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Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Appendix C

Submitted Cases

Workshop registrants were invited to submit case examples on K–12 data science education for one of three topic areas:

Examples of challenges educators face when working to enact data science activities;

Examples that reflect successful real-world application of data science with youth; or

Examples of an existing software/tool/technology that has been used and could be adopted to supplement data science learning.

Cases were featured during the workshop. A summary of the cases can be found in Table C-1.1

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1 The full cases are available at https://www.nationalacademies.org/event/09-13-2022/docs/DC43B789C2136F62C030FAE3E7A8EB58C45EF823FACF

Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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TABLE C-1 Submitted Cases by Case Type

Case Type: Challenges Educators Face
Case Title Description Relevant Links/Citations
Access to Data Science Courses in K–12 Data science courses are not widely taught at the high school level. Partnerships between colleges, nonprofits, and high schools will need to be established to support, implement, and sustain high-quality professional development, and course development. What are examples of successful partnerships to facilitate data science teaching and learning at the K–12 levels?
Lack of Data Science Fundamentals Learning the fundamentals of how data are collected, analyzed, and communicated starts with a solid foundation in the science and engineering practices outlined in the Framework for Science Education. One challenge educators face when teaching data science is the lack of fundamentals.
Connecting an abstract concept like data science to real-world applications can be a huge barrier when it comes to keeping students engaged. Many science teachers struggle to engage students actively and feel stuck using slides, lectures, and traditional science experiments to collect data that can be time-consuming to set up and maintain.
Teachers need time to be teachers, and students feel more engaged when they connect data to the world in which they live.
Gulf of Maine Research Institute Inquiry-, interest-, and place-based pedagogies typically are described as highly engaging for learners whether in the classroom or in an informal learning context like 4-H. Yet these approaches pose challenges to learners having a data-rich experience. Data relevant to explorations of my place or my question can be difficult to secure, too complicated for the developmental stage of the learner, or simply may not exist. Where it exists, data provenance may not be obvious and may therefore hide important information from the novice data scientist. This produces what we call “data dead ends” and frustration—
Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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independent learners design interesting questions to pursue but become exhausted at the task of finding and accessing data that might inform their investigations. Where data exist and are usable, many tools don’t support authentic work with the data by novices.
Gulf of Maine Research Institute In response to dramatic media reports about massive die-offs of moose, a class in Aroostook, ME, decides to investigate the impact of ticks on Maine moose populations and its link to climate change. They need to secure and visualize state data on both tick and moose populations alongside precipitation and temperature data over a long enough period to distinguish a climate-driven trend from an anomalous year/period or natural variability. Tick populations also turn out to be related to several key invasive plant species (e.g., barberry) that are tick “magnets.” Ideally, they would do so in a tool that allows them to mess around with the data in an inquiry-based fashion. And, like most climate phenomena, they need a tool that allows them to view change over both space and time.
Need for High-Quality, Consistent Data Science Training for Practitioners Regardless of how hard we work at designing clear, important, and meaningful district and state science and math standards and guidelines that incorporate data science skill development, what actually ends up happening in the classroom may be vastly different than our intentions. The amount of training on data science that K–12 teachers have and their confidence with teaching it has immense variability, even within a single school, mine included. Many teachers thus either avoid doing much meaningful data science with their students or they transfer their own misunderstandings onto their students by doing it wrong.
Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
×
Case Type: Challenges Educators Face
Case Title Description Relevant Links/Citations
Differences Between Data Science and Statistics—Need for High-Quality, Consistent Data Science Training for Practitioners Many educators don’t receive great training on how to implement a data science curriculum, so they fall back on old practices and rely on their knowledge of statistics to push them through. This usually leads to students being in courses labeled as data science but taught as statistics, which is to say they omit the computer science aspects that are so necessary to the implementation of a data science foundation. To this point, many math educators don’t think they belong in the computer science field (many also don’t believe they belong in the statistics field, strangely). However, in California, you need a math credential to teach computer science, so the disconnect is probably as a result of their preservice courses (many of which lack a computer science strand).
Lack of Time, Physical Resources, and Staff Are a Hurdle for Many Implementing Data Science Courses My colleagues and I led a statewide professional development course—“What Is Data Science?”—for high school math teachers last year. During this course the teachers identified challenges they face when working to incorporate data science activities. Some of these challenges included time restraints—including the excessive number of math standards they are expected to cover; scheduling restraints—many teachers in Idaho teach in rural schools where there is often only one or two high school math teachers for the entire school; the infeasibility of adding in a full data science class; their own fear or lack of time to learn a new software/tool/technology in order to teach data science—we are thinking as a state to offer a follow-up professional development course that is intentionally designed to help 6–12 educators get over this hurdle; and explicit connections between data science and mathematics content standards—many math teachers want to know they are still teaching math when they are leading data science activities.
Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Use of Excel vs. SPSS for Data Analysis In science education research, students usually analyze data using the Excel program; because of the advantages of this program, students can detect and design the data analysis formulas used. However, when using SPSS, students can only output but do not know how to process data analysis in SPSS. This is an obstacle that is often faced by students when the results of the analysis are presented, where they are not able to explain how the data analysis process is carried out.

As an answer to these problems, I often suggest to students that before using SPSS, first read the SPSS Manual to get an in-depth picture of the contents of the SPSS program, especially related to the use of statistical formulas in SPSS.
Difficulties Finding Clean, Multivariate Datasets With regard to teaching science, it is very challenging to find relatively clean, multivariate datasets that allow for rich exploration of science topics that are directly relevant to what teachers are currently teaching. That is, in order to incorporate data science into an already over-full, year-long curriculum, teachers need to access multivariate datasets that will enable their students to learn the science by working with the data. Such datasets are difficult to find and take a lot of digging to find—even when they exist.
Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
×
Case Type: Challenges Educators Face
Case Title Description Relevant Links/Citations
Judging in K–12 Science and Engineering Fairs Judges in K–12 science and engineering fairs often find students have very poor grasps of basic statistics and probability, and nearly zero comprehension of error sources and variability. We cannot leave statistics to a high school AP statistics course! Data science basics need to include the basics of these important mathematical concepts, and teachers need support to do that inclusion. More data are NOT equal to better data, necessarily; evaluating data quality is similar in concept to evaluating bibliographic sources.
Enhancing Statistics Teacher Education through E-Modules [ESTEEM] (NSF DUE 1625713) The ESTEEM project began in 2016 to develop teacher education curriculum materials designed to support secondary (grades 6–12) mathematics teachers to learn to teach statistics. The project’s focus on the statistical education of teachers was due to increased expectations for students to learn statistics at the secondary level that needed to be matched by enhancement and prioritization of statistics teacher education. The ESTEEM project packages materials into e-modules. Their modular format makes their use more flexible for faculty, allowing them to choose the modules that work best in their teacher preparation program. The modules are easily imported into learning management systems [LMSs] for adaptation and integration with other course materials. Faculty can access the entire set of ESTEEM-developed modules at the ESTEEM portal, available through free registration. At the ESTEEM portal, faculty can download a version of the complete set of materials in an easy-to-use format that can be imported into Canvas, Moodle, or Blackboard. We also offer a Common Cartridge format that can be imported into other LMSs. All materials are distributed using the Creative Commons Attribution Noncommercial Share-Alike 4.0 license. http://go.ncsu.edu/esteem
https://www.fi.ncsu.edu/projects/esteem
Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Implementing Data Science Outside of Mathematics Curriculum Data science is interdisciplinary in nature. However, the core curriculum places most of the work in developing students’ understanding of data within mathematics. In taking this approach, mathematical techniques tend to get foregrounded, and other aspects of critical data literacy remain underdeveloped. To better prepare students as citizens in an increasingly data-driven world, we need students to have opportunities to engage with data across the curriculum. This raises the challenge of better understanding how data science education intersects with subject matter taught across the curriculum, the pedagogical content knowledge teachers from different disciplines draw upon as they integrate data investigations with existing curricula, and the resources teachers need to do this.
Hesitation in Students Surrounding Data Science Data science is so new that students do not have a fundamental understanding of what it is, or even who uses it, which makes diving into the curriculum difficult. Students also have a false sense of the education that is necessary to pursue a career in a data field because they are under the false assumption that anything that is in math must require a college education and a great deal of higher math, which scares students and creates hesitation around the field.
I began my year with two activities during the first week of school:
Introduction to Data Literacy Skills: Students worked in groups to learn about a skill and made a mini-poster explaining the skill in their own words. Students had an immediate introduction to what data science and data literacy are though this activity and were able to easily transition into the curriculum with a basic background of what this class would cover. Career Project: Student pairs chose a career to research that uses data science and were asked to find information about that career, including education requirements and average salary. Students then presented their findings to their peers.
The results were very positive. Students realized from the first week of school that there are careers they never heard of that are in high demand right now that don’t require extensive education after high school. Students became excited about the possibility of opening up new future opportunities. Share-Alike 4.0 license.
Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Case Type: Challenges Educators Face
Case Title Description Relevant Links/Citations
Using Data to Answer Questions That Are Important to Students One goal (an important one, in my opinion) in data science education is for students to pose investigative questions that are important to them and to use data to answer (at least partially) their questions. This poses big challenges to the instructor, who must potentially grade or assess many different projects, and must have sufficient confidence in their own knowledge of data analysis to assist students to follow many different pathways, without knowing ahead of time what the “right” answer is. Even when working with the same dataset, different students might pursue different approaches. How do we prepare instructors so that they can support students to follow valid approaches without teaching the misconception that “you can say anything with data”?
EDC’s Oceans of Data Institute The Education Development Center’s (EDC’s) Oceans of Data Institute has explored how educators can integrate the use of authentic data into their teaching practice. Topic areas include Earth science, biology, social studies, and civil engineering. In all cases, students work with “CLIP” data, which are complex, large, interactively accessed using a computer, and professionally collected. This approach models real-world situations and helps to build critical “habits of mind” when analyzing and interpreting the data. https://edc.org/oceans-data-institute
One of the recurring challenges in having students work with CLIP data has to do with the nature of the interface itself, which typically has to do three things simultaneously.
Allow students to explore and manipulate the data in various forms, including tables, graphs, and maps;
Allow space for written instructions, prompts, or background; and Give students a way to capture their work—ideally in such a way that the instructor can review it and provide feedback.
Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Previous solutions have included the following:
Use of a bespoke Web-based interface and Concord Consortium’s CODAP interface.
Use of Concord’s LARA framework, which easily integrates either CODAP or SageModeler interactive data windows.
Integration of CODAP and SageModeler data windows into Canvas. Canvas would allow integrations of these tools into a single page, but website navigation takes up much of the screen real estate. Early feedback from undergraduate students suggests that they are comfortable navigating multiple tabs/windows to work around this issue; they are uncertain how younger students will respond to this workaround.
EMBEDS: Exploring the Mathematics of Biological Ecosystems with Data Science The EMBEDS project at TERC and the University of Colorado, Boulder is integrating “data excursions” into a ninth-grade biology unit on the changing ecology of the Serengeti by using the same datasets scientists used to understand why the populations of both wildebeest and buffalo increased rapidly and substantially between 1960 and 1975.
Challenges:
Scientific datasets from several decades ago are not always available and require a lot of work to make them usable by current-day students.
Difficulties in techniques for measuring ecological quantities.
Students hypothesized that an increased food source could drive population growth, but we were uncertain how to measure the food supply in the Serengeti—an issue scientists also had—so we used rainfall as a proxy. Data indicated that rainfall did not increase during the period of population growth, urging the students to draw a conclusion contradictory to their hypothesis and two challenges arose:
Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Case Type: Challenges Educators Face
Case Title Description Relevant Links/Citations
Understanding how data can indicate NO relationship between two variables, and
Reasoning about food availability through the proxy quantity of rainfall.
These challenges arose because we were trying to fulfill two different learning goals:
Introduce the basics of data science, and
Embed science concepts dictated by the NGSS.
NGSS suggests that students in ninth grade use mathematical and computational models in science, but simultaneously teaching science and data fluency is not straightforward because students may lack the skills they need to do this effectively. Our students struggled with covariation, so we developed materials to introduce basic graph analysis and relevant scientific principles.
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2031459&HistoricalAwards=false
Incorporating “Messy” Datasets into Classroom Instruction Teachers (at all educational levels, K–16) often struggle to incorporate real-world, complex, “messy” datasets into classroom instructional activities—even when those data are easily available in online sources, like interactive data maps or news features. Students easily navigate to confusing and complicated places in those data sources, and a teacher can be utterly lost when coming around to help them.
Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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InquirySpace The National Science Foundation (NSF)-funded InquirySpace projects, aimed at providing opportunities for helping youth engage in independent STEM investigations, represent a successful real-world application of data science. Aspects of the curriculum provide enlightening opportunities that other programs may be able to adopt or emulate. One specific set of examples derives from classroom observations in the project that showed students leveraging graphs as epistemic tools. Video analysis of extended classroom interactions indicated that student use of data graphs during sensor-based experiments could range from a focus on producing them as a procedural display to engaging deeply over several days to make sense of different graphical representations of their data. In a multi-case study, we investigated high school physics students’ use of graphs and what prompted those uses. Unexpected data patterns and graphical anomalies appeared to play a strong role in provoking student reasoning and triggering engagement with the graphs. For the three representative groups subject to in-depth analysis, graphs appeared to play an important role in their knowledge production as they made decisions about their experimental procedures and goodness of data. We conclude that when students produce data in an experiment for which they feel a sense of ownership, they can exhibit an almost proprietary interest in representations of their data. As they work to create alignments between their conceptions of their data and the unexpected data patterns they see in the graphs, they begin to use their graphs as epistemic tools. https://concord.org/our-work/research-projects/inquiryspace/
Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Case Type: Existing Tool/Software/Technology
Case Title Description Relevant Links/Citations
Gulf of Maine Research Institute Our work focuses on explorations of locally relevant impacts of climate change. At its heart, climate change is change over both space and time. Our experience is that existing tools do one or the other well but not both. So CODAP is fantastic for time series data. Tools from Esri and Field Scope are great for geographic information system (GIS) data. This is complicated by pedagogy. CODAP is designed for a “messing around” approach to working with data, which we love. To our knowledge there’s no equivalent that allows play with GIS, particularly over time. Animation of place over time is the likely current solution but I don’t THINK (could be wrong!) any existing tools put that power in the hands of learners. https://gmri.org/
CourseKata CourseKata Statistics and Data Science, an innovative interactive online textbook for teaching introductory statistics and data science in colleges, universities, and high schools. https://coursekata.org
Part of CourseKata’s Better Book Project is to leverage research and student data to guide continuous improvement of online learning resources.
STATS4STEM Fully funded by the National Science Foundation, STATS4STEM provides a collection of learning, assessment, tutoring, data, and computing resources for statistics educators and their students. Our assessment and tutoring feature allows for instantaneous student feedback with question-specific hints for students who are struggling. In addition, real-time learning charts and reports are available to help educators adapt instruction to meet the individual needs of their students. Our computing resources integrate real-world data with the RStudio statistical computing platform, allowing all students the opportunity to conduct real-world statistical analysis. Finally, in an effort to foster community, our site integrates a message board for statistics educators looking to share ideas, tips, and insights with other educators worldwide. https://www.stats4stem.org/
Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Any high school, college, or university educator worldwide who teaches statistics, or a course related to statistics, can register.
Pivot Interactives Two educators from Minneapolis, Minnesota, created a platform called Pivot Interactives that allows students to explore scientific phenomena and make sense of them through data analysis tools. Their goal is to ignite students’ passion for science, and teachers’ love for teaching, and to instill the proper fundamentals of collecting and analyzing data. https://www.pivotinteractives.com/
Pivot Interactives is the only platform for interactive video-based science activities with embedded data-collection tools. Based on real-world science phenomena, students become engaged with data capture, process, analysis, and communication—the fundamentals of data science. Under controlled conditions, students who use a curriculum based on Pivot Interactives interactive video showed more significant learning gains in critical thinking skills even when compared to integrated hands-on learning. Science education researchers at the Massachusetts Institute of Technology (MIT) and Carleton College measured students’ experience using Pivot Interactives interactive video compared to traditional methods:
85% reported that interactive video made it easier to understand the scenario being investigated,
92% said they’d encourage their friends to take courses that use interactive video,
65% wished MIT’s online physics course included more of our interactive video, and
60% reported an increase in using scientific skills like measurement techniques when using our interactive video.
Khan Lab School I piloted a year-long data science course to high schoolers (fresh-seniors) last year and it went great! We used the CourseKata curriculum and supplemented with projects and other activities I created. We use Jupyter Notebooks along with an integrated textbook and Deepnote.com and R to analyze our data. https://khanlabschool.org/
Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Case Type: Existing Tool/Software/Technology
Case Title Description Relevant Links/Citations
The FieldScope Project FieldScope is an innovative map-based data collection and analysis platform managed by BSCS Science Learning. It supports citizen and community science projects and other initiatives with tools to collect data from dispersed geographic locations and to analyze trends, patterns, and change over time, including the impact of interventions. FieldScope enables organizations, community members, and learners to monitor and actively address issues, such as environmental and social challenges, that are concerning to them. It empowers participants in projects to use maps, graphs, and other visualization tools to make meaning of crowd-sourced datasets and turn data into stories that can be used in science communication and grassroots advocacy. https://www.fieldscope.org
Invitations to Inquiry Invitations to Inquiry are short instructional activities designed to help middle and high school students work with large data sets hosted on FieldScope. Teachers and students use the interactive FieldScope platform to collect, visualize, and analyze environmental data. With these new Inquiries, students can explore FieldScope’s advanced mapping and graphing tools to dig deeper into data in the context of meaningful science classroom lessons. Each lesson engages students in interpreting graphs or maps, or both, and figuring out what the data mean. Ultimately, the Inquiries are intended to increase student confidence in working with data and using visualization tools. These Inquiries are designed for two to four days of learning and support the Science and Engineering Practices from the NGSS. They include teacher guides, slides, handouts, and other instructional resources and supports. https://bscs.org/resources/invitations-to-inquiry/
Center for Curriculum Redesign (CCR) My center has deeply redesigned math standards, in partnership with the Organisation for Economic Co-operation and Development, to reflect a K–12 emphasis on data science (aka stats/probs) and discrete/computational math. https://curriculumredesign.org/modern-mathematics/
Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Data Nuggets Data Nuggets are free classroom activities that bring authentic data and science stories into the classroom. More than 100 Data Nuggets are available on our website. Written by the scientists behind the research, each activity guides students through the process of working with a dataset to answer a scientific question. Because the authenticity of the research process is maintained, students often face unexpected results, messy data, and findings that do not support original hypotheses. Students who use Data Nuggets show increased interest in STEM careers, confidence when working with data, and improved abilities to construct scientific explanations. https://datanuggets.org
DataClassroom and DataClassroom U DataClassroom and DataClassroom U are web-apps for graphing, statistics, and data analysis in grades 6–12 and university level science and math classrooms. The tool runs on any device that can access the Internet and can integrate with LMSs such as Schoology, Clever, Google Classroom, Canvas, and Classlink. The tool has been designed by teachers to provide the opportunity to integrate next-generation data skills seamlessly with the learning experiences they are already creating. https://about.dataclassroom.com/ https://u.dataclassroom.com/
Functionally, students can produce a wide variety of data visualizations, and get support with graph choice, applying inferential statistics, and learning the math behind statistical tests with animation. The DataClassroom U version of the tool contains a Bridge to R which generates well annotated R code for any of the graphs or statistical tests called through the easy to use interface. This supports students early in the process of learning a statistical programming language.
The app contains 100+ curated datasets and lessons that are available with the free version of the app.
Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Case Type: Existing Tool/Software/Technology
Case Title Description Relevant Links/Citations
Schoolytics Schoolytics is a student data platform that gives data superpowers to teachers, administrators, parents, and students by building and automating data pipelines, and providing out-of-the-box data visualizations of academic data. We believe in empowering students with actionable data on their own progress and contend that students can and should have agency over their own learning. Schoolytics transforms Google Classroom assignment data into meaningful key performance indicators and time series trends for users to reflect on and respond to. https://www.schoolytics.com/
By using their Schoolytics dashboard, students get exposure to simple statistics and graphs based on their own data, which takes on a personal and real quality that more theoretical discussions on data science struggle to match. In addition, because the primary source of data for Schoolytics is Google Classroom, teachers and students can engage together and work as partners in analyzing and interpreting the data.
databot databot™ is a company that is pretty passionate about K–12 data science. We sell a STEM device, databot™, a tiny, wireless, classroom-tough multi-sensor product with 15 built-in sensors. Students and educators can easily connect to databot™ via bluetooth and immediately have access to scientific data from databot™ including CO2, volatile organic compounds, humidity, air pressure, and many more. databot™ joined the Data Science 4 Everyone coalition this summer and pledged to create 10 lesson plans that emphasize data science practices. The first is an air quality investigation. https://databot.us.com/ds4e-iaq/
Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Helioviewer: A Solar Data Viewer from NASA I would like to share an example of how a data tool can be designed and adapted for learners to enable students to better engage in analyzing and interpreting solar data and phenomena. A Solar Data Viewer from NASA, the Helioviewer was redesigned to put phenomena exploration first. Designing tools that better enable sensemaking and student exploration will be critical to the future of data science for learning. https://helioviewer.org/ https://student.helioviewer.org/
Coding Like a Data Miner We’ve developed an NSF-funded curriculum that teaches students how to scrape (i.e., data mine) Twitter along their personal interests. The curriculum combines quality computing practices and data science techniques using equity-driven framings. This means rather than navigate data sources generated by others, learners can construct their own data sources in real time. In this way, Coding Like a Data Miner is couched in real work applications suited to reflect the nature of the increasingly digital world around us! https://www.nsf.gov/awardsearch/showAward?AWD_ID=2137708&HistoricalAwards=false
Ohio Data Science Foundations Course Pilot This year I am involved in the Ohio Data Science Foundations Course Pilot: using the Data Science Curriculum (UCLA: Introduction to Data Science). By participating in this pilot I will be able to bring these two models together and finalize a course that is aligned with the state standards and provide my students with real-world engaging learning experience.
Also, this year, two students from the Intro to Data Science from last semester are now enrolled in our apprenticeship class. The plan is they will prepare for the SAS certification test and explore options with local companies. Our school has just been approved to access the SAS Educator Portal; all Perry Lake students will be able to access the SAS Skill Builder for Students. These students will independently work through the SAS Virtual Learning Environment: SAS Programming 1: Essentials course to prepare them for the SAS certification. I believe these students will be the first in the state to add this credential to their resumes.
Information about course pilot: https://education.ohio.gov/Topics/Learning-in-Ohio/Mathematics/Resources-for-Mathematics/Math-Pathways/Data-Science-Foundations
Referenced curriculum: https://www.idsucla.org/
Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Case Type: Existing Tool/Software/Technology
Case Title Description Relevant Links/Citations
University of Virginia School of Data Science I have developed a high school level data science course with one of my students. The course launches this week at a private school in Charlottesville, VA. The course does two things:
Students work together in small teams on an end-to-end data science project, which covers the Big Idea of a data science pipeline. Students select a project from an open-source repository of municipally generated data.
Students learn the essential ingredients to accomplish they learn a mix of computing, data skills, modeling, and statistics.
Awash in Data Awash in Data is an always-evolving e-book by Tim Erickson introducing students (and teachers) to basic ideas in data science. It begins with a set of lessons that take about three hours of class time, plus homework and a mini-project. Students use CODAP throughout. The book itself is “live” in the sense that you can do many tasks in the book itself; that is, CODAP windows are embedded in the website. Like CODAP, the book is free and requires no sign-in to use. The introductory lessons have been used several times with high school students as a supplement to an applied math class. Erickson and Chen published an article describing the work.

In 2020, Erickson used the book as the foundation for a one-semester high school introduction to data science. He is updating the book with assignments and learnings from that longer class.
https://www.concord.org/awashindata

Erickson, T., and Chen, E. (2021). Introducing data science with data moves and CODAP. Teaching Statistics, 43(S1), S124–S132. https://onlinelibrary.wiley.com/doi/10.1111/test.12240
GAISE Guidelines for Assessment and Instruction in Statistics Education (GAISE) Report: A Pre-K–12 Curriculum Framework provides recommendations and a curriculum framework with examples for teaching statistics in the pre-K–12 years. Resources and information about GAISE: https://www.amstat.org/education/guidelines-forassessment-and-instruction-in-statistics-education-(gaise)-reports
Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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I am putting GAISE in the “tool” category because learning needs to be assessed and the document provides direction for assessment. There are also many useful examples to help teachers see the path to integrating a data-centric approach while satisfying existing curricular demands.
Friday Institute for Educational Innovation at North Carolina State University When educators are ready to develop their skills in teaching data science and statistics, they can use online professional learning courses and platforms to engage with highly effective activities, videos, data investigations, readings, and assessments. Since 2015, 6,500+ educators from more than 90 countries have engaged in such self-guided learning through the Friday Institute for Educational Innovation at NC State University. Two current self-paced professional learning opportunities include Amplifying Statistics and Data Science in Classrooms http://go.ncsu.edu/amplifystats
InSTEP: Invigorating Statistics and Data Science Teaching Through Professional Learning http://instepwithdata.org
GAISE II In 2020, Pre-K–12 Guidelines for Assessment and Instruction in Statistics Education II (GAISE II): A Framework for Statistics and Data Science Education was co-published by the American Statistical Association (ASA) and the National Council of Teachers of Mathematics (NCTM). GAISE II incorporates enhancements and new skills needed for making sense of data today while maintaining the spirit of the original Pre-K–12 GAISE report published in 2005. Resources and information about GAISE: https://www.amstat.org/education/guidelines-for-assessment-and-instruction-in-statistics-education-(gaise)-reports
State Frameworks for Florida State We have developed a four-course program of study for the Career and Technical Division for grades 9–12 and have it aligned to a draft version of the pending frameworks for the State/Community College Frameworks for Data Science and Fintech. We have started to develop an open-source curriculum portal in Canvas under a CC-Share Alike license for use by teachers. We provide professional development and coaching to teachers to help them develop the skills they need to successfully teach this course. We are creating data science projects with Florida themes in agriculture, health care, science, marine health, manufacturing, finance, and other career related areas working with industry partners for data sets. We can provide expanded versions of these frameworks. See pages 62–70 for more information: https://www.nationalacademies.org/event/09-13-2022/docs/DC43B789C2136F62C030FAE3E7A8EB58C45EF823FACF
Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Case Type: Existing Tool/Software/Technology
Case Title Description Relevant Links/Citations
WeatherX WeatherX is an NSF-funded project that has been developing curriculum strategies to promote scientific data practices and interest in data science careers among middle school students in low-income rural areas. A major tool that WeatherX has developed is the National Oceanic and Atmospheric Administration (NOAA) Weather portal, a permanent plugin within the online data analysis and visualization tool CODAP. The portal provides anyone with a web browser access to large-scale weather data from NOAA. Using the portal, individuals can select and download hourly, daily, and monthly weather measurements (such as for temperature, wind speed, and precipitation levels) from 1,783 weather stations in the United States, with records spanning as far back as the mid-1800 to the present. Students using WeatherX lessons investigate data from their own local weather stations and make claims about whether specific weather events are extreme by comparing event data with 30-year climate averages. Based on research during an initial implementation of the WeatherX units, students and teachers spoke positively of the ability to investigate and learn from large-scale weather data from their own and other locations using the NOAA Weather portal and CODAP. In addition, teachers observed, and students reported, higher levels of confidence in making and interpreting graphs in CODAP after engaging in WeatherX activities. We will be making our WeatherX units available for public use in the upcoming months. An online tutorial on how to make graphs in CODAP to examine weather data is available at https://codap.concord.org/app/static/dg/en/cert/index.html#shared=https://cfm-shared.concord.org/vljl6HALM3Kwxorhb0EV/file.json
Jupyter Notebooks Educationally designed Jupyter Notebooks based on Python make complex machine learning algorithms accessible and transparent to students. Jupyter Notebooks can be designed with a kind of menu-driven interface, where coding and recoding parts can be used, but that is not necessary. Jupyter Notebooks can be designed as worked examples that can scaffold students in writing a computational essay for their own data analysis. A computational essay can be conceived as in interactive changeable book with code, graphs, tables, and text that make a data analysis reproducible. Fleischer, Y., Biehler, R., and Schulte, C. (2022). Teaching and learning data-driven machine learning with educationally designed Jupyter Notebooks. Statistics Education Research Journal, 21(2). https://doi.org/10.52041/serj.v21i2.61
Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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SocialExplorer The website SocialExplorer, developed by sociologist Andrew Beveridge, is an excellent U.S. Census Data access tool. It is useful for researchers, teachers, students, and the public. The subscription version provides access to historical census data back to 1790, and the free public version has great mapping and reporting tools for recent censuses. https://www.socialexplorer.com/
DataFlow DataFlow is a comprehensive open-source platform for programming, data processing, and real-time data graphing. Within DataFlow, students can produce meaningful data and control the data through their lifecycle, making decisions as data flow from the collection device to a representation on screen. Students choose what data are being collected, where to collect them, how to modify or transform them, how to use the data to actuate a relay, how to store those data, and how to view the data. For an example of DataFlow use: https://concord.org/newsletter/2020-spring/sensors-and-spinach-increasing-student-agency-in-biology-class/
When working with real-world data, teachers and curriculum developers face many dilemmas. One of the most prominent lies in the complexity of these datasets. For example, some relevant datasets in ongoing projects may include tens of thousands of cases and over one hundred attributes. These datasets on their own are much too large and complicated for the middle school audience the project targets. Choosy, a simple data tool designed to address this issue, could be adopted broadly to enhance data science curricula and approaches. Information about Choosy plugin: https://concord.org/newsletter/2021-fall/under-the-hood-three-new-codap-plugins/
Designed to enable the straightforward creation of simple datasets from complex ones, Choosy is implemented as a plugin to CODAP. By allowing teachers or curriculum developers to work with and easily select sub-portions of larger datasets, it enables classroom activities or curriculum units to involve a much wider range of datasets than otherwise possible. Choosy has a simple interface, with a tab for selecting the dataset, a tab for attributes, and a tab for tagging specific cases. Users proceed through these tabs, using a dropdown to select their desired dataset, a series of checkboxes to identify the attributes they desire to include, and a streamlined interface to select specific cases to set aside or delete. When they are satisfied with their choices (which can be monitored in a live readout reporting the number of attributes and cases currently selected), users can export the final simplified dataset for inclusion in their desired activity.
Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Case Type: Existing Tool/Software/Technology
Case Title Description Relevant Links/Citations
Transformers Plugin for CODAP In using CODAP, students can easily make simple changes to datasets by dragging and dropping attributes and creating formulas. However, this streamlined interface can render more complex operations difficult or impossible at times. Additionally, changes to datasets must be performed individually and repeated by hand, making the application of a series of data moves on multiple datasets tedious or prone to error. Information about Transformer plugin: https://concord.org/newsletter/2021-fall/under-the-hood-three-new-codap-plugins/
The Transformers plugin for CODAP is a tool aimed at addressing this issue. Designed by the Bootstrap curriculum with the goal of featuring data moves more prominently in its curriculum resources that use CODAP, the plugin provides 30 different transformations for users to apply to datasets automatically. Among many other transformations, this set of built-in transformations makes it easier for students to filter and sort attributes and to measure, aggregate, and summarize data. Additionally, the tool enables a more programmatic, documented, and repeatable approach to data transformation within the CODAP environment. Users can save sets of transformations as miniature programs, coming back to them later or saving them as mini-tools for future use.
Signification Plugin for CODAP Students often have issues facing complex datasets. However, a significant fraction of learners are blind or visually impaired, rendering traditional visual data analysis tools practically inaccessible. An experimental plugin for CODAP aims to begin to address both of these issues by making data “visible” through signification, the use of audio portrayals of data.
Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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It includes an interface for selecting a sub-portion of a dataset and a play button that loops through the selected subset repeatedly. As it loops through the dataset, the plugin translates the data into audio signals, varying aspects of the audio including pitch, timbre, and other attributes. Users can assign different attributes of a dataset to different aspects of the audio output to allow for examination of multiple attributes simultaneously, modify the speed and repetition rate of the looping to enable closer study of patterns, and easily compare portions of a dataset with each other to make differences more readily apparent.
Story Builder Plugin for CODAP Inspired by the NSF-funded Writing Data Stories project’s goal of helping middle school students become “data storytellers,” the Story Builder plugin allows students to build story “moments,” with each interactive moment capturing the state of a CODAP document at a given time. Since CODAP can also embed web pages and videos, a story can be truly multimedia. The plugin allows students to edit, delete, and rearrange their “moments” or lock them to prevent accidental changes. Information about Story Builder plugin: https://concord.org/newsletter/2021-fall/under-the-hood-three-new-codap-plugins/
Initial work to date has shown that stories created with the Story Builder plugin are great for student projects and presentations, introductions to data-rich content, arrangements of data sequences for curriculum development, and even applications as broad as a prototype mockup environment for designing new CODAP plugin capabilities.
Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Case Type: Existing Tool/Software/Technology
Case Title Description Relevant Links/Citations
DataBytes Data Story Bytes or “DataBytes” describes a framework for discussing data visualizations that may appear as part of a teacher’s curriculum, or in local or national news stories, with their students. These activities are designed to support quick (30 mins) discussions, in the spirit of “number talks,” to critically analyze and interpret data visualizations in ways that connect to students’ lives and important issues in society. Teachers can select from a series of questions designed to guide students through the following:
Making sense of trends and relationships in the data or visualization, what these patterns mean, and how they connect to key science concepts.
Building personal connections by considering how students’ own lives and communities may be impacted by or reflected by the patterns found in data.
Reflecting on the context and history of the data, how they were collected, by whom (including what gets “counted” and why), how they are visualized, what might be missing/hidden, and what questions the data can and cannot answer.
Envisioning future uses of data and visualization to expand the investigation, include and explore different perspectives, and highlight the importance of understanding what’s happening in the world around us in multiple ways.
Teacher guide for using Data Story Bytes: https://docs.google.com/document/d/1tAnSAZuxPKW8pigpWvjVDd8RlgH0UQ7N4DUVzceWw44/edit
Government Open Data Sets The government is making large strides toward making open data sets findable and accessible. Along with this is a growing number of solutions to make those data sets usable. Each solution targets a different audience and, therefore, differs with regard to underlying technology, capabilities, data access strategy, and requisite skills for use. In the past, I have proposed a game to teach critical thinking approaches to problem solving. The game targets K–12 students and uses Grafana, which is open-source software that is suitable for both the needs of scientists and K–12 students as envisioned above. The primary function of Grafana is logging, but it can be adapted. https://grafana.com/
Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Case Type: Real-World Application
Case Title Description Relevant Links/Citations
My NASA Data The NASA My NASA Data project began in 2004 to make NASA’s large collection of Earth science data accessible to K–12 students. The website now offers a number of audience-tested and proven tools for introducing data to students. https://mynasadata.larc.nasa.gov
Invitations to Inquiry The Invitations to Inquiry project is designed to provide short learning experiences (two to three classroom 45-minute periods) that are focused on using real-world community and citizen science data sets to make sense of a phenomenon. Students are introduced to data sets and are scaffolded through a process that helps them consider where the data come from and how they can be used to answer questions (or not). The data are hosted in FieldScope, a citizen science mapping and graphic platform, and the lessons use data from five different citizen science projects. Additionally, we have a self-paced free course for teachers to become familiar with the project, the lessons, and FieldScope. We also provide some program level resources for helping students with data (e.g., using data in discussions, introducing variables, choosing data representations). Importantly, these lessons are intended as introductory learning in data science, and they are explicitly designed to help build teacher- and student confidence in working with data. There are opportunities to go further with data in a few of the lessons (called Data+ activities). https://bscs.org/resources/invitations-to-inquiry/
Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Case Type: Real-World Application
Case Title Description Relevant Links/Citations
Meharry Data Science Summer Academy Students, mostly from underrepresented backgrounds, learned to apply coding and data science to robotics on the Meharry Medical College campus. The students were attending the Meharry Data Science Summer Academy, funded by NASA through the Minority University Research and Education project and provided to students at no cost. NASA’s goal is to support the dreams of students from traditionally underrepresented and underserved communities to enter careers in STEM. In addition to courses in programming, robotics, and data science, special sessions featured Black professionals in data science and robotics: Kenneth Harris, the deputy lead integration engineer for the NASA James Webb Telescope’s Integrated Science Instrument Module Electronic Components; and Dr. Sian Proctor, the first Black woman to pilot a spacecraft. The overwhelming message was clear. A future in science and technology is within each student’s grasp. https://sacsmeharry.org/nasacamp/
Making Sense of Data: A Statistics Survival Guide I have written a statistics guide for my high school students. I call it Making Sense of Data: A Statistics Survival Guide. Link to statistics guide: https://drive.google.com/file/d/18EfKKZrYJdSnsMdhdzwOXWnBuw4Ivbom/view?usp=sharing
The Relationship Between Bone Density & Age in Older Females: Anatomy and Physiology Data-based Question Strong bones are important to human health. Bone mineral density (BMD) is a way to measure how much calcium and other types of minerals are in an area of bone and helps health care providers detect bone diseases like osteoporosis and predict a person’s risk for bone fractures. Biological females and the elderly are most at risk for developing osteoporosis, which is directly related to BMD. Medical researchers at the University of California, San Francisco were interested in tracking just how quickly bone density declines on average in females and how this change might vary in different bones. This kind of research is called a descriptive study. The researchers obtained (with permission) 1,886 bone density scans and age data of several hundred patients from three Link to the full activity, including the data and citation: https://docs.google.com/document/d/18MO6avz6YrguPUO8MY8uPLU-0xLpFmiFAwSm1pn77yI/edit?usp=sharing
Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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clinics. The subjects were stratified into five-year age groups and BMD (g/cm2) was calculated for several skeletal areas of interest, including the lumbar spine (L1–L4), proximal radius, distal radius, and calcaneus. The researchers then converted BMD to a BMD percent relative to the 66–70 year age group.
Indirect Estimate of Potato Cell Cytoplasm Molarity Students use data analysis to complete an activity based on the following background (full activity included in link):

Background: In 1940, botanists at The Ohio State University, Bernard Meyer and Atwell Wallace, tested two methods for indirectly determining the molarity (moles cytoplasm particles per liter of cytosol) of the solution within potato (Solanum tuberosum) cells. In general, the botanists soaked cores of potato tissue in a series of different sucrose solution strengths. The solutions were measured in osmotic pressure (atmospheres at 20°C) and ranged from 0.0 atm to 33 atm. In their study, a change of zero in length or total weight of the cores indicated that the solution in which the cores were soaked was isotonic to the potato cell cytoplasm.
Link to the full activity: https://docs.google.com/document/d/1hL4BBM46tKx7yadMmbqZojs8DCs7HQh9SA8HvANFv0U/edit?usp=sharing
Timberline High School Timberline students in Intro to Data Science used data from our school to help determine if students who went from Algebra 2 to AP Calculus performed similarly to students who went from Precalculus to AP Calculus. Their results were used to help the school develop math pathways for all students at Timberline High School. For more information about the math department and courses offered: https://sites.google.com/boiseschools.org/timberline-student-services/course-descriptions/math-dept?pli=1
Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Case Type: Real-World Application
Case Title Description Relevant Links/Citations
Google Map Data: A Source of “Measurement of the Distance” to Students, Traveled Paths for Supplementing Data Science Learning Activity Students’ experiences outside of the classroom, encapsulated by images, video, sound, notes, and Global Positioning System tags, can ever more easily become texts for study. Students pick distance measurements from Google Maps, and I engage them to locate two locations they have manually walked to represent the path on graph paper/board as a relationship between distance traveled and time used. Thus, students who engage in such educational data mining (picking distance from the Google map) tend to focus on using Google Maps as a tool for discovering data. This engages students to build their data and computational literacies. Full activity can be accessed in cases document: https://www.nationalacademies.org/event/09-13-2022/docs/DC43B789C2136F62C030FAE3E7A8EB58C45EF823FACF
Transitioning from Statistics to Data Science Course in High School Setting For the past three years, I have been working on transitioning from a traditional high school statistics course to one that uses real-world applications and daily work with SAS Studio. Thus, statistics at Perry High School has been replaced with an Intro to Data Science course. https://www.sas.com/en_us/software/studio.html
I have been very fortunate to be supported by Professor Lisa Dierker from Wesleyan University. Professor Dierker is a co-creator of Wesleyan’s “Passion-Driven Statistics” model, a data-driven, project-based introductory curriculum backed by the National Science Foundation. This flexible curriculum engages students from a range of disciplines with large, real-world data sets and code-based analytic software (SAS Studio), providing experience in the rich, complicated, decision-making process of real statistical inquiry. On a daily basis, students work with current real-world data sets such as Gapminder, Nhanes, Addhealth, and Nesarc. I found the Passion-Driven Statistics model really enriched my teaching materials and student engagement.
Introduction to Data Science Activity At the beginning of the course, the DSS classes collect data about themselves (age, height, shoe size, cotton ball toss, reaction speed, and more) to analyze throughout the course. The data collected were also geared to have a mix of quantitative and categorical data to use for different group and regression
Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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models analysis. When analyzing the data, we are able the relate topics of sampling, measurement error/bias, mistakes, and outliers to real data they collected. I believe this is more powerful because it is one thing to be given a dataset and talk about measurement bias/mistakes in the data, but it is another when they are the source of the data. One example of a good discussion was “How did someone get a measurement of 8 inches for their index finger?”
Mobile City Science Mobile City Science (MCS) is a series of design studies of youth learning how to use data about their daily lives to make evidence-based recommendations for community change. These efforts required sometimes large groups of community stakeholders to support youth in this endeavor, including public school teachers and administrators, out-of-school time educators from libraries and museums, caregivers and families, Mayor’s Office representatives, and researchers. We developed a broad design commitment to honor and elevate data collected by youth while supporting them to use tools and create data stories in forms familiar to decision makers. This design commitment came from observations and analyses of interactions between residents and urban planners that were talking, and making decisions about the future built environment, using complex spatial data visualizations. https://www.education.uw.edu/mcs/
National Science Teaching Association (NSTA) Daily Do Playlist There is a NSTA Daily Do Playlist of three lessons forming an instructional sequence in which students are using mathematical models (computer simulations) to explain the phenomenon of the spread of COVID-19. Students use data science and new understandings to make recommendations to keep themselves, their families, and their communities safe. https://www.nsta.org/resources/daily-do
Fall Data Challenge Fall Data Challenge is sponsored by the ASA. Each year, this contest challenges undergraduate and high school students to work in teams to analyze real-world data and make recommendations to combat critical issues. https://thisisstatistics.org/FallDataChallenge/
Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Case Type: Real-World Application
Case Title Description Relevant Links/Citations
Census at School Census at School—U.S. is a free international classroom project that engages students in grades 4–12 in statistical problem solving using their own real data. Students complete an online survey, analyze their class census results, and compare their class with random samples of students in the United States and abroad. https://ww2.amstat.org/censusatschool/
ASA Data Visualization Poster Competition The ASA Data Visualization Poster Competition is for grades K–12 students to create a display containing two or more related graphics that summarize a set of data, look at the data from different points of view, and answer specific questions about the data. https://www.amstat.org/education/asa-data-visualization-postercompetition-for-grades-k-12-
Hub for Innovation and Research in Statistics Education (HI-RiSE) For many years, the projects within the Hub for Innovation and Research in Statistics Education [HI-RiSE] at NC State have worked in classrooms with students engaging them in data science activities using larger multivariate datasets in CODAP. These videos represent a sample of the successful ways students engage in data science skills and thinking within mathematics curriculum and classrooms. We use these videos to support teacher professional learning in statistics and data science. Video 1: Students Working on the Roller Coaster Investigation https://www.youtube.com/watch?v=RvzAxKlHr0E
Video 2: Discussion of the Roller Coaster Investigation https://www.youtube.com/watch?v=ETNF_542DvU
Video 3: Investigating Fuel Efficiency in AP Statistics https://youtu.be/HqHiFrI6i-E
Research Projects by High School Students In the spring of our High School Intro to Data Science class, students create their own research project. They create their own research questions and create surveys. They collect data, clean them, and analyze them using RStudio and other tools. They even use Code.org to use machine learning to create an app based on their data that will predict/classify new users. They then present their research. https://docs.google.com/presentation/d/17rc7a1JPyU7kGSGWPrYEbXYsEIGP4sh4S5scc2SsAQc/edit?usp=sharing
Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Using CODAP to Tell Different Stories Using CODAP to Tell Different Stories is an online lesson in The Statistics Teacher, a joint online publication of the ASA and the NCTM. This lesson uses the Statistical Investigation Cycle at the middle grades level to engage students in using and developing their understanding of statistics through problem solving with technological tools. Students collect data, analyze and interpret the results, as well as review the work of their peers to more deeply understand the meaning of the data and its potential implications. The problem solving and sense making using the various facets of data science provides an exceptional learning experience for students. https://www.statisticsteacher.org/files/2021/10/Using-CODAP-to-telldifferent-stories-101521_CC.pdf
GAISE II The Guidelines for Assessment and Instruction in Statistics Education II (GAISE II) document, from the ASA and the NCTM, provides a wealth of real-world applications organized around consideration of the development of statistical thinking/data science in pre-K–12 learning. Resources and information about GAISE: https://www.amstat.org/education/guidelines-forassessment-and-instruction-in-statistics-education-(gaise)-reports
Strengthening Data Literacy across the Curriculum (SDLC) Strengthening Data Literacy across the Curriculum (SDLC) studies high school mathematics curriculum modules that focus on social justice issues among students from historically marginalized groups. One module supports student analyses of income inequality in the United States using U.S. Census Bureau microdata and the online data analysis tool CODAP. As students explore these issues, they deepen their abilities to compare quantitative distributions using measures of center and variability. They also build abilities to reason with multivariable data. https://sites.google.com/view/uss-data/home
Based on pre- and post-module assessments and results from almost 200 students, we found statistically significant growth in students’ understanding of important statistical concepts and interests in data analysis. We also found signs of greater social and political awareness and agency with data—outcomes associated with increased critical data literacy. In this module, students work through seven lessons and a final team data investigation, exploring different forms of income inequality, their scope, and possible explanations.
Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Case Type: Real-World Application
Case Title Description Relevant Links/Citations
Using Data Cards for Data Exploration Using data cards for data exploration and unplugged introduction of decision trees with data with nutrition facts of food products. Children (grades 5 and 6) are asked to develop a multistep decision rule for predicting whether a food is recommendable or not. Children sort the data cards, identify reasonable split criteria, and experience the different rates of false classifications. The dependence of the decision rule on the somewhat subjective labeling and the training dataset can be experienced. Podworny, S., Fleischer, Y., Hüsing, S., Biehler, R., Frischemeier, D., Höper, L., and Schulte, C. (2021). Using data cards for teaching data based decision trees in middle school. 21st Koli Calling International Conference on Computing Education Research (Koli Calling ‘21), Joensuu, Finland. ACM. https://doi.org/10.1145/3488042.3489966
High School Machine Learning Teaching Module Development This paper reports on progress in the development of a teaching module on machine learning with decision trees for secondary-school students, in which students use survey data about media use of youth to predict who plays online games frequently (to suggest specific advertisements for online games to these students). The dataset consists of more than 500 cases and more than 50 variables and is also used for just data exploration activities as a preparatory step before introducing machine learning. They use CODAP’s “Arbor” plugin to manually build decision trees and understand how to systematically build trees based on data. Further on, the students use a menu-based environment in a Jupyter Notebook to apply an algorithm that automatically generates decision trees and to evaluate and optimize the performance of these. Students acquire technical and conceptual skills but also reflect on personal and social aspects of the uses of algorithms from machine learning. Biehler, R., and Fleischer, Y. (2021). Introducing students to machine learning with decision trees using CODAP and Jupyter Notebooks. Teaching Statistics, 43(S1), S133–S142. https://doi.org/10.1111/test.12279
Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Introduction to Data Science for Secondary School Students In this paper, we will describe an introduction to data science for secondary school students. We will report on the design and implementation of an introductory unit on “data and data detectives with CODAP” in which secondary school students used the online tool CODAP to explore real and meaningful survey data on leisure time activities and media use (so-called JIM-PB data) in a statistical project setting as a starting point for data science. The JIM-PB dataset served as a valuable dataset that offered meaningful and exciting opportunities for data exploration for secondary school students, and CODAP proved to be a valuable tool for the first explorations of these data. Frischemeier, D., Biehler, R., Podworny, S., and Budde, L. (2021). A first introduction to data science education in secondary schools: Teaching and learning about data exploration with CODAP using survey data. Teaching Statistics, 43(S1), S182–S189. https://doi.org/10.1111/test.12283
NetApp Data Explorers NetApp Data Explorers is an after-school program, funded by NetApp, that introduces middle school students to data science in the context of “using data for social good.” Data Explorers focuses on the 17 United Nations Sustainable Development Goals (SDGs), which present a “blueprint to achieve a better and more sustainable future for all,” and introduces participants to the data the United Nations has collected to track progress in the SDGs. Using CODAP, students explore health and education indicators from 195 countries that belong to the United Nations. https://www.netapp.com/social-impact/data-science-education/
While Data Explorers has been very engaging for participants in its pilot phases in the United States, England, and the Netherlands, we have also identified some challenges in the process of implementing (and revising) the program. For example, while middle school students are especially attuned to issues of “social good” and inequity, it is a big leap from noticing, for example, that African countries have lower life expectancies to coming up with a sensible and realistic “call to action.” In addition, much of the data is reported as rates (e.g., number of doctors per 100,000 people in a country), which may be mathematically challenging for some students to understand. Finally, all of these kinds of “civic data” are aggregated across a country, county, or other political entity; this kind of aggregation disguises a lot of local variability so runs contrary to many youths’ experience, which tends to be at much smaller geographical scale, like a neighborhood.
Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Case Type: Real-World Application
Case Title Description Relevant Links/Citations
Data Jam/Puerto Rico Data Jam/Puerto Rico is a five-year-long project in Puerto Rico to introduce middle and high school students to data science by engaging them in authentic environmental investigations using long-term ecological datasets. Students engaged in Data Jam/Puerto Rico are provided access to decades of long-term ecological data from El Yunque National Forest gathered by the Luquillo Long-Term Ecological Research (LTER) program at the University of Puerto Rico. After a series of introductory activities, students work in teams to come up with a research question, analyze the data using CODAP, and use the evidence from their analyses to answer their research question. Some of the groups present their posters at an annual Data Jam Symposium. General information about DataJam: https://lternet.edu/data-jams/

https://czo-archive.criticalzone.org/luquillo/education-outreach/k-12education-luquillo/
The project team’s primary activities are providing professional development for the participating teachers (most of whom teach either science or math), supporting teachers in classroom implementation through a combination of educational materials and classroom visits, and curating data sets from the LTER so that they are possible for students to use. In this last task, the team has been supported by a data fellow funded by the Environmental Data Initiative (EDI).
The intervention also includes several scientific mentors, young Puerto Rican scientists who serve as role models for students by sharing their career paths and advising them on their data analysis.
Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Financial Literacy: Retirement Calculations Students decide on a retirement age (my students used age 65) to calculate their age until retirement. They decide on two amounts they think they could save per week—one realistic, one a stretch—and everyone uses those amounts for comparison purposes. They use TheMint.org’s compounding calculator to figure amounts until retirement if that weekly money (calculated to yearly savings) is invested in a savings account (0.50% return) or the stock market (7% return, historically). Next, they collect and share those data in either an online spreadsheet or a paper graph. Students make predictions about how weekly savings amounts translate across time and investment vehicle. Some students multiply by 48 weeks instead of 52, so there are opportunities for conversations there. (Also requires conversations around unpredictability of stock market as well as how fees, buying and selling, etc. affect growth.) Students who initially said they had NO money to invest usually are thinking by the end of the activity about how much they could spare per week. https://themint.org/~theminto/kids/compounding-calculator.html
MATH STANDARD: Represent and analyze quantitative relationships between dependent and independent variables.
EXTENSION ACTIVITY: Calculate how much money a person could save if they had a better credit rating on a vehicle loan or a house mortgage. Take the difference in those two amounts and have students see how much that could equate to if it was invested and compounded over time.
Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Case Type: Real-World Application
Case Title Description Relevant Links/Citations
DataJam Pittsburgh DataWorks, along with the West Big Data Innovation Hub and the New York Hall of Science, developed a virtualized version of the DataJam, an annual program and competition for high school students that runs throughout the academic year and introduces youth to data visualization and analytic skills to answer a research question of their own design. DataJam mentors—volunteer university undergraduate students trained in mentorship, statistics, data ethics, and community-based research principles—guide students. https://www.pghdataworks.org/data-jam
The COVID-19 pandemic forced us to migrate DataJam to 100% online for the 2021–22 school year. We piloted an online format to expand the program to culturally and geographically diverse teams and an eMentoring model. This provides us with an opportunity to determine how to expand, scale, and replicate the success of DataJam to reach the most underserved communities that lack access to the resources and expertise to participate in rich data science programs. We expanded DataJam participation to high schools across Pennsylvania, New Jersey, and Massachusetts, and the Pala Native American Tribal lands in Southern California through videoconferencing and other online collaboration tools.
Focus groups and surveys conducted with the students and teachers showed that the program was successful online in very diverse cultural and geographical settings. We hope to explore expanding the diversity of high school participants to include underserved urban, rural, immigrant, unhoused, and tribal youth, and to deepen our understanding of the factors that lead to changes in self-efficacy and career aspirations in the target youth.
Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Impact of Introducing Data Science to Students on Major/Degree Choice My background is pediatrics, immunology, and medical informatics. I have a profound experience of using ontology for data integration and data analysis in health and regulatory areas. I have been mentoring junior high schoolers and college students to work on Food and Drug Administration–funded informatics projects or university collaborative research projects. Out of four female (all minority background) students, three switched their major or chose the major in data science or information technology. One of them switched from pre-med to a data science track. I keep mentoring those students who are currently in college majoring in data science for continuous data science research.
StoryQ The NSF-funded StoryQ project, aimed at providing opportunities for youth to use data science and AI to explore language-centered scenarios, represents a successful real-world application of data science. One example of this application engages students in using a data-focused view to analyze poetry. Students process poetry to identify and characterize features of its language such as the number and type of syllables in each line, then use patterns uncovered in the data to develop and train a machine learning model in the StoryQ app, a plugin to CODAP. The goal of the curriculum is to scaffold text analytics for English Language Arts students, so they learn to understand and appreciate both poetry and applications of data and AI. Data analysis lies doubly at the heart of the StoryQ activities, as students not only use analysis to examine and identify patterns in language to train a model but also use data visualization and analysis to evaluate the results of the model, directly evaluating the weights of various parameters derived from applying their model in order to characterize the text itself as well as to judge the accuracy of the model. In this manner, StoryQ stands as an example of the ways students can use data analysis as a tool both to shed new light on traditional school subjects and as a pathway toward understanding new applications of data and computing. https://www.nsf.gov/awardsearch/showAward?AWD_ID=1949110
Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Case Type: Real-World Application
Case Title Description Relevant Links/Citations
Maize High School Biology students at Maize High School conducted primary water quality sampling in partnership with the United States Geological Survey—Kansas (USGS-KS) in 2018 to develop a predictive model for the occurrence of harmful algal blooms in Cheney Reservoir, the primary drinking water supply for Wichita, Kansas and surrounding communities. The students working on this project ALL became STEM majors at universities across the United States, and the teacher was contracted by Microsoft as a big data analyst. She wrote FarmBeats for Students machine learning and AI curricula for Future Farmers of America (FFA) and Center for Agriculture Science Education (CASE), and helped to develop the machine learning methodologies being deployed across all industries.
New Data Science Course for High Schoolers In our state, we had a success in data science with youth as we developed a course for high schoolers based upon data science, and offered a course for which students could receive either math or computer science credit. A lot of time and research went in to developing this course, and we believe it highlights the changes in education, and the importance of computer science and data science in today’s education.
The Next Grand Challenge: Building Critical Mass for Data Science The California Education Learning Lab is pleased to announce the release of a new grant opportunity to promote the buildout of critical data science educational infrastructure. Through this request for proposal, Learning Lab’s Grand Challenge seeks to incentivize public higher education institutions to embrace data science as an opportunity to build new pathways, modernize majors, attract historically underrepresented students into STEM, and deepen both civic and interdisciplinary learning. https://calearninglab.org/

https://calearninglab.org/grant/data-science-rfp/
Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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Suggested Citation:"Appendix C: Submitted Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26852.
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On September 13 and 14, 2022, the Board on Science Education at the National Academies of Sciences, Engineering, and Medicine held a workshop entitled Foundations of Data Science for Students in Grades K–12. Speakers and participants explored the rapidly growing field of K-12 data science education, by surveying the current landscape, surfacing what is known, and identifying what is needed to support student learning, develop curriculum and tools, and prepare educators. To support these conversations, four papers were commissioned and discussed during the workshop. This publication summarizes the presentations and discussion of the workshop.

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