For as long as the Bayesian Knowledge Tracing (BKT) approach is known, so are the attempts to account for not only skill-level but individual student factors. A lot of computational methods to implement individualization in BKT were proposed over the past 25 years as BKT existed. To this day, virtually all individualization approaches were not suited for easy implementation. Either they were purely analytical (only fit for post-hoc analyses) or required significant computational effort to realize (e.g., calibrating individual factors as students cleared units of content). In this work, we discuss implementing the individualization of BKT using a mechanism of an Elo rating schema. Elo has been established in the educational domain for some time and offers tangible theoretical and practical benefits. We show that infusing BKT even with an Elo component using a single parameter to track student-specific factors results in significant quantitative and qualitative improvements to modeling student learning. This approach is easy to implement in a system already featuring BKT.
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