The National Academies Press

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Pages 76-109

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From page 76... ... This includes collaboration among teachers; across levels of education administration; and with community members, librarians, practicing scientists, and others. Building and sustaining these collaborations will require incentives and supports, she said, such as time, encouragement, and opportunity for teachers to pursue the skills and knowledge that it takes to teach data science to their students. Read the entire page →
From page 77... ... (2018) . Data Science for Undergraduates: Opportunities and Options. Read the entire page →
From page 79... ... The workshop will bring together researchers and practitioners engaged in K–12 data science education from a variety of contexts including formal and informal; designed and emergent; elementary and secondary; and whose efforts include standalone curricula as well as activities integrated within other content areas (e.g., STEM disciplines and the humanities) Read the entire page →
From page 80... ... , University of California, Berkeley A Vision for High-Quality Data Science Education 10:20–11:20 AM This session will explore what defines a valuable learning experience for students, what research tells us about successful vs. unsuccessful curricular intervention, and how those learnings can be articulated into policy and practice. Read the entire page →
From page 81... ... Background Readings: A Secret Agent. K–12 Data Science Learning Through the Lens of Agency1 Critical Data Literacy: Creating a More Just World with Data2 Moderator: Nicholas Horton, Amherst College Presenters: • Ryan "Seth" Jones, Middle Tennessee State University • Jo Louie, Education Development Center, Inc. Read the entire page →
From page 82... ... Background Reading: Tools to Support Data Analysis and Data Science in K–12 Education3 Moderator: Tim Erickson, Epistemological Engineering Panelists: • Rolf Biehler, Paderborn University, Germany (virtual) • Chad Dorsey, Concord Consortium • Randy Kochevar, Education Development Center, Inc. Read the entire page →
From page 83... ... Topics will include student learning progressions, opportunities for different school subjects to impart data science topics, and the wraparound resources needed for implementation. Background Reading: Previewing the National Landscape of K–12 Data Science Implementation4 Moderator: Zarek Drozda, Director, Data Science 4 Everyone, University of Chicago Panelists: • Suyen Machado, University of California, Los Angeles • Stephanie Melville, San Diego Unified School District • Paul Strode, Fairview High School • Katie Headrick Taylor, University of Washington 11:15–11:30 AM Break How Is Data Science Integrated in Content Areas? Read the entire page →
From page 84... ... The goal of this session is to examine issues on teachers' use of data and the preparation needed to teach statistics/data science/computation for prospective teachers and practicing teachers in formal and informal education settings. Moderator: Hollylynne Lee, North Carolina State University Panelists: • Anna Bargagliotti, Loyola Marymount University • Stephanie Casey, Eastern Michigan University • Anne Leftwich, Indiana University Bloomington (virtual) Read the entire page →
From page 85... ... She also co-authored the book Statistics and Data Science for Teachers with Christine Franklin. Bargagliotti's interests are in nonparametric and circular statistics, statistics education throughout the K–16 grade bands, data visualization, and multivariate models. Read the entire page →
From page 86... ... His research interests include probability, statistics, and data science education; university mathematics education; and the professional development of mathematics teachers. He has co-edited the 2022 Special Issue on data science education of the Statistics Education Research Journal and previously worked as a professor for didactics of mathematics at Kassel University before he moved to Paderborn University. Read the entire page →
From page 87... ... She is part of the writing team for the proposed Mathematics Framework for the state of California, is co-leading a K–12 Data Science Initiative, and was named as one of the eight educators "changing the face of education" by the BBC. Angela Calabrese Barton (Presenter, she/her/hers) Read the entire page →
From page 88... ... She has received generous funding from the National Science Foundation, through grants and an National Science Foundation CAREER award, as well as the National Security Agency, Facebook, and multiple Google Faculty Research Awards. Tamara L Read the entire page →
From page 89... ... Dorsey currently heads several National Science Foundation–funded projects devoted to investigating cutting-edge topics in data science education research. He has been a consistent advocate for expanding research, pedagogy, and awareness of K–12 data science education. Read the entire page →
From page 90... ... He is the faculty advisor of the Introduction to Data Science project, a high school data science course. Gould is also founder of DataFest, an undergraduate data analysis competition and celebration of all things data that is held annually at over 40 universities and colleges around the world. Read the entire page →
From page 91... ... He served as the chair of the Committee of Presidents of Statistical Societies, as a member of the Roundtable on Data Science Postsecondary Education, and as a member of the National Academies of Sciences, Engineering, and Medicine's Data Science for Undergraduates Consensus Study, and he is a co-chair of the National Academies' Committee on Applied and Theoretical Statistics. Horton has published a plethora of papers in statistics and biomedical research and four books on statistical computing and data science. Read the entire page →
From page 92... ... She designs and offers free online professional development for teachers, related to teaching statistics and data science, as well as creates and distributes open educational resources for preservice teacher education. Lee earned her Ph.D. Read the entire page →
From page 93... ... is a senior research scientist and specializes in research on innovations in STEM education. Her research currently focuses on interventions that promote interests and learning in STEM fields of rapidly growing importance, such as data science, with an emphasis on collaborating with interdisciplinary teams and community partners to co-develop greater educational opportunities for learners from historically marginalized communities. Read the entire page →
From page 94... ... is the data science education project director at the University of California, Los Angeles. She is a former classroom teacher, instructional coach, professional development facilitator, and administrator for the Los Angeles Unified School District (LAUSD) Read the entire page →
From page 95... ... Mojica works on the Hub for Innovation and Research in Statistics Education team on multiple initiatives focused on building foundations for K–12 data science education and data-informed citizenry. Leigh F Read the entire page →
From page 96... ... Radinsky teaches courses on the design of learning environments, qualitative research methods, and social studies teaching methods, among others, and does professional development with middle school and high school social studies teachers. He is former coeditor in chief of the Journal of the Learning Sciences, and a fellow of the International Society of the Learning Sciences. Read the entire page →
From page 97... ... is a mathematician, computer scientist, and learning scientist at TERC, an educational research and development non-profit, where she has been studying the growth of students' and teachers' statistical reasoning, particularly as it is enabled by research-based tools for statistics education. She was involved in the development of several such pieces of software -- Stretchy Histograms, Shifty Lines, TinkerPlots, and Fathom -- and led the ViSOR (Visualizing Statistical Reasoning) Read the entire page →
From page 98... ... In this role, she directed or co-directed several projects including the study that resulted in the report A Framework for K–12 Science Education, the blueprint for the Next Generation Science Standards. Schweingruber is a nationally recognized leader in leveraging research findings to catalyze improvements in science and STEM education policy and practice. Read the entire page →
From page 99... ... , and he has just completed co-authoring Data Science in Context: Foundations, Challenges, and Opportunities. He is a fellow of the the Association for Computing Machinery, IEEE, the National Academy of Engineering, and the American Academy of Arts and Sciences, where he serves on its council. Read the entire page →
From page 100... ... Wilkerson's work was awarded by an Early CAREER grant from the National Science Foundation and the American Educational Research Association's Jan Hawkins Award for Humanistic Research and Scholarship in Learning Technologies. She received her Ph.D. Read the entire page →
From page 101... ... Glenadine Gibb Achievement Award for her contribution to the improvement of mathematics education at the state and national levels. Wilkerson's research interests include mathematics education, algebra teacher efficacy, and professional development. Read the entire page →
From page 103... ... 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. Read the entire page →
From page 104... ... What are examples of successful partnerships to facilitate data science teaching and learning at the K–12 levels? Lack of Data Science Learning the fundamentals of how data are collected, analyzed, and Fundamentals communicated starts with a solid foundation in the science and engineering practices outlined in the Framework for Science Education. Read the entire page →
From page 105... ... And, like most climate phenomena, they need a tool that allows them to view change over both space and time. Need for High-Quality, Regardless of how hard we work at designing clear, important, and meaningful Consistent Data Science district and state science and math standards and guidelines that incorporate Training for Practitioners data science skill development, what actually ends up happening in the classroom may be vastly different than our intentions. Read the entire page →
From page 106... ... Some of these challenges included time restraints -- including Courses 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. Read the entire page →
From page 107... ... 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. Read the entire page →
From page 108... ... 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. Read the entire page →
From page 109... ... 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 Data science is so new that students do not have a fundamental understanding Surrounding Data Science of what it is, or even who uses it, which makes diving into the curriculum difficult. Read the entire page →

From page 76...

... This includes collaboration among teachers; across levels of education administration; and with community members, librarians, practicing scientists, and others. Building and sustaining these collaborations will require incentives and supports, she said, such as time, encouragement, and opportunity for teachers to pursue the skills and knowledge that it takes to teach data science to their students.