ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2021, Vol. 58 ›› Issue (12): 2618-2629.doi: 10.7544/issn1000-1239.2021.20211021

Special Issue: 2021可解释智能学习方法及其应用专题

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Interpretable Deep Knowledge Tracing

Liu Kunjia, Li Xinyi, Tang Jiuyang, Zhao Xiang   

  1. (Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha 410073)
  • Online:2021-12-01
  • Supported by: 
    This work was supported by the National Key Research and Development Program of China (2020AAA0108800), the National Natural Science Foundation of China (61872446, 71971212, 62002373), and the Postgraduate Scientific Research Innovation Project of Hunan Province (CX20200067).

Abstract: The task of knowledge tracing involves tracking users’ cognitive states by modeling their exercise-answering sequence, predicting their performance over time, and achieving an intelligent assessment of the users’ knowledge. Current works mainly model the skills related to the exercises, while ignoring the rich information contained in the contexts of exercises. Moreover, the current deep learning-based methods are agnostic, which undermines the explainability of the model. In this paper, we propose an interpretable deep knowledge tracking (IDKT) framework. First, we alleviate the data sparsity problem by using the contextual information of the exercises and skills to obtain more representative exercise and skill representations. Then the hidden knowledge states are fused with the aforementioned embeddings to learn a personalized attention, which is later used to aggregate neighbor embeddings in the exercise-skill graph. Finally, given a prediction result, an inference path is selected as the explanation based on the personalized attention. Compared with typical existing methods, IDKT exhibits its superiority by not only achieving the best prediction performance, but also providing an explanation at the inference path level for the prediction results.

Key words: interpretability, knowledge tracing, personalization, attention, context information

CLC Number: