BAIM enriches knowledge tracing item representations by deriving stage-level embeddings from Polya's four problem-solving stages and routing them adaptively per learner context, yielding consistent gains over pretraining baselines on two datasets.
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An LLM pipeline generates knowledge components for coding problems, enabling KCGen-KT to outperform existing KT methods and human-written KCs on student response prediction across two datasets.
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Behavior-Aware Item Modeling via Dynamic Procedural Solution Representations for Knowledge Tracing
BAIM enriches knowledge tracing item representations by deriving stage-level embeddings from Polya's four problem-solving stages and routing them adaptively per learner context, yielding consistent gains over pretraining baselines on two datasets.
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Automated Knowledge Component Generation for Interpretable Knowledge Tracing in Coding Problems
An LLM pipeline generates knowledge components for coding problems, enabling KCGen-KT to outperform existing KT methods and human-written KCs on student response prediction across two datasets.
- Knowing When to Defer: Selective Prediction for Responsible Knowledge Tracing