"Data comes from the real world": A Constructionist Approach to Mainstreaming K12 Data Science Education

Prerna Ravi, Robert Parks, John Masla, Harold Abelson, Cynthia Breazeal

Abstract: Data science is emerging as a crucial 21st-century competence, influencing professional practices from citing evidence when advocating for social change to developing artificial intelligence (AI) models. For middle and high school students, data science can put formerly decontextualized subjects into real-world scenarios. Many existing curricula, however, lack authenticity and personal relevance for students. A critique of data science courseware cites the lack of "author proximity," in which students do not contribute to the data's production or see their personal experiences reflected in the data. This paper introduces a novel data science curriculum to scaffold middle and high school students in undertaking real-world data science practices. Through project-based learning modules, the curriculum engages students in investigating solutions to community-based problems through visualization and analysis of live sensor data and public data sets. Materials include formative assessments to help educators (especially those from non-math and computing backgrounds) measure their students' abilities to identify statistical patterns, critically evaluate data biases, and make predictions. As we pilot and co-design with teachers, we will look closely at whether the curriculum's resources can successfully support non-technical practitioners engaging in an integrated curriculum.