Knowledge tracing stops at one right-or-wrong score: DIKT splits the prediction by skill and gains 2.04 AUC points on average across three benchmarks
DIKT decomposes the probability that a student answers the next exercise correctly into a product of per-skill success probabilities, giving each skill a cognitive state bounded between 0 and 1 that evolves over time. Most deep knowledge-tracing models predict from implicit vectors and cannot say which skill is holding the result back. On ASSIST09, ASSIST17 and Algebra2005, DIKT's AUC exceeds every listed baseline, an average absolute gain of 2.04 percentage points.