Pimentel, D. R., Horton, N. J., & Wilkerson, M. H. (2022). Tools to support data analysis and data science in k-12 education. Commissioned paper for the National Academy of Sciences, Engineering, and Medicine, Board on Science Education, Committee on Science Investigations and Engineering Design for Grades 6-12. Washington, DC. [PDF]
Together with Danny Pimentel (Stanford) and Nick Horton (Amherst), I has the opportunity to prepare this commissioned paper as part of the National Academies’ Foundations of Data Science in K-12 Workshop. See the abstract below; in addition to the report itself Nick has made a number of supplementary materials and resources available at this site.
Abstract. There has been a proliferation of tools for teaching data analysis and data science
at the middle and high school levels. While a few frameworks for systematically
exploring the affordances and constraints of such tools exist, most work has only
explored one or a few tools at once, or has not focused on K-12 usage. In this paper,
we blend first-hand comparative analysis methods and supplemental literature review
to conduct a systematic analysis of several common data analysis software packages
in use at the K12 level. Using an adaptation of a framework proposed by McNamara
(2019), we grouped the tools into related genres. Spreadsheets, while familiar and
accessible to many, lacked many desirable features. Visual tools (e.g., CODAP, Social
Explorer, iNZight) lower the barrier for data exploration, but may not easily support
more advanced statistical tests. Scripting tools (e.g., Python, Pyret, R) provide great
extensibility but with increased degree of difficulty. Looking across tools and genres,
our analysis suggests that these genres boast complementary strengths depending
on students’ developmental and investigative needs. We make recommendations for
the design and use of tools, notably highlighting the importance of working across
different tool types as a part of data practice.
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