Deep Attentive Model for Knowledge Tracing

This paper proposes DAKTN, a deep learning model for knowledge tracing that predicts student performance on exercises by incorporating historical behavior sequences with an attention mechanism, improving upon existing DNN-based approaches that only consider current-step information.

Knowledge Tracing (KT) is a crucial task in the field of online education, since it aims to predict students' performance on exercises based on their learning history. One typical solution for knowledge tracing is to combine the classic models in educational psychology, such as Item Response Theory (IRT) and Cognitive Diagnosis (CD), with Deep Neural Networks (DNN) technologies. In this solution, a student and related exercises are mapped into feature vectors based on the student's performance a

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