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Dynamic knowledge tracing through data driven recency weights

There has been considerable interest in techniques for modelling student learning across practice problems to drive real-time adaptive learning, with particular focus on variants of the classic Bayesian Knowledge Tracing (BKT) model proposed by Corbett & Anderson, 1995. Over time researches have proposed many variants of BKT with differentiation based on their treatment of the underlying parameters: (a) general across student and questions; (b) individualized for students; and (c) individualized for questions. Yet at the same time, most of these variants are similar in that they utilize the same Hidden Markov (HMM) architecture to model student learning and share many of the same drawbacks, including less effective balancing between recent and historical student data and assuming that students learn at the same rate across all the attempts irrespective of if they get the question right. At the same time, these variants share the virtue of parameter interpretability, a virtue not seen in recent efforts to recast knowledge tracing as a deep learning problem. This paper proposes a different architecture that replaces learning rate with recency weights which capture student improvement wholly through data rather than assuming constant learning across attempts and manages recent and historical data more appropriately while retaining the interpretability of BKT parameters. The proposed model was tested on multiple public datasets from ASSISTments and Mindspark and performed similarly to classic BKT model on unseen data.