RECAST: Learning-Science Inductive Biases for Knowledge Tracing

Knowledge tracing predicts whether a learner will answer the next item correctly from their interaction history. Transformer KT models are strong sequence predictors, yet they typically treat tutoring logs as generic tokens and rely on Softmax attention to rediscover structure that learning science already identifies as central: last-attempt retrieval, local response persistence, spacing-dependent forgetting, and individualised cross-skill transfer. We encode these regularities as explicit inductive biases rather than incidental byproducts of attention. In this paper, we propose REcency- and Concept-lag Aware Skill Tracing (RECAST), a dual-stream causal Transformer with a Skill Recency Bank for last same-skill outcome, lag, and visit count; a Local Markov state for streaks and skill switches; concept-conditioned lag biases with distinct same- vs. cross-skill schedules; and a student factor $U$ that gates a mixture of low-rank transfer graphs. On five pyKT benchmarks with 5-fold evaluation, RECAST obtains the best AUC on four datasets and the best ACC on three against recurrent, memory, and Transformer baselines, with the largest gains when learners frequently revisit the same skill and competitive accuracy under heavily interleaved practice.