Deep Knowledge Tracing Incorporating a Hypernetwork With Independent Student and Item Networks

This paper proposes a novel Deep Knowledge Tracing method that combines Item Response Theory with deep learning using independent student and item networks via a hypernetwork architecture to predict student performance and track knowledge states. The method is evaluated on six benchmark datasets and demonstrates improved prediction accuracy compared to earlier Deep-IRT approaches.

Knowledge tracing (KT), the task of tracking the knowledge state of a student over time, has been assessed actively by artificial intelligence researchers. Recent reports have described that Deep-IRT, which combines item response theory (IRT) with a deep learning method, provides superior performance. It can express the abilities of each student and the difficulty of each item such as IRT. Nevertheless, its interpretability is inadequate compared to that of IRT because the ability parameter depe

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