Given an increased focus on computer science education as a valuable context to teach data science—due in part to the potential of computing for accessing, processing, and analyzing digital datasets—there have been steady efforts to develop kindergarten through 12th grade (K-12) curricula that productively engage learners in these academic areas. Bootstrap: Data Science and Exploring Computer Science (ECS) are prominent curricular examples designed to support high school data science access in computing contexts. While these vital efforts have found success bridging computer and data science, there remain growing concerns about how we can ensure that such learning experiences support the demographic and intellectually diverse cohorts of students needed for field innovation, occupational attainment, and public literacy. Challenges to these efforts often persist because existing data sources and activities offered to students are typically shaped by others (e.g., curriculum designers, teachers, etc.) rather than by learners themselves. This results in inquiry-driven questions, processes, and outcomes that can restrict exploration and engagement, as opposed to inherently and authentically linking to learners' diverse personal interests, styles and concerns. Perspectives in culturally responsive computing (CRC) provide viable frames for how to design learning experiences that encourage learner access, empowerment, and personal interests—key features for spurring field diversity through learning. With this imperative and framing in mind, we share our project called "Coding Like a Data Miner" (CLDM), which leverages a social media-based application programming interface (API) to teach learners how to gather, process (or wrangle), analyze and then communicate insights learned from "big data" sets. We describe this design as sandbox data science (SDS)—an approach to computing-based data science that is consistent with CRC perspectives with demonstrated promise in broadening participation and enhancing productivity in computer science education. In this article, we share insights into our rationale and the theoretical perspectives that drive our curricular design. We then provide an overview of the curriculum with case examples of the sorts of pursuits that can be taken up by learners in this context. Finally, we reflect on CLDM and design principles that make SDS a viable approach to broadening computing-based data science participation and productivity. This curriculum and accompanying resources are publicly available for review, use and adaptation at www.abclearninglab.com/cldm.
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Index Terms
- Sandbox Data Science: Culturally Relevant K-12 Computing
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