Data science for K-12: A design-based research study

Data science is a field that merges advancements in computer science, statistics, and machine learning. To date, much data science instruction has been through graduate programs or self-directed course offerings. There are therefore fewer opportunities for formal data science education, especially for K-12 educators and learners who have limited exposure to these fields. While some efforts have emerged to introduce data science in pre-college settings, there is little research regarding learning design strategies that support data science for inservice teachers. As such, this project employs a design-based research (DBR) approach to develop accessible and engaging open-access learning resources as a way to reduce barriers to data science instruction across the educational spectrum. Given the gap regarding data science in K-12, our DBR study identifies the following design elements: (a) revised data science content scope, (b) design standardization for interactive learning, (c) reframing data science for case-based learning, (d) artificial intelligence (AI) towards data personalization, and (e) data science coding through goal-based learning. The project also introduces aspects of the curriculum via a teacher professional development, which provides important insights and challenges regarding data science at the middle and high school levels. Implications for learning design, open-educational resources, data science standards, and others are discussed.