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Multi-type factors representation learning for deep learning-based knowledge tracing

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Abstract

Knowledge Tracing (KT) refers to the problem of predicting future learner performance given their historical interactions with e-learning platforms. Recent years, Deep Learning-based Knowledge Tracing (DLKT) methods show superior performance than traditional methods due to their strong representational ability. However, researchers usually focus on innovations in model structure, while ignoring the importance of Representation Learning (RL) for DLKT. Investigating previous studies, it is found that the mining and integration of learning-related factors can effectively improve the performance of DLKT models. This paper focuses on providing a model embedding interface for DLKT by considering multiple types of learning-related factors. We first explore and analyze four types of learning-related factors: exercise and skill, the attributes of exercise, learners’ historical performance, and learners’ forgetting behavior in the learning process. We then propose an Extensible Representation Learning (ERL) approach for DLKT to extract and integrate the representations of these four types of factors by setting five components: base embedding, auxiliary embedding, performance embedding, forgetting embedding, and embedding integration. Finally, we apply ERL into two mainstream DLKT models and comprehensively evaluate the proposed approach on several real-world benchmark datasets. Results show that ERL can significantly improve the performances of these two network on predicting future learner responses.

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Notes

  1. The corresponding source code and all preprocessed datasets are available at https://github.com/HLBilove/ERL-master

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Acknowledgements

We would like to thank the anonymous reviewers for their helpful comments. The research is supported by the National Natural Science Foundation of China (61702532, 61690203).

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Authors and Affiliations

  1. College of Computer, National University of Defense Technology, Yanwachi Street, Changsha, 410073, Hunan, People’s Republic of China

    Liangliang He, Jintao Tang, Pancheng Wang, Feng Chen & Ting Wang

  2. Information center, National University of Defense Technology, Yanwachi Street, Changsha, 410073, Hunan, People’s Republic of China

    Xiao Li

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  1. Liangliang He
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Correspondence to Ting Wang.

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He, L., Tang, J., Li, X. et al. Multi-type factors representation learning for deep learning-based knowledge tracing. World Wide Web 25, 1343–1372 (2022). https://doi.org/10.1007/s11280-022-01041-2

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