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With a call for schools to infuse data across the curriculum, many are creating curricula and examining students’ thinking in data-intensive problems. As the discipline of statistics education broadens to data science education, there is a need to examine how practices in data science can inform work in K-12. We synthesize literature about statistics investigation processes, data science as a field and practices of data scientists. Further, we provide results from an ethnographic and interview study of the work of data scientists. Together, these inform a new framework to support data investigation processes. We explicate the practices and dispositions needed and offer a glimpse of how the framework can be used to move the discipline of data science education forward.
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What to consider when we consider data
Abstract The data sets used in statistics education have changed over time, from mathematically “well‐behaved” ones that facilitated computation, to more context‐rich sources and now, with the increasing influence of data science practices, to “found” data, often from open data sites. As data sources change, it is important for educators to take a fresh look at the ways we engage students in thinking about the processes that generated the data they encounter. The use of already collected data requires particular attention because many of the decisions that went into the processes of obtaining the data are hidden. Students need to learn to ask “Who, When, How, Where, and Why?” data were collected and to wonder if the data really measure what needs to be measured. Our advocacy in this paper is to deepen the educational treatment of data production to better reflect the current and future practice of statistics and data science.
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- PAR ID:
- 10388961
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Teaching Statistics
- Volume:
- 43
- Issue:
- S1
- ISSN:
- 0141-982X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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