In educational data mining, knowledge tracing (KT) aims to model learning performance based on student knowledge mastery. Deep-learning-based KT models perform remarkably better than traditional KT and have attracted considerable attention. However, most of them lack interpretability, making it challenging to explain why the model performed well in the prediction. In this paper, we propose an interpretable deep KT model, referred to as fuzzy deep knowledge tracing (FDKT) via fuzzy reasoning. Specifically, we formalize continuous scores into several fuzzy scores using the fuzzification module. Then, we input the fuzzy scores into the fuzzy reasoning module (FRM). FRM is designed to deduce the current cognitive ability, based on which the future performance was predicted. FDKT greatly enhanced the intrinsic interpretability of deep-learning-based KT through the interpretation of the deduction of student cognition. Furthermore, it broadened the application of KT to continuous scores. Improved performance with regard to both the advantages of FDKT was demonstrated through comparisons with the state-of-the-art models.

Fei Liu

Chenyang Bu

Haotian Zhang

Le Wu

Kui Yu

Xuegang Hu

ACM transactions on office information systems

ACM Transactions on Office Information Systems

FDKT: Towards an Interpretable Deep Knowledge Tracing via Fuzzy Reasoning

F fuzzy deep knowledge tracing via fuzzy reasoning greatly enhanced the intrinsic interpretability of deep-learning-based KT through the interpretation of the deduction of student cognition and broadened the application of KT to continuous scores.

FDKT：通过模糊推理实现可解释的深度知识追踪

FDKT: Towards an Interpretable Deep Knowledge Tracing via Fuzzy Reasoning

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