Abstract
Knowledge Tracing (KT) refers to the problem of predicting learners’ future potential performance given their past learning history in e-learning systems. In order to better trace the learners’ knowledge, KT tasks have become increasingly complicated recently, and various factors related to learning (such as skill, exercise, hint, etc.) have been incorporated into the modeling of KS of the learner, which renders it inadequate for the traditional KT definition to formalize these tasks. Therefore, this paper first gives a more general formal definition of KT tasks, and then proposes an Extensible Deep Knowledge Tracing model for multiple learning factors based on this general definition, named EDKT. EDKT can integrate various different learning factors by extending or ablating factors in two plug-ins on the basis of minor modifications. To demonstrate the effectiveness of the proposed model, we conduct extensive experiments on three real-world benchmark datasets, and the results show that EDKT comprehensively outperforms the state-of-the-art KT models on predicting future learner responses.
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Acknowledgment
We would like to thank the anonymous reviewers for their helpful comments. The research is supported by the National Key Research and Development Program of China (2018YFB1004502) and the National Natural Science Foundation of China (61702532, 61532001, 61690203).
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He, L., Li, X., Tang, J., Wang, T. (2021). EDKT: An Extensible Deep Knowledge Tracing Model for Multiple Learning Factors. In: Jensen, C.S., et al. Database Systems for Advanced Applications. DASFAA 2021. Lecture Notes in Computer Science(), vol 12681. Springer, Cham. https://doi.org/10.1007/978-3-030-73194-6_23
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