Learning analytics and educational data mining have come a long way in a short time. In this article, a lightly edited transcript of a keynote talk at the Learning Analytics and Knowledge Conference in 2019, I present a vision for some directions I believe the field should go: towards greater interpretability, generalizability, transferability, applicability, and with clearer evidence for effectiveness. I pose these potential directions as a set of six contests, with concrete criteria for what would represent successful progress in each of these areas: the Baker Learning Analytics Prizes (BLAP). Solving these challenges will bring the field closer to achieving its full potential of using data to benefit learners and transform education for the better.
How to Cite
Baker Learning Analytics Prizes, BLAP, learning analytics, educational data mining, model generalizability
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