Integrated Data Science for Secondary Schools: Design and Assessment of a Curriculum
Emmanuel Schanzer, Nancy Pfenning, Flannery Denny, Sam Dooman, Joe Gibbs Politz, Benjamin S. Lerner, Kathi Fisler, Shriram Krishnamurthi
ACM Technical Symposium on Computer Science Education, 2022
We propose that secondary-school data-science curricula should be based on four key ingredients: two are technical (programming and statistics, with visualization sitting at their intersection), while two are human-facing (meaningful domains, and civic responsibility). We describe their relationship and argue for their importance.
Based on this, we then present the Bootstrap:Data Science curriculum, designed for integration into multiple disciplines and settings. It achieves this by (a) being designed as a set of remix-able lessons, and (b) letting classes and students choose personally meaningful datasets.
We also initiate the process of evaluating this curriculum. We create two assessment instruments, one focused on learning and the other on personalization and engagement. We provide very preliminary data gathered from students and teachers.
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