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The paper shows that study-strategy clusters from EdNet logs predict learner engagement but not mastery, highlighting that behavior-only profiling is insufficient for assessing knowledge gains.
Proposes Phase-Aware Knowledge Tracing (PAKT), a framework that decomposes student interactions into ability and proficiency phases, using a multi-branch Transformer to model phase-specific and holistic knowledge states, achieving consistent improvements over baselines on six benchmarks.
MOSAIC is a novel framework that uses a frozen LLM to generate semantic embeddings and hierarchical prediction prompts for knowledge tracing, achieving state-of-the-art results on multiple benchmarks.
This paper introduces SLC (State-space Logit Correction), which corrects per-item logit bias in knowledge tracing models using empirical-Bayes shrinkage via a Kalman smoother, improving AUC beyond global calibration techniques.
This paper introduces MBP-KT, a framework for enhanced knowledge tracing that leverages meta-behavioral patterns to extract global collaborative information from learner interactions, improving performance across various downstream models.