Improving Interpretability of Deep Sequential Knowledge Tracing Models with Question-centric Cognitive Representations

This paper presents QIKT, a deep learning model for knowledge tracing that predicts student performance on future questions by modeling knowledge state changes at a question-specific level, addressing limitations of homogeneous question assumptions in existing models. The work evaluates prediction accuracy on three real-world educational datasets and improves model interpretability through question-centric cognitive representations.

Knowledge tracing (KT) is a crucial technique to predict students’ future performance by observing their historical learning processes. Due to the powerful representation ability of deep neural networks, remarkable progress has been made by using deep learning techniques to solve the KT problem. The majority of existing approaches rely on the homogeneous question assumption that questions have equivalent contributions if they share the same set of knowledge components. Unfortunately, this assump

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