcomes?: Evidence from the introduction of the WIC program. Journal of Public Economics 95(7-8):813-827.
Huang-Pollock, C. L., S. L. Karalunas, H. Tam, and A. N. Moore. 2012. Evaluating vigilance deficits in ADHD: A meta-analysis of CPT performance. Journal of Abnormal Psychology 121(2):360-371.
Huffman, L. C., S. L. Mehlinger, and A. S. Kerivan. 2000. Risk factors for academic and behavioral problems in the beginning of school. Chapel Hill: University of North Carolina, Frank Porter Graham Child Development Center.
Hughes, C., and R. Ensor. 2011. Individual differences in growth in executive function across the transition to school predict externalizing and internalizing behaviors and self-perceived academic success at 6 years of age. Journal of Experimental Child Psychology 108:663-676.
Huntley-Fenner, G., S. Carey, and A. Solimando. 2002. Objects are individuals but stuff doesn’t count: Perceived rigidity and cohesiveness influence infants’ representations of small groups of discrete entities. Cognition 85:203-221.
Huttenlocher, J. 1998. Language input and language growth. Preventive Medicine 27(2): 195-199.
Huttenlocher, J., N. C. Jordan, and S. C. Levine. 1994. A mental model for early arithmetic. Journal of Experimental Psychology: General 123:284-296.
Huttenlocher, J., M. Vasilyeva, E. Cymerman, and S. Levine. 2002. Language input and child syntax. Cognitive Psychology 45(3):337-374.
Huttenlocher, J., H. Waterfall, M. Vasilyeva, J. Vevea, and L. V. Hedges. 2010. Sources of variability in children’s language growth. Cognitive Psychology 61(4):343-365.
Hyde, D. C., and E. S. Spelke. 2011. Neural signatures of number processing in human infants: Evidence for two core systems underlying numerical cognition. Developmental Science 14(2):360-371.
Inagaki, K., and G. Hatano. 2004. Vitalistic causality in young children’s naive biology. Trends in Cognitive Sciences 8(8):356-362.
IOM (Institute of Medicine). 2011. Hunger and obesity: Understanding a food insecurity paradigm: Workshop summary, edited by L. M. Troy, E. A. Miller, and S. Olson. Washington, DC: The National Academies Press.
———. 2013. Educating the student body: Taking physical activity and physical education to school, edited by H. W. Kohl III and H. D. Cook. Washington, DC: The National Academies Press.
IOM and NRC (National Research Council). 2009. Preventing mental, emotional, and behavioral disorders among young people: Progress and possibilities. Washington, DC: The National Academies Press.
Isen, A., M. Rossin-Slater, and W. R. Walker. 2014. Every breath you take—every dollar you’ll make: The long-term consequences of the Clean Air Act of 1970. National Bureau of Economic Research Working Paper Series No. 19858.
Jacoby, J. W., and N. K. Lesaux. 2014. Support for extended discourse in teacher talk with linguistically diverse preschoolers. Early Education and Development 25(8):1162-1179.
Jamison, K. R., S. Q. Cabell, J. LoCasale-Crouch, B. K. Hamre, and R. C. Pianta. 2014. Class–infant: An observational measure for assessing teacher–infant interactions in center-based child care. Early Education & Development 25(4):553-572.
Janzen, J. 2008. Teaching English language learners. Review of Educational Research 78:1010-1038.
Jaswal, V. K. 2010. Believing what you’re told: Young children’s trust in unexpected testimony about the physical world. Cognitive Psychology 61(3):248-272.
Jenks, K. M., E. C. van Lieshout, and J. M. de Moor. 2012. Cognitive correlates of mathematical achievement in children with cerebral palsy and typically developing children. British Journal of Educational Psychology 82(1):120-135.
Jeon, L., C. K. Buettner, and A. R. Snyder. 2014. Pathways from teacher depression and childcare quality to child behavioral problems. Journal of Consulting and Clinical Psychology 82(2):225-235.
Jiang, Y., M. Ekono, and C. Skinner. 2014. Basic facts about low-income children: Children under 6 years, 2012. http://nccp.org/publications/pub_1088.html (accessed January 26, 2015).
Johnson-Pynn, J. S., C. Ready, and M. Beran. 2005. Estimation mediates preschoolers: Numerical reasoning: Evidence against precise calculation abilities. Paper read at Biennial Meeting of the Society for Research in Child Development, April, Atlanta, GA.
Johnston, T., and J. Kirby. 2006. The contribution of naming speed to the simple view of reading. Reading and Writing 19(4):339-361.
Jordan, N. C., L. B. Hanich, and H. Z. Uberti. 2003. Mathematical thinking and learning difficulties. In The development of arithmetic concepts and skills: Constructing adaptive expertise, edited by A. J. Baroody and A. Dowker. Mahwah, NJ: Lawrence Erlbaum Associates. Pp. 359-383.
Joshi, R. M., and P. G. Aaron. 2000. The component model of reading: Simple view of reading made a little more complex. Reading Psychology 21(2):85-97.
Justice, L. M., J. Meier, and S. Walpole. 2005. Learning new words from storybooks: An efficacy study with at-risk kindergartners. Language, Speech, and Hearing Services in Schools 36(1):17-32.
Justice, L. M., A. S. McGinty, T. Zucker, S. Q. Cabell, and S. B. Piasta. 2013. Bi-directional dynamics underlie the complexity of talk in teacher–child play-based conversations in classrooms serving at-risk pupils. Early Childhood Research Quarterly 28(3):496-508.
Kamins, M. L., and C. S. Dweck. 1999. Person versus process praise and criticism: Implications for contingent self-worth and coping. Developmental Psychology 35(3):835-847.
Kaplan, D. W., C. D. Brindis, S. L. Phibbs, P. Melinkovich, K. Naylor, and K. Ahlstrand. 1999. A comparison study of an elementary school-based health center: Effects on health care access and use. Archives of Pediatrics and Adolescent Medicine 153(3):235-243.
Karmiloff, K., and A. Karmiloff-Smith. 2001. Pathways to language: From fetus to adolescent. Cambridge, MA: Harvard University Press.
Kaufman, E. L., M. W. Lord, T. W. Reese, and J. Volkmann. 1949. The discrimination of visual number. American Journal of Psychology 62:498-525.
Keenan, K., D. S. Shaw, B. Walsh, E. Delliquadri, and J. Giovannelli. 1997. DSM-III-R disorders in preschool children from low-income families. Journal of the American Academy of Child and Adolescent Psychiatry 36(5):620-627.
Kelley, S. A., C. A. Brownell, and S. B. Campbell. 2000. Mastery motivation and self-evaluative affect in toddlers: Longitudinal relations with maternal behavior. Child Development 71(4):1061-1071.
Kerr, A., and P. D. Zelazo. 2004. Development of “hot” executive function: The children’s gambling task. Brain and Cognition 55(1):148-157.
Keyl, P. M., M. P. Hurtado, M. M. Barber, and J. Borton. 1996. School-based health centers. Students’ access, knowledge, and use of services. Archives of Pediatrics and Adolescent Medicine 150(2):175-180.
Kiecolt-Glaser, J. K., J. P. Gouin, and L. Hantsoo. 2010. Close relationships, inflammation, and health. Neuroscience and Biobehavioral Reviews 35(1):33-38.
King, J. C., Jr., J. J. Stoddard, M. J. Gaglani, K. A. Moore, L. Magder, E. McClure, J. D. Rubin, J. A. Englund, and K. Neuzil. 2006. Effectiveness of school-based influenza vaccination. New England Journal of Medicine 355(24):2523-2532.
Kipping, P., A. Gard, L. Gilman, and J. Gorman. 2012. Speech and language development chart, 3rd ed. Austin, TX: Pro-Ed.
Kirkham, N. Z., J. A. Slemmer, and S. P. Johnson. 2002. Visual statistical learning in infancy: Evidence for a domain general learning mechanism. Cognition 83(2):B35-B42.
Kishiyama, M. M., W. T. Boyce, A. M. Jimenez, L. M. Perry, and R. T. Knight. 2009. Socioeconomic disparities affect prefrontal function in children. Journal of Cognitive Neuroscience 21(6):1106-1115.
Klibanoff, R. S., S. C. Levine, J. Huttenlocher, M. Vasilyeva, and L. V. Hedges. 2006. Preschool children’s mathematical knowledge: The effect of teacher “math talk.” Developmental Psychology 42:59-69.
Koenig, M. A., and S. Doebel. 2013. Children’s understanding of unreliability: Evidence for a negativity bias. In Navigating the social world, edited by M. R. Banaji and S. A. Gelman. New York: Oxford University Press. Pp. 235-240.
Koles, B., E. O’Connor, and K. McCartney. 2009. Teacher–child relationships in prekindergarten: The influences of child and teacher characteristics. Journal of Early Childhood Teacher Education 30(1):3-21.
Koponen, T., P. Salmi, K. Eklund, and T. Aro. 2013. Counting and ran: Predictors of arithmetic calculation and reading fluency. Journal of Educational Psychology 105(1):162-175.
La Paro, K. M., and R. C. Pianta. 2000. Predicting children’s competence in the early school years: A meta-analytic review. Review of Educational Research 70(4):443-484.
Ladd, G. W., and K. B. Burgess. 2001. Do relational risks and protective factors moderate the linkages between childhood aggression and early psychological and school adjustment? Child Development 72(5):1579-1601.
Ladd, G. W., B. J. Kochenderfer, and C. C. Coleman. 1996. Friendship quality as a predictor of young children’s early school adjustment. Child Development 67(3):1103-1118.
———. 1997. Classroom peer acceptance, friendship, and victimization: Distinct relational systems that contribute uniquely to children’s school adjustment? Child Development 68(6):1181-1197.
Ladd, G. W., S. Birch, and E. Buhs. 1999. Children’s social and scholastic lives in kindergarten: Related spheres of influence? Child Development 70:1373-1400.
Lalonde, C. E., and M. J. Chandler. 2002. Children’s understanding of interpretation. New Ideas in Psychology 20(2-3):163-198.
Lan, X., C. H. Legare, C. C. Ponitz, S. Li, and F. J. Morrison. 2011. Investigating the links between the subcomponents of executive function and academic achievement: A cross-cultural analysis of chinese and American preschoolers. Journal of Experimental Child Psychology 108:677-692.
Lane, K., S. Barton-Arwood, J. R. Nelson, and J. Wehby. 2008. Academic performance of students with emotional and behavioral disorders served in a self-contained setting. Journal of Behavioral Education 17(1):43-62.
Laski, E. V., and R. S. Siegler. 2014. Learning from number board games: You learn what you encode. Developmental Psychology 50(3):853-864.
Lauderdale, D. S. 2006. Birth outcomes for Arabic-named women in California before and after September 11. Demography 43(1):185-201.
Lavigne, J. V., S. A. Lebailly, J. Hopkins, K. R. Gouze, and H. J. Binns. 2009. The prevalence of ADHD, odd, depression, and anxiety in a community sample of 4-year-olds. Journal of Clinical Child and Adolescent Psychology 38(3):315-328.
Lawrence, J., and C. Snow. 2011. Oral discourse and reading. In Handbook of reading research, edited by M. L. Kamil, P. D. Pearson, E. B. Moje, and P. Afflerbach. New York: Routledge.
Le Corre, M., G. A. Van de Walle, E. M. Brannon, and S. Carey. 2006. Re-visiting the competence/performance debate in the acquisition of counting as a representation of the positive integers. Cognitive Psychology 52(2):130-169.
Leckman, J. F., and J. S. March. 2011. Editorial: Developmental neuroscience comes of age. Journal of Child Psychology and Psychiatry 52(4):333-338.
LeDoux, J. 1996. The emotional brain: Development and psychopathology. New York: Simon & Schuster.
Lee, J. S. 2011. Size matters: Early vocabulary as a predictor of language and literacy competence. Applied Psycholinguistics 32(1):69-92.
Lee, J. S., and H. P. Ginsburg. 2007. Preschool teachers’ beliefs about appropriate early literacy and mathematics education for low- and middle-socioeconomic status children. Early Education & Development 18(1):111-143.
Lee, K. 2013. Little liars: Development of verbal deception in children. Child Development Perspectives 7(2):91-96.
Leerkes, E. M., M. J. Paradise, M. O’Brien, S. D. Calkins, and G. Lange. 2008. Emotion and cognition processes in preschool children. Merrill-Palmer Quarterly 54(1):102-124.
LeFevre, J.-A., L. Berrigan, C. Vendetti, D. Kamawar, J. Bisanz, S.-L. Skwarchuk, and B. L. Smith-Chant. 2013. The role of executive attention in the acquisition of mathematical skills for children in grades 2 through 4. Journal of Experimental Child Psychology 114(2):243-261.
Lerkkanen, M.-K., H. Rasku-Puttonen, K. Aunola, and J.-E. Nurmi. 2005. Mathematical performance predicts progress in reading comprehension among 7-year-olds. European Journal of Psychology of Education 20(2):121-137.
Levine, S. C., L. W. Suriyakham, M. L. Rowe, J. Huttenlocher, and E. A. Gunderson. 2010. What counts in the development of young children’s number knowledge? Developmental Psychology 46(5):1309-1319.
Lewit, E. M., and L. S. Baker. 1995. School readiness. The Future of Children/Center for the Future of Children, the David and Lucile Packard Foundation 5(2):128-139.
Libertus, M. E., and E. M. Brannon. 2010. Stable individual differences in number discrimination in infancy. Developmental Science 13(6):900-906.
Lieberman, A. F., A. Chu, P. Van Horn, and W. W. Harris. 2011. Trauma in early childhood: Empirical evidence and clinical implications. Development and Psychopathology 23(2):397-410.
Lien, D. S., and W. N. Evans. 2005. Estimating the impact of large cigarette tax hikes: The case of maternal smoking and infant birth weight. Journal of Human Resources 40(2):373-392.
Low, M., A. Farrell, B. A. Biggs, and S. R. Pasricha. 2013. Effects of daily iron supplementation in primary-school-aged children: Systematic review and meta-analysis of randomized controlled trials. Canadian Medical Association Journal 185(17):E791-E802.
Lozoff, B. 2011. Early iron deficiency has brain and behavior effects consistent with dopaminergic dysfunction. Journal of Nutrition 141(4):740s-746s.
Lozoff, B., M. Castillo, K. M. Clark, J. B. Smith, and J. Sturza. 2014. Iron supplementation in infancy contributes to more adaptive behavior at 10 years of age. Journal of Nutrition 144(6):838-845.
Luby, J. L. 2006. Handbook of preschool mental health: Development, disorders, and treatment. New York: Guilford Press.
Luby, J. L., D. M. Barch, A. Belden, M. S. Gaffrey, R. Tillman, C. Babb, T. Nishino, H. Suzuki, and K. N. Botteron. 2012. Maternal support in early childhood predicts larger hippocampal volumes at school age. Proceedings of the National Academy of Sciences of the United States of America 109(8):2854-2859.
Lupien, S. J., B. S. McEwen, M. R. Gunnar, and C. Heim. 2009. Effects of stress throughout the lifespan on the brain, behaviour and cognition. Nature Reviews Neuroscience 10(6):434-445.
Lynam, D., T. Moffitt, and M. Stouthamer-Loeber. 1993. Explaining the relation between IQ and delinquency: Class, race, test motivation, school failure, or self-control? Journal of Abnormal Psychology 102(2):187-196.
Lyon, G. R., and N. A. Krasnegor. 1996. Attention, memory, and executive function. Baltimore, MD: Paul H. Brookes Publishing Co.
Maclellan, E. 2012. Number sense: The underpinning understanding for early quantitative literacy. Numeracy 5(2):1-19.
Mages, W. K. 2008. Does creative drama promote language development in early childhood?: A review of the methods and measures employed in the empirical literature. Review of Educational Research 78(1):124-152.
Mahoney, C. R., H. A. Taylor, R. B. Kanarek, and P. Samuel. 2005. Effect of breakfast composition on cognitive processes in elementary school children. Physiology and Behavior 85(5):635-645.
Maier, M. F., and D. B. Greenfield. 2008. The differential role of initiative and persistence in early childhood. Paper presented at Institute of Education Science 2007 Research Conference, Washington, DC.
Mandler, J. M. 2004. The foundations of mind origins of conceptual thought. http://site.ebrary.com/id/10103678 (accessed January 2, 2015).
Mantzicopoulos, P. 1997. The relationship of family variables to Head Start children’s preacademic competence. Early Education and Development 8(4):357-375.
Marcovitch, S., and P. D. Zelazo. 2009. A hierarchical competing systems model of the emergence and early development of executive function. Developmental Science 12(1):1-25.
Markman, E. M. 1977. Realizing that you don’t understand: A preliminary investigation. Child Development 48:986-999.
———. 1981. Comprehension monitoring. In Children’s oral communication skills, edited by W. P. Dickson. New York: Academic Press. Pp. 61-84.
Marsh, H. W., R. Craven, and R. Debus. 1998. Structure, stability, and development of young children’s self-concepts: A multicohort-multioccasion study. Child Development 69(4):1030-1053.
Marsh, H. W., L. A. Ellis, and R. G. Craven. 2002. How do preschool children feel about themselves? Unraveling measurement and multidimensional self-concept structure. Developmental Psychology 38(3):376-393.
Masataka, N., T. Ohnishi, E. Imabayashi, M. Hirakata, and H. Matsuda. 2006. Neural correlates for numerical processing in the manual mode. Journal of Deaf Studies and Deaf Education 11(2):144-152.
Mashburn, A. J., R. C. Pianta, B. K. Hamre, J. T. Downer, O. A. Barbarin, D. Bryant, M. Burchinal, D. M. Early, and C. Howes. 2008. Measures of classroom quality in prekindergarten and children’s development of academic, language, and social skills. Child Development 79(3):732-749.
Mashburn, A. J., L. M. Justice, J. T. Downer, and R. C. Pianta. 2009. Peer effects on children’s language achievement during pre-kindergarten. Child Development 80(3):686-702.
Masten, A. S., J. E. Herbers, C. D. Desjardins, J. J. Cutuli, C. M. McCormick, J. K. Sapienza, J. D. Long, and P. D. Zelazo. 2012. Executive function skills and school success in young children experiencing homelessness. Educational Researcher 41(9):375-384.
Master, A., E. M. Markman, and C. S. Dweck. 2012. Thinking in categories or along a continuum: Consequences for children’s social judgments. Child Development 83(4):1145-1163.
Mazzocco, M. M. M., and L. B. Hanich. 2010. Math achievement, numerical processing, and executive functions in girls with Turner syndrome: Do girls with Turner syndrome have math learning disability? Learning and Individual Differences 20:70-81.
Mazzocco, M. M. M., and G. F. Myers. 2003. Complexities in identifying and defining mathematics learning disability in the primary school-age years. Annuals of Dyslexia 53:218-253.
Mazzocco, M. M. M., and R. E. Thompson. 2005. Kindergarten predictors of math learning disability. Learning Disability Quarterly Research and Practice 20:142-155.
McClelland, M. M., C. E. Cameron, C. M. Connor, C. L. Farris, A. M. Jewkes, and F. J. Morrison. 2007. Links between behavioral regulation and preschoolers’ literacy, vocabulary, and math skills. Developmental Psychology 43(4):947-959.
McCormick, M. C., J. Brooks-Gunn, S. L. Buka, J. Goldman, J. Yu, M. Salganik, D. T. Scott, F. C. Bennett, L. L. Kay, J. C. Bernbaum, C. R. Bauer, C. Martin, E. R. Woods, A. Martin, and P. H. Casey. 2006. Early intervention in low birth weight premature infants: Results at 18 years of age for the infant health and development program. Pediatrics 117(3):771-780.
McEwen, B. S. 2012. Brain on stress: How the social environment gets under the skin. Proceedings of the National Academy of Sciences of the United States of America 109(Suppl. 2):17180-17185.
McGillicuddy-De Lisi, A. V. 1982. The relationship between parents’ beliefs about development and family constellation, socioeconomic status, and parents’ teaching strategies. In Families as learning environments for children, edited by L. M. Laosa and I. E. Sigel. New York: Plenum Press. Pp. 261-299.
McIntyre, L. L., J. Blacher, and B. L. Baker. 2006. The transition to school: Adaptation in young children with and without intellectual disability. Journal of Intellectual Disability Research 50(Pt. 5):349-361.
McLean, J. F., and G. J. Hitch. 1999. Working memory impairments in children with specific arithmetic learning difficulties. Journal of Experimental Child Psychology 74:240-260.
McLeod, D. B., and V. M. Adams, eds. 1989. Affect and mathematical problem solving. New York: Springer-Verlag.
McMillan, T. 2014. The new face of hunger. National Geographic Magazine, August 2014, 66-68, 70, 72-74, 77-80, 83-89.
Meltzoff, A. N. 1995. Understanding the intentions of others: Re-enactment of intended acts by 18-month-old children. Developmental Psychology 31(5):838-850.
Metsala, J. L., and A. C. Walley. 1998. Spoken vocabulary growth and the segmental restructuring of lexical representations: Precursors to phonemic awareness and early reading ability. In Word recognition in beginning literacy, edited by J. L. Metsala and L. C. Ehri. Hillsdale, NJ: Lawrence Erlbaum Associates. Pp. 89-120.
Middleton, J. A., and P. Spanias. 1999. Motivation for achievement in mathematics: Findings, generalizations, and criticisms of the research. Journal for Research in Mathematics Education 30:65-88.
Miller, A. L., R. Seifer, L. Stroud, S. J. Sheinkopf, and S. Dickstein. 2006. Biobehavioral indices of emotion regulation relate to school attitudes, motivation, and behavior problems in a low-income preschool sample. Annals of the New York Academy of Sciences 1094:325-329.
Miller, G. E., E. Chen, and K. J. Parker. 2011. Psychological stress in childhood and susceptibility to the chronic diseases of aging: Moving toward a model of behavioral and biological mechanisms. Psychological Bulletin 137(6):959-997.
Mills, C. M. 2013. Knowing when to doubt: Developing a critical stance when learning from others. Developmental Psychology 49(3):404-418.
Mischel, W., Y. Shoda, and P. K. Peake. 1988. The nature of adolescent competencies predicted by preschool delay of gratification. Journal of Personality and Social Psychology 54(4):687-696.
Mitchell-Copeland, J., S. A. Denham, and E. K. DeMulder. 1997. Q-sort assessment of child–teacher attachment relationships and social competence in the preschool. Early Education and Development 8(1):27-39.
Mix, K. S., J. Huttenlocher, and S. C. Levine. 2002. Quantitative development in infancy and early childhood. New York: Oxford University Press.
Mix, K. S., C. M. Sandhofer, and A. J. Baroody. 2005. Number words and number concepts: The interplay of verbal and nonverbal processes in early quantitative development. In Advances in child development and behavior. Vol. 33, edited by R. Kail. New York: Academic Press. Pp. 305-345.
Miyake, A., N. P. Friedman, M. J. Emerson, A. H. Witzki, A. Howerter, and T. D. Wager. 2000. The unity and diversity of executive functions and their contributions to complex “frontal lobe” tasks: A latent variable analysis. Cognitive Psychology 41(1):49-100.
Mol, S. E., A. G. Bus, and M. T. de Jong. 2009. Interactive book reading in early education: A tool to stimulate print knowledge as well as oral language. Review of Educational Research 79(2):979-1007.
Moll, L. C., C. Amanti, D. Neff, and N. Gonzalez. 1992. Funds of knowledge for teaching: Using a qualitative approach to connect homes and classrooms. Theory into Practice 31(2):132-141.
Mooji, T. 2010. Design and implementation of ICT-supported education for highly able pupils. Paper read at European Conference on Educational Research, Helsinki, Finland.
Morgan, G. A., R. J. Harmon, and C. A. Maslin-Cole. 1990. Mastery motivation: Definition and measurement. Early Education and Development 1(5):318-339.
Morgan, P. L., G. Farkas, P. A. Tufis, and R. A. Sperling. 2008. Are reading and behavior problems risk factors for each other? Journal of Learning Disabilities 41(5):417-436.
Morrow, L. M., and J. A. Schickedanz. 2006. The relationships between sociodramatic play and literacy development. In Handbook of early literacy research. Vol. 2, edited by D. K. Dickinson and S. B. Neuman. New York and London: Guilford Press.
Müller, U., D. Lieberman, D. Frye, and P. D. Zelazo. 2008. Executive function, school readiness, and school achievement. In Cognitive development in K-3 classroom learning: Research applications, edited by C. Fiorello and K. Thurman. Mahwah, NJ: Lawrence Erlbaum Associates. Pp. 41-84.
Mullis, I. V. S., M. O. Martin, P. Foy, and A. Arora. 2012. TIMSS 2011 international results in mathematics. Chestnut Hill, MA: Boston College, Trends in International Mathematics and Science Study and Progress in International Reading Literacy Study International Study Center.
Munn, P. 2006. Mathematics in early childhood—the early years math curriculum in the UK and children’s numerical development. International Journal of Early Childhood 38(1):99-112.
Nachmias, M., M. Gunnar, S. Mangelsdorf, R. H. Parritz, and K. Buss. 1996. Behavioral inhibition and stress reactivity: The moderating role of attachment security. Child Development 67(2):508-522.
Nader-Grosbois, N., and N. Lefèvre. 2011. Self-regulation and performance in problem-solving using physical materials or computers in children with intellectual disability. Research in Developmental Disabilities 32:1492-1505.
Nagy, W., V. W. Berninger, and R. D. Abbott. 2006. Contributions of morphology beyond phonology to literacy outcomes of upper elementary and middle-school students. Journal of Educational Psychology 98(1):134-147.
Nation, K., and M. J. Snowling. 1999. Developmental differences in sensitivity to semantic relations among good and poor comprehenders: Evidence from semantic priming. Cognition 70(1):1.
National Early Literacy Panel. 2008. Developing early literacy: A report of the National Early Literacy Panel. http://purl.access.gpo.gov/GPO/LPS108121 (accessed December 15, 2014).
National Mathematics Advisory Panel. 2008. Foundations for success: The final report of the National Mathematics Advisory Panel. Washington, DC: U.S. Department of Education, Office of Planning, Evaluation and Policy Development.
Nayfeld, I., J. Fuccillo, and D. B. Greenfield. 2013. Executive functions in early learning: Extending the relationship between executive functions and school readiness to science. Learning and Individual Differences 26:81-88.
NCTSN (National Child Traumatic Stress Network). 2005. Understanding child traumatic stress. http://www.nctsnet.org/sites/default/files/assets/pdfs/understanding_child_traumatic_stress_brochure_9-29-05.pdf (accessed March 23, 2015).
NCTSN Core Curriculum on Childhood Trauma Task Force. 2012. The 12 core concepts: Concepts for understanding traumatic stress responses in children and families. Core curriculum on childhood trauma. Los Angeles, CA, and Durham, NC: UCLA-Duke University National Center for Child Traumatic Stress.
Neece, C. L., B. L. Baker, J. Blacher, and K. A. Crnic. 2011. Attention-deficit/hyperactivity disorder among children with and without intellectual disability: An examination across time. Journal of Intellectual Disability Research 55(7):623-635.
Nelson, J. R., G. J. Benner, K. Lane, and B. W. Smith. 2004. Academic achievement of K-12 students with emotional and behavioral disorders. Exceptional Children 71(1):59-73.
Nes, F. T. v. 2009. Young children’s spatial structuring ability and emerging number sense. PhD diss., de Universtiteit Utrecht, Utrecht, The Netherlands.
Neuenschwander, R., M. Röthlisberger, P. Cimeli, and C. M. Roebers. 2012. How do different aspects of self-regulation predict successful adaptation to school? Journal of Experimental Child Psychology 113(3):353-371.
NICHD (Eunice Kennedy Shriver National Institute of Child Health and Human Development) Early Child Care Research Network. 2003. Social functioning in first grade: Associations with earlier home and child care predictors and with current classroom experiences. Child Development 74(6):1639-1662.
———. 2005. A day in third grade: A large-scale study of classroom quality and teacher and student behavior. Elementary School Journal 105(3):305-323.
NRC (National Research Council). 1998. Preventing reading difficulties in young children. Washington, DC: National Academy Press.
———. 2001. Eager to learn: Educating our preschoolers, edited by B. T. Bowman, M. S. Donovan, and M. S. Burns. Washington, DC: National Academy Press.
———. 2009. Mathematics in early childhood: Learning paths toward excellence and equity, edited by C. T. Cross, T. A. Woods, and H. Schweingruber. Washington, DC: The National Academies Press.
NRC and IOM. 2000. From neurons to neighborhoods: The science of early childhood development, edited by J. P. Shonkoff and D. A. Phillips. Washington, DC: National Academy Press.
———. 2009. Depression in parents, parenting, and children: Opportunities to improve identification, treatment, and prevention. Washington, DC: The National Academies Press.
Nunes, T., P. Bryant, and R. Barros. 2012. The development of word recognition and its significance for comprehension and fluency. Journal of Educational Psychology 104(4):959-973.
Nyaradi, A., J. Li, S. Hickling, J. Foster, and W. H. Oddy. 2013. The role of nutrition in children’s neurocognitive development, from pregnancy through childhood. Frontiers in Human Neuroscience 7:97.
O’Connor, E., and K. McCartney. 2007. Examining teacher–child relationships and achievement as part of an ecological model of development. American Educational Research Journal 44(2):340-369.
O’Connor, R. E. 2014. Teaching word recognition effective strategies for students with learning difficulties. New York: Guilford Press.
O’Connor, R. E., and J. R. Jenkins. 1995. Improving the generalization of sound-symbol knowledge: Teaching spelling to kindergarten children with disabilities. Journal of Special Education 29(3):255-275.
O’Connor, R. E., J. R. Jenkins, and T. A. Slocum. 1995. Transfer among phonological tasks in kindergarten: Essential instructional content. Journal of Educational Psychology 2:202-217.
Olson, S. L., and B. Hoza. 1993. Preschool developmental antecedents of conduct problems in children beginning school. Journal of Clinical Child Psychology 22(1):60.
O’Neill, D. K., M. J. Pearce, and J. L. Pick. 2004. Preschool children’s narratives and performance on the peabody individualized achievement test—revised: Evidence of a relation between early narrative and later mathematical ability. First Language 24(2):149-183.
Oreopoulos, P., M. Stabile, R. Walld, and L. L. Roos. 2008. Short-, medium-, and long-term consequences of poor infant health: An analysis using siblings and twins. Journal of Human Resources 43(1):88-138.
Ostad, S. A. 1998. Subtraction strategies in developmental perspective: A comparison of mathematically normal and mathematically disabled children. In Proceedings of the 22nd Conference for the International Group for the Psychology of Mathematics Education. Vol. 3, edited by A. Olivier and K. Newstead. Stellenbosch, South Africa: University of Stellenbosch. Pp. 311-318.
Pagani, L., and S. Messier. 2012. Links between motor skills and indicators of school readiness at kindergarten entry in urban disadvantaged children. Journal of Educational and Developmental Psychology 2(1):95.
Palmer, A., and A. J. Baroody. 2011. Blake’s development of the number words “one,” “two,” and “three.” Cognition and Instruction 29:265-296.
Parish-Morris, J., N. Mahajan, K. Hirsh-Pasek, R. M. Golinkoff, and M. F. Collins. 2013. Once upon a time: Parent–child dialogue and storybook reading in the electronic era. Mind, Brain, and Education 7(3):200-211.
Passolunghi, M. C., B. Vercelloni, and H. Schadee. 2007. The precursors of mathematics learning: Working memory, phonological ability and numerical competence. Cognitive Development 22(2):165-184.
Pearson, P. D., and E. H. Hiebert. 2010. National reports in literacy: Building a scientific base for policy and practice. Educational Researcher 39:286-294.
Perfetti, C. A. 1985. Reading ability. New York: Oxford University Press.
Perfetti, C. A., and L. Hart. 2002. The lexical quality hypothesis. In Precursors of functional literacy. Vol. 11, edited by L. T. Verhoeven, C. Elbro, and P. Reitsma. Amsterdam and Philadelphia, PA: John Benjamins Publishing Company. Pp. 67-86.
Piaget, J., and A. Szeminska. 1952. The child’s conception of number. London: Routledge and Kegan Paul.
Pianta, R. C. 1999. Enhancing relationships between children and teachers. Washington, DC: American Psychological Association.
Pianta, R. C., and M. W. Stuhlman. 2004a. Conceptualizing risk in relational terms: Associations among the quality of child–adult relationships prior to school entry and children’s developmental outcomes in first grade. Educational and Child Psychology 21(1):32-45.
———. 2004b. Teacher–child relationships and children’s success in the first years of school. School Psychology Review 33(3):444-458.
Pianta, R. C., M. S. Steinberg, and K. B. Rollins. 1995. The first two years of school: Teacher–child relationships and deflections in children’s classroom adjustment. Development and Psychopathology 7(02):295-312.
Piazza, M., V. Izard, P. Pinel, D. Le Bihan, and S. Dehaene. 2004. Tuning curves for approximate numerosity in the human intraparietal sulcus. Neuron 44:547-555.
Pinhas, M., S. E. Donohue, M. G. Woldorff, and E. M. Brannon. 2014. Electrophysiological evidence for the involvement of the approximate number system in preschoolers’ processing of spoken number words. Journal of Cognitive Neuroscience 26(9):1891-1904.
Ponitz, C. C., M. M. McClelland, J. S. Matthews, and F. J. Morrison. 2009. A structured observation of behavioral self-regulation and its contribution to kindergarten outcomes. Developmental Psychology 45(3):605-619.
Praet, M., D. Titeca, A. Ceulemans, and A. Desoete. 2013. Language in the prediction of arithmetics in kindergarten and grade 1. Learning and Individual Differences 27:90-96.
Purpura, D. J., L. E. Hume, D. M. Sims, and C. J. Lonigan. 2011. Early literacy and early numeracy: The value of including early literacy skills in the prediction of numeracy development. Journal of Experimental Child Psychology 110:647-658.
Raches, D., and M. M. M. Mazzocco. 2012. Emergence and nature of mathematical difficulties in young children with Barth syndrome. Journal of Developmental and Behavioral Pediatrics 33(4):328-335.
Rachlin, H. 2000. The science of self-control. Cambridge, MA: Harvard University Press.
Raikes, H., B. A. Pan, G. Luze, C. S. Tamis-LeMonda, J. Brooks-Gunn, J. Constantine, L. B. Tarullo, H. A. Raikes, and E. T. Rodriguez. 2006. Mother-child bookreading in low-income families: Correlates and outcomes during the first three years of life. Child Development 77(4):924-953.
Ramirez, G., E. A. Gunderson, S. C. Levine, and S. L. Beilock. 2013. Math anxiety, working memory, and math achievement in early elementary school. Journal of Cognition and Development 14(2):187-202.
Rampersaud, G. C., M. A. Pereira, B. L. Girard, J. Adams, and J. D. Metzl. 2005. Breakfast habits, nutritional status, body weight, and academic performance in children and adolescents. Journal of the American Dietetic Association 105(5):743-760; quiz 761-742.
Raver, C. C. 2013. Targeting self-regulation through intervention: Lessons from RCTS. Paper presented at Society for Research on Educational Effectiveness (SREE), September 27, Washington, DC.
Raver, C. C., and J. Knitzer. 2002. Ready to enter: What research tells policymakers about strategies to promote social and emotional school readiness among three- and four-year-old children. New York: Columbia University, National Center for Children in Poverty, Mailman School of Public Health.
Raver, C. C., S. M. Jones, C. Li-Grining, F. Zhai, M. W. Metzger, and B. Solomon. 2009. Targeting children’s behavior problems in preschool classrooms: A cluster-randomized controlled trial. Journal of Consulting and Clinical Psychology 77(2):302-316.
Raver, C. C., S. M. Jones, C. Li-Grining, F. Zhai, K. Bub, and E. Pressler. 2011. CSRP’s impact on low-income preschoolers’ preacademic skills: Self-regulation as a mediating mechanism. Child Development 82(1):362-378.
Reigosa-Crespo, V., E. González-Alemañy, T. León, R. Torres, R. Mosquera, and M. Valdés-Sosa. 2013. Numerical capacities as domain-specific predictors beyond early mathematics learning: A longitudinal study. PLoS ONE 8(11):e79711.
Rimm-Kaufman, S. E., R. C. Pianta, and M. J. Cox. 2000. Teachers’ judgments of problems in the transition to kindergarten. Early Childhood Research Quarterly 15(2):147-166.
Roberts, T. A. 2008. Home storybook reading in primary or second language with preschool children: Evidence of equal effectiveness for second-language vocabulary acquisition. Reading Research Quarterly 43(2):103-130.
Roebers, C. M., P. Cimeli, M. Röthlisberger, and R. Neuenschwander. 2012. Executive functioning, metacognition, and self-perceived competence in elementary school children: An explorative study on their interrelations and their role for school achievement. Metacognition Learning 7(3):151-173.
Rogoff, B. 1991. Apprenticeship in thinking: Cognitive development in social context. New York: Oxford University Press.
Rogoff, B., and C. Angelillo. 2002. Investigating the coordinated functioning of multifaceted cultural practices in human development. Human Development 45(4):211-225.
Romine, C. B., and C. R. Reynolds. 2005. A model of the development of frontal lobe functioning: Findings from a meta-analysis. Applied Neuropsychology 12(4):190-201.
Romine, C. B., D. Lee, M. E. Wolfe, S. Homack, C. George, and C. A. Riccio. 2004. Wisconsin card sorting test with children: A meta-analytic study of sensitivity and specificity. Archives of Clinical Neuropsychology 19(8):1027-1041.
Rones, M., and K. Hoagwood. 2000. School-based mental health services: A research review. Clinical Child and Family Psychology Review 3(4):223-241.
Roseberry, S., K. Hirsh-Pasek, and R. M. Golinkoff. 2014. Skype me! Socially contingent interactions help toddlers learn language. Child Development 85(3):956-970.
Rossin-Slater, M. 2013. WIC in your neighborhood: New evidence on the impacts of geographic access to clinics. Journal of Public Economics 102:51-69.
Rothbart, M. K., and M. R. Rueda. 2005. The development of effortful control. In Developing individuality in the human brain: A tribute to Michael I. Posner, edited by U. Mayr, E. Awh, and S. Keele. Washington, DC: American Psychological Association. Pp. 167-188.
Rouse, C., J. Brooks-Gunn, and S. McLanahan. 2005. Introducing the issue. The Future of Children 15:5-14.
Rowe, M. L. 2012. A longitudinal investigation of the role of quantity and quality of child-directed speech in vocabulary development. Child Development 83(5):1762-1774.
Royer, H. 2009. Separated at girth: US twin estimates of the effects of birth weight. American Economic Journal: Applied Economics 1(1):49-85.
Rutter, M. 2012. Achievements and challenges in the biology of environmental effects. Proceedings of the National Academy of Sciences of the United States of America 109(Suppl. 2):17149-17153.
Saarni, C. D., D. Mumme, and J. J. Campos. 1998. Emotional development: Action, communication, and understanding. In Handbook of child psychology, 5th ed., edited by W. Damon. New York: Wiley. Pp. 237-309.
Sadler, P. M., and R. H. Tai. 2007. The two high-school pillars supporting college science. Science 317:457-458.
Saffran, J. R. 2003. Statistical language learning: Mechanisms and constraints. Current Directions in Psychological Science 12(4):110-114.
Saffran, J. R., R. N. Aslin, and E. L. Newport. 1996. Statistical learning by 8-month-old infants. Science 274(5294):1926-1928.
Saffran, J. R., E. K. Johnson, R. N. Aslin, and E. L. Newport. 1999. Statistical learning of tone sequences by human infants and adults. Cognition 70(1):27-52.
Samarapungavan, A., H. Patrick, and P. Mantzicopoulos. 2011. What kindergarten students learn in inquiry-based science classrooms. Cognition and Instruction 29(4):416-470.
Sanders, N. J. 2012. What doesn’t kill you makes you weaker: Prenatal pollution exposure and educational outcomes. Journal of Human Resources 47(3):826-850.
Sandhofer, C. M., and L. B. Smith. 1999. Learning color words involves learning a system of mappings. Developmental Psychology 35:668-679.
Sandler, A. D., T. E. Watson, M. Footo, M. D. Levine, W. L. Coleman, and S. R. Hooper. 1992. Neurodevelopmental study of writing disorders in middle childhood. Journal of Developmental and Behavioral Pediatrics 13(1):17-23.
Sandman, C. A., E. P. Davis, C. Buss, and L. M. Glynn. 2012. Exposure to prenatal psychobiological stress exerts programming influences on the mother and her fetus. Neuroendocrinology 95(1):8-21.
Sarama, J., and D. H. Clements. 2009. Early childhood mathematics education research: Learning trajectories for young children. New York: Routledge.
Sarama, J., A. Lange, D. H. Clements, and C. B. Wolfe. 2012. The impacts of an early mathematics curriculum on emerging literacy and language. Early Childhood Research Quarterly 27:489-502.
Saxe, G. B., S. R. Guberman, and M. Gearhart. 1987. Social processes in early number development. Monographs of the Society for Research in Child Development 52(2, Serial #216).
Saxe, R. 2013. The new puzzle of theory of mind development. In Navigating the social world: What infants, children, and other species can teach us, edited by M. R. Banaji and S. A. Gelman. New York: Oxford University Press.
Scarborough, H. S. 2001. Connecting early language and literacy to later reading (dis)abilities: Evidence, theory, and practice. In Handbook of early literacy research, edited by S. B. Neuman and D. K. Dickinson. New York: Guilford Press. Pp. 97-110.
Schleppegrell, M. J. 2003. Grammar for writing: Academic language and the ELD Standards. Santa Barbara, CA: University of California Linguistic Minority Research Institute.
Schneidman, L. A., M. E. Arroyo, S. C. Levine, and S. Goldin-Meadow. 2013. What counts as effective input for word learning? Journal of Child Language 40(3):672-686.
Schoemaker, K., T. Bunte, K. A. Espy, M. Dekovic, and W. Matthys. 2014. Executive functions in preschool children with ADHD and DBD: An 18-month longitudinal study. Developmental Neuropsychology 39(4):302-315.
Schore, J., and A. Schore. 2008. Modern attachment theory: The central role of affect regulation in development and treatment. Clinical Social Work Journal 36(1):9-20.
Schulz, L. E., and E. B. Bonawitz. 2007. Serious fun: Preschoolers engage in more exploratory play when evidence is confounded. Developmental Psychology 43(4):1045-1050.
Secada, W. G. 1992. Race, ethnicity, social class, language, and achievement in mathematics. In Handbook of research on mathematics teaching and learning, edited by D. A. Grouws. Toronto and New York: Macmillan, Maxwell Macmillan Canada, Maxwell Macmillan International. Pp. 623-660.
Semrud-Clikeman, M. 2012. The role of inattention on academics, fluid reasoning, and visual-spatial functioning in two subtypes of ADHD. Applied Neuropsychology. Child 1(1):18-29.
Sénéchal, M., G. Ouellette, and D. Rodney. 2006. The misunderstood giant: On the predictive role of early vocabulary in future reading. In Handbook of early literacy research. Vol. 2, edited by D. K. Dickinson and S. B. Neuman. New York: Guilford Press. Pp. 173-184.
Seo, K.-H., and H. P. Ginsburg. 2004. What is developmentally appropriate in early childhood mathematics education? In Engaging young children in mathematics: Standards for early childhood mathematics education, edited by D. H. Clements, J. Sarama, and A.-M. DiBiase. Mahwah, NJ: Lawrence Erlbaum Associates. Pp. 91-104.
Shayer, M., and M. Adhami. 2010. Realizing the cognitive potential of children 5-7 with a mathematics focus: Post-test and long-term effects of a 2-year intervention. British Journal of Educational Psychology 80(3):363-379.
Shonkoff, J. P., W. T. Boyce, and B. S. McEwen. 2009. Neuroscience, molecular biology, and the childhood roots of health disparities: Building a new framework for health promotion and disease prevention. Journal of the American Medical Association 301(21):2252-2259.
Shuai, L., R. C. Chan, and Y. Wang. 2011. Executive function profile of Chinese boys with attention-deficit hyperactivity disorder: Different subtypes and comorbidity. Archives of Clinical Neuropsychology 26(2):120-132.
Siegel, L. S., and S. Mazabel. 2013. Basic cognitive processes and reading disabilities. In Handbook of learning disabilities, edited by H. L. Swanson, K. R. Harris, and S. Graham. New York: Guilford Press. Pp. 186-213.
Silver, R. B., J. R. Measelle, J. M. Armstrong, and M. J. Essex. 2005. Trajectories of classroom externalizing behavior: Contributions of child characteristics, family characteristics, and the teacher–child relationship during the school transition. Journal of School Psychology 43(1):39-60.
Simmons, F. R., C. Willis, and A.-M. Adams. 2012. Different components of working memory have different relationships with different mathematical skills. Journal of Experimental Child Psychology 111(2):139-155.
Skinner, E. A., and M. J. Belmont. 1993. Motivation in the classroom: Reciprocal effects of teacher behavior and student engagement across the school year. Journal of Educational Psychology 85(4):571-581.
Slaughter, V., S. Itakura, A. Kutsuki, and M. Siegal. 2011. Learning to count begins in infancy: Evidence from 18 month olds’ visual preferences. Proceedings: Biological Sciences 278(1720):2979-2984.
Smiley, P. A., and C. S. Dweck. 1994. Individual differences in achievement goals among young children. Child Development 65(6):1723-1743.
Smith, M. W., and D. K. Dickinson. 1994. Describing oral language opportunities and environments in Head Start and other preschool classrooms. Early Childhood Research Quarterly 9(3-4):345-366.
Snow, C. E., and P. Uccelli. 2009. The challenge of academic language. In Cambridge handbook of literacy, edited by D. R. Olson and N. Torrance. Cambridge, MA: Cambridge University Press. Pp. 112-133.
Snow, C. E., P. O. Tabors, and D. K. Dickinson. 2001. Language development in the preschool years. In Beginning literacy with language: Young children learning at home and school, edited by D. K. Dickinson and P. O. Tabors. Baltimore, MD: Paul H. Brookes Publishing Co. Pp. 1-25.
Spelke, E. S., and K. D. Kinzler. 2007. Core knowledge. Developmental Science 10(1):89-96.
Stanovich, K. E., and L. S. Siegel. 1994. Phenotypic performance profile of children with reading disabilities. Journal of Educational Psychology 86:24-53.
Starr, A., M. E. Libertus, and E. M. Brannon. 2013. Infants show ratio-dependent number discrimination regardless of set size. Infancy 1-15.
Steacy, L. M., J. R. Kirby, R. Parrila, and D. L. Compton. 2014. Classification of double deficit groups across time: An analysis of group stability from kindergarten to second grade. Scientific Studies of Reading 18(4):255-273.
Steffe, L. P., and R. Tzur. 1994. Interaction and children’s mathematics. Journal of Research in Childhood Education 8(2):99-116.
Sternberg, R. 1985. Beyond IQ. Cambridge, MA: Cambridge University Press.
Stevenson, H. W., and R. S. Newman. 1986. Long-term prediction of achievement and attitudes in mathematics and reading. Child Development 57:646-659.
Stipek, D., S. Recchia, and S. McClintic. 1992. Self-evaluation in young children. Monographs of the Society for Research in Child Development 57(1):1-98.
Strouse, G. A., K. O’Doherty, and G. L. Troseth. 2013. Effective coviewing: Preschoolers’ learning from video after a dialogic questioning intervention. Developmental Psychology 49(12):2368-2382.
Szűcs, D., A. Devine, F. Soltesz, A. Nobes, and F. Gabriel. 2014. Cognitive components of a mathematical processing network in 9-year-old children. Developmental Science 17(4):506-524.
Taras, H. 2005. Nutrition and student performance at school. Journal of School Health 75(6):199-213.
Thayer, Z. M., and C. W. Kuzawa. 2014. Early origins of health disparities: Material deprivation predicts maternal evening cortisol in pregnancy and offspring cortisol reactivity in the first few weeks of life. American Journal of Human Biology 26(6):723-730.
Thompson, R. A. 2006. The development of the person: Social understanding, relationships, self, conscience. In Handbook of child psychology, 6th ed., Vol. 3, edited by W. Damon and R. M. Lerner. Hoboken, NJ: John Wiley & Sons. Pp. 24-98.
———. 2008. Early attachment and later development: Familiar questions, new answers. In Handbook of attachment: Theory, research, and clinical applications, 2nd ed., edited by J. Cassidy and P. R. Shaver. New York: Guilford Press. Pp. 348-365.
———. 2012. Whither the preconventional child? Toward a life-span moral development theory. Child Development Perspectives 6(4):423-429.
———. 2013. Attachment theory and research: Precis and prospect. In Oxford handbook of developmental psychology. Vol. 2, edited by P. Zelazo. New York: Oxford University Press. Pp. 191-216.
———. 2014. Stress and child development. The Future of Children 24(1):41-59.
———. 2015. The development of virtue: A perspective from developmental psychology. In Cultivating virtue: Perspectives from philosophy, theology, and psychology, edited by N. E. Snow. New York: Oxford University Press.
———. in press. Early attachment and later development: Reframing the questions. In Handbook of attachment, 3rd ed., edited by J. Cassidy and P. R. Shaver. New York: Guilford Press.
Thompson, R. A., and R. Goodvin. in press. Social support and developmental psychopathology. In Developmental psychopathology, 3rd ed., edited by D. Cicchetti. New York: John Wiley & Sons.
Thompson, R. A., D. J. Laible, and L. L. Ontai. 2003. Early understandings of emotion, morality, and self: Developing a working model. Advances in Child Development and Behavior 31:137-171.
Thomson, S., K. Rowe, C. Underwood, and R. Peck. 2005. Numeracy in the early years: Project good start. Camberwell, Victoria, Australia: Australian Council for Educational Research.
Toll, S. W. M., S. Van der Ven, E. Kroesbergen, and J. E. H. Van Luit. 2010. Executive functions as predictors of math learning disabilities. Journal of Learning Disabilities 20(10):1-12.
Tomasello, M., M. Carpenter, J. Call, T. Behne, and H. Moll. 2005. Understanding and sharing intentions: The origins of cultural cognition. Behavioral and Brain Sciences 28(5):675-691; discussion 691-735.
Tomlinson, H. B. 2014. An overview of development in the primary grades. In Developmentally appropriate practice, edited by C. Copple, S. Bredekamp, D. G. Koralek, and K. Charner. Washington, DC: National Association for the Education of Young Children. Pp. 9-38.
Trentacosta, C. J., and C. E. Izard. 2007. Kindergarten children’s emotion competence as a predictor of their academic competence in first grade. Emotion 7(1):77-88.
Trick, L. M., and Z. W. Pylyshyn. 1994. Why are small and large numbers enumerated differently?: A limited-capacity preattentive stage in vision. Psychological Review 101:80-102.
Troseth, G. L., M. M. Saylor, and A. H. Archer. 2006. Young children’s use of video as a source of socially relevant information. Child Development 77(3):786-799.
Tseng, V., and E. Seidman. 2007. A systems framework for understanding social settings. American Journal of Community Psychology 39(3-4):217-228.
Tunmer, W., and W. Hoover. 1993. Components of variance models of language-related factors in reading disability: A conceptual overview. In Reading disabilities: Diagnosis and component processes, edited by R. J. Joshi and C. K. Leong. Dordrecht, The Netherlands: Kluwer. Pp. 135-173.
Tymms, P., and C. Merrell. 2011. ADHD and academic attainment: Is there an advantage in impulsivity? Learning and Individual Differences 21(6):753-758.
Uccelli, P., L. Hemphill, B. A. Pan, and C. Snow. 2006. Conversing with toddlers about the nonpresent: Precursors to narrative development in two genres. In Child psychology: A handbook of contemporary issues, 2nd ed., edited by L. Balter and C. S. Tamis-LeMonda. New York: Taylor & Francis. Pp. 215-237.
Ulrich-Lai, Y. M., and J. P. Herman. 2009. Neural regulation of endocrine and autonomic stress responses. Nature Reviews: Neuroscience 10(6):397-409.
U.S. Department of Education Office for Civil Rights. 2014. Civil rights data collection: Data snapshot (school discipline) (Issue brief no. 1). http://www2.ed.gov/about/offices/list/ocr/docs/crdc-discipline-snapshot.pdf (accessed February 9, 2015).
Valiente, C., N. Eisenberg, R. Haugen, T. L. Spinrad, C. Hofer, J. Liew, and A. S. Kupfer. 2011. Children’s effortful control and academic achievement: Mediation through social functioning. Early Education & Development 22(3):411-433.
Van den Heuvel-Panhuizen, M. 1996. Assessment and realistic mathematics education. Utrecht, The Netherlands: Utrecht University, Freudenthal Institute.
Van der Ven, S. H. G., E. H. Kroesbergen, J. Boom, and P. P. M. Leseman. 2012. The development of executive functions and early mathematics: A dynamic relationship. British Journal of Educational Psychology 82(1):100-119.
van Ijzendoorn, M. H., J. Dijkstra, and A. G. Bus. 1995. Attachment, intelligence, and language: A meta-analysis. Social Development 4(2):115-128.
van Kleek, A. 2004. Fostering preliteracy development via storybook-sharing interactions: The cultural context of mainstream family practices. In Handbook of language and literacy: Development and disorders, edited by C. A. Stone, E. R. Silliman, B. Ehren, and K. Apel. New York: Guilford Press. Pp. 175-208.
Van Luit, J. E. H., and B. A. M. Van de Rijt. 1998. Effectiveness of the additional early mathematics program for teaching children early mathematics. Instructional Science 26:337-358.
Vasilyeva, M., and H. Waterfall. 2011. Variability in language development: Relation to socioeconomic status and environmental input. In Handbook of early literacy research, Vol. 3, edited by S. B. Neuman and D. K. Dickinson. New York: Guilford Press. Pp. 36-48.
Vasilyeva, M., J. Huttenlocher, and H. Waterfall. 2006. Effects of language intervention on syntactic skill levels in preschoolers. Developmental Psychology 42(1):164-174.
Vellutino, F. R., W. E. Tunmer, J. J. Jaccard, and R. Chen. 2007. Components of reading ability: Multivariate evidence for a convergent skills model of reading development. Scientific Studies of Reading 11(1):3-32.
Vest, J. R., T. K. Catlin, J. J. Chen, and R. C. Brownson. 2002. Multistate analysis of factors associated with intimate partner violence. American Journal of Preventive Medicine 22(3):156-164.
Vieillevoye, S., and N. Nader-Grosbois. 2008. Self-regulation during pretend play in children with intellectual disability and in normally developing children. Research in Developmental Disabilities 29(3):256-272.
Vitiello, V. E., D. B. Greenfield, P. Munis, and J. L. George. 2011. Cognitive flexibility, approaches to learning, and academic school readiness in head start preschool children. Early Education & Development 22(3):388-410.
von Suchodoletz, A., and C. Gunzenhauser. 2013. Behavior regulation and early math and vocabulary knowledge in German preschool children. Early Education & Development 24(3):310-331.
Vukovic, R. K. 2012. Mathematics difficulty with and without reading difficulty: Findings and implications from a four-year longitudinal study. Exceptional Children 78:280-300.
Vukovic, R. K., and N. K. Lesaux. 2013. The language of mathematics: Investigating the ways language counts for children’s mathematical development. Journal of Experimental Child Psychology 115(2):227-244.
Vygotsky, L. S. 1978. Mind in society: The development of higher psychological processes, edited by M. Cole, V. John-Steiner, S. Scribner, and E. Souberman. Cambridge, MA: Harvard University Press.
———. 1986. Thought and language. Cambridge, MA: MIT Press.
Wagner, R. K., J. K. Torgesen, P. Laughon, K. Simmons, and C. A. Rashotte. 1993. Development of young readers’ phonological processing abilities. Journal of Educational Psychology 85:83-103.
Wagner, S. W., and J. Walters. 1982. A longitudinal analysis of early number concepts: From numbers to number. In Action and thought, edited by G. E. Forman. New York: Academic Press. Pp. 137-161.
Wang, J., and K. C. Barrett. 2013. Mastery motivation and self-regulation during early childhood. In Handbook of self-regulatory processes in development new directions and international perspectives, edited by K. C. Barrett, N. A. Fox, G. A. Morgan, D. J. Fidler, and L. A. Daunhauer. Boca Raton, FL: Taylor & Francis Press. Pp. 337-380.
Wanless, S. B., M. M. McClelland, S. L. Tominey, and A. C. Acock. 2011. The influence of demographic risk factors on children’s behavioral regulation in prekindergarten and kindergarten. Early Education & Development 22(3):461-488.
Warneken, F., and M. Tomasello. 2006. Altruistic helping in human infants and young chimpanzees. Science 311(5765):1301-1303.
———. 2007. Helping and cooperation at 14 months of age. Infancy 11(3):271-294.
Weiner, B. 1986. An attributional theory of motivation and emotion. New York: Springer-Verlag.
Weisleder, A., and A. Fernald. 2013. Talking to children matters: Early language experience strengthens processing and builds vocabulary. Psychological Science 24(11):2143-2152.
Weizman, Z. O., and C. E. Snow. 2001. Lexical input as related to children’s vocabulary acquisition: Effects of sophisticated exposure and support for meaning. Developmental Psychology 37(2):265-279.
Wellman, H. M. 2011. Developing a theory of mind. In The handbook of childhood cognitive development, 2nd ed., edited by U. C. Goswami. Malden, MA: Wiley-Blackwell. Pp. 258-284.
Wellman, H. M., and J. D. Woolley. 1990. From simple desires to ordinary beliefs: The early development of everyday psychology. Cognition 35(3):245-275.
Welsh, J. A., R. L. Nix, C. Blair, K. L. Bierman, and K. E. Nelson. 2010. The development of cognitive skills and gains in academic school readiness for children from low-income families. Journal of Educational Psychology 102(1):43-53.
West, K. K., M. B. L., and K. K. A. 2013. Mother–child attachment and cognitive performance in middle childhood: An examination of mediating mechanisms. Early Childhood Research Quarterly 28(2):259-270.
Whitehurst, G. J., F. L. Falco, C. J. Lonigan, F. J. E., B. D. DeBarshe, M. C. Valdex-Menchaca, and M. Caulfield. 1988. Accelerating language development through picture book reading. Developmental Psychology 24(4):552-559.
Wigfield, A., J. S. Eccles, U. Schiefele, R. W. Roeser, and P. Davis-Kean. 2006. Development of achievement motivation. In Handbook of child psychology, 6th ed., Vol. 3, edited by W. Damon and R. M. Lerner. New York: Wiley. Pp. 933-1002.
Willoughby, M. T., J. Kupersmidt, M. Voegler-Lee, and D. Bryant. 2011. Contributions of hot and cool self-regulation to preschool disruptive behavior and academic achievement. Developmental Neuropsychology 36(2):161-180.
Wright, R. J. 1994. A study of the numerical development of 5-year-olds and 6-year-olds. Educational Studies in Mathematics 26:25-44.
Wulfert, E., J. A. Block, E. Santa Ana, M. L. Rodriguez, and M. Colsman. 2002. Delay of gratification: Impulsive choices and problem behaviors in early and late adolescence. Journal of Personality 70(4):533-552.
Wynn, K. 1992a. Addition and subtraction by human infants. Nature 358(6389):749-750.
———. 1992b. Children’s acquisition of the number words and the counting system. Cognitive Psychology 24:220-251.
Wynn, K., P. Bloom, and W.-C. Chiang. 2002. Enumeration of collective entities by 5-month-old infants. Cognition 83:B55-B62.
Xu, F., and S. Denison. 2009. Statistical inference and sensitivity to sampling in 11-month-old infants. Cognition 112(1):97-104.
Yoon, J. M., M. H. Johnson, and G. Csibra. 2008. Communication-induced memory biases in preverbal infants. Proceedings of the National Academy of Sciences of the United States of America 105(36):13690-13695.
Young, C. B., S. S. Wu, and V. Menon. 2012. The neuro-developmental basis of math anxiety. Psychological Science Online First 23(5):10.1177/0956797611429134.
Yow, W. Q., and E. M. Markman. 2011a. Bilingual children’s use of paralinguistic cues to determine emotion in speech. Bilingualism: Language and Cognition 14(4):562-569.
———. 2011b. Young bilingual children’s heightened sensitivity to referential cues. Journal of Cognition and Development 12(1):12-31.
Yuill, N. 1996. A funny thing happened on the way to the classroom: Jokes, riddles, and metalinguistic awareness in understanding and improving poor comprehension in children. In Reading comprehension difficulties: Processes and intervention, edited by C. Cornoldi and J. Oakhill. Mahwah, NJ: Lawrence Erlbaum Associates. Pp. 193-220.
Zeanah, C. H. 2009. Handbook of infant mental health. New York and London: Guilford Press.
Zelazo, P. D., and S. M. Carlson. 2012. Hot and cool executive function in childhood and adolescence: Development and plasticity. Child Development Perspectives 6(4):354-360.
Zelazo, P. D., and K. E. Lyons. 2012. The potential benefits of mindfulness training in early childhood: A developmental social cognitive neuroscience perspective. Child Development Perspectives 6(2):154-160.
Zelazo, P. D., S. Jacques, J. A. Burack, and D. Frye. 2002. The relation between theory of mind and rule use: Evidence from persons with autism-spectrum disorders. Infant and Child Development 11:171-195.
Zelazo, P. D., U. Müller, D. Frye, S. Marcovitch, G. Argitis, J. Boseovski, J. K. Chiang, D. Hongwanishkul, B. V. Schuster, A. Sutherland, and S. M. Carlson. 2003. The development of executive function in early childhood. Monographs of the Society for Research in Child Development 68(3), Serial No. 274.
Zentall, S. R., and B. J. Morris. 2010. “Good job, you’re so smart”: The effects of inconsistency of praise type on young children’s motivation. Journal of Experimental Child Psychology 107(2):155-163.
Zero to Three. 2005. Diagnostic classification of mental health and developmental disorders of infancy and early childhood. Washington, DC: Zero to Three Press.
Ziegler, J. C., and U. Goswami. 2005. Reading acquisition, developmental dyslexia, and skilled reading across languages: A psycholinguistic grain size theory. Psychonomic Bulletin 131:3-29.
Zimmerman, B. J. 2002. Achieving academic excellence: A self-regulatory perspective. In The pursuit of excellence through education, edited by M. Ferrar. Mahwah, NJ: Lawrence Erlbaum Associates. Pp. 85-110.
Zimmerman, E., and S. H. Woolf. 2014. Understanding the relationship between education and health. Discussion paper. http://www.iom.edu/understandingtherelationship (accessed March 23, 2015).
Zins, J. E., M. R. Bloodworth, R. P. Weissberg, and H. J. Walberg. 2007. The scientific base linking social and emotional learning to school success. Journal of Educational and Psychological Consultation 17(2-3):191-210.
Zipke, M., L. C. Ehri, and H. S. Cairns. 2009. Using semantic ambiguity instruction to improve third graders’ metalinguistic awareness and reading comprehension: An experimental study. Reading Research Quarterly 44(3):300-321.
Zucker, T. A., S. Q. Cabell, L. M. Justice, J. M. Pentimonti, and J. N. Kaderavek. 2013. The role of frequent, interactive prekindergarten shared reading in the longitudinal development of language and literacy skills. Developmental Psychology 49(8):1425-1439.
This page intentionally left blank.