Scientific Research in Education (2002) / Chapter Skim
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2 Accumulation of Scientific Knowledge
2 Accumulation of Scientific Knowledge
Pages 28-49
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The prevailing view is that findings from education research studies are of low quality and are endlessly contested the result of which is that no consensus emerges about anything. We argue in Chapter 1 that this skepticism is not new.
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We trace the history of three productive lines of inquiry related to education as "existence proofs" to support this assertion and to convey the promise for future investments in scientific education research. What is needed is more and better scientific research of this kind on education.
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ILLUSTRATIONS OF KNOWLEDGE ACCUMULATION In this section we provide examples of how scientific knowledge has accumulated in four areas. First, we describe the progression of scientific insight in differential gene activation, a line of inquiry in molecular biology that began 50 years ago and laid the foundation for today's groundbreaking human genome project.
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with no attempt to , 1 conceptualize the physical mechanism by which the trait was passed on from generation to generation (Derry, 1999~. By the time Mendel's work became known to the scientific world, cell biologists with newly improved microscopes had identified the threadlike structures in the nuclei of cells called chromosomes, which soon became known through experiments as the carriers of hereditary information.
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This work has led to new knowledge in molecular biology that affects our understanding of both cell development and of genetic disease. The work of countless molecular biologists in the past 50 years has resulted in the recent publication of the linear "map" of the entire human genome, from which, one day perhaps many years in the future all genetically influenced diseases and all developmental steps may be deduced.
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Test Re/iabi/ity The notion of test reliability the consistency of scores produced by a test grew out of the recognition that test scores could differ from one ACCU M U LATION OF SCI ENTIFIC KNOWLEDG E 33
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For instance, remembering an answer to a particular question when the same test was administered twice meant that "memory" contributed to a respondent's consistency or true score, but not so upon taking parallel forms of the test. Moreover, Cronbach, Guttman, Thorndike, and others recognized that test performance is more complex than what a single trait could predict, and that there can be many sources of measurement error including inconsistency due to different occasions, different test forms, different test administrations, and the like.
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, drawing on advances in statistical theory (especially Fisher's variance partitioning and random components of variance theory) incorporated this understanding into a framework that accounted, simultaneously, for multiple sources of measurement error.
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discussed factors that distort measured correlation coefficients: these included ignoring variation in the ages of the children tested as well as other factors that affect both quantities being correlated and the correlation of quantities subject to substantial measurement error. The "Army Alpha" test was developed in 1917 for use in classification and assignment curing world War I
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These mathematical models then evolved to classical reliability theory with a single underlying trait. The mathematical models developed in close conjunction with the increasingly more complicated uses of tests and more complex demands made on the inferences based on them.
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. Phonological Awareness and Early Reading Skills A third example traces the history of inquiry into the role of phonological awareness, alphabetic knowledge, and other beginning reading skills.
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, the view of written language as scaffolded on oral language gradually took hold despite criticisms that the research was simplistic and reductionistic (e.g., Satz and Fletcher, 1980~. In the 1980s, research expanded into areas that involved the development of phonological awareness and reading capabilities, ultimately leading to large-scale longitudinal studies showing that phonological awareness could be measured reliably in young children and that its development preceded the onset of word recognition skills (Wagner,Torgesen, and Rashotte,1994~.
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These studies overturned prevailing notions about reading disability that reported non-normality and implied qualitative differences between good and poor readers that had led to theories specific to the poor reader; rather, these findings indicated that the same theory could be used to explain good and poor reading. The prevalence studies also showed that most poor readers had word recognition difficulties and that the prevalence of reading failure was shockins~lv high (Fletcher and Lyon, 1998~.
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Functional brain imaging studies possible only over the past few years have identified neural networks that support phonological processing and word recognition. These findings have been replicated in several laboratories using different neuroimaging methods and reflect more than 20 years of research to identify reliable neural correlates of reading disability (Eden and Zeffiro,1998~.
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Equality of Educational Opportunity (see also Socks et al., 1972) , social science research began to document the relative absence of direct schooling effects on student achievement in comparison with the effects of students' background characteristics.
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that resources were viewed as a necessary but not sufficient condition for productive education, and educational experiences were viewed as the mechanism through which resources are transformed into student outcomes. It may be that resources do matter when translated into productive learning experiences for students.
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Therefore, an alternative approach is concentrating research efforts on understanding how different incentive structures affect student outcomes (Hanushek et al., 1994~. Both of these avenues of research build on existing evidence.
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In contrast, in education research technical achievements are often ignored, and research findings tend to be dismissed as irrelevant or (sometimes vehemently) discredited through public advocacy campaigns when they do not comport with conventional wisdom or ideological views.
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that seems to lead to self-correction as debates and resolutions occur and new methods, empirical findings, or theories emerge to shed light on and change fundamental perceptions about an issue (e.g., Shavelson, 1 988; Weiss, 1 980~. A second characteristic of knowledge accumulation is that it is contested.
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conclude that the indirect effects of resources on student outcomes are both small and, as policy instruments, difficult to manage. For them, establishing ACCU M U LATION OF SCI ENTIFIC KNOWLEDG E 47
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Application of these new measurement techniques, in turn, produces new empirical evidence, and so the cycle continues. This cycle is characteristic of the natural sciences, as illustrated in our example of differential gene activation, and also evident in social science in the measurement of economic and social indicators (deNeufville, 1975; Sheldon, 1975)
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Rather, scientific findings interact with differing views in practical and political arenas (Lindblom andWodehouse, 1993; Feldman and March, 1981;Weiss, 1998b, 1999; Bane, 2001; Reimers and McGinn, 1997~. The scientist discovers the basis for what is possible.
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