DKVMN-KAPS: Dynamic Key-Value Memory Networks Knowledge Tracing With Students’ Knowledge-Absorption Ability and Problem-Solving Ability

This paper proposes DKVMN-KAPS, a deep learning model for knowledge tracing that predicts students' future question-answering performance by modeling individual differences in knowledge-absorption ability and problem-solving ability using hierarchical CNNs and autoencoders. The model aims to improve personalized learning by more accurately tracking students' knowledge states and providing feedback on weak knowledge areas.

Knowledge tracing aims to predict students’ future question-answering performance based on their historical question-answering records, but the current mainstream knowledge tracing model ignores the individual differences in different students’ knowledge-absorption and problem-solving abilities, which leads to a poor prediction of students’ question-answering performance by the model. To solve this, Dynamic Key-Value Memory Networks Knowledge Tracing with Students’ Knowledge-Absorption Ability a

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