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Interpreting Deep Learning Models for Knowledge Tracing

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Abstract

As a prominent aspect of modeling learners in the education domain, knowledge tracing attempts to model learner’s cognitive process, and it has been studied for nearly 30 years. Driven by the rapid advancements in deep learning techniques, deep neural networks have been recently adopted for knowledge tracing and have exhibited unique advantages and capabilities. Due to the complex multilayer structure of deep neural networks and their ”black box” operations, these deep learning based knowledge tracing (DLKT) models also suffer from non-transparent decision processes. The lack of interpretability has painfully impeded DLKT models’ practical applications, as they require the user to trust in the model’s output. To tackle such a critical issue for today’s DLKT models, we present an interpreting method by leveraging explainable artificial intelligence (xAI) techniques. Specifically, the interpreting method focuses on understanding the DLKT model’s predictions from the perspective of its sequential inputs. We conduct comprehensive evaluations to validate the feasibility and effectiveness of the proposed interpreting method at the skill-answer pair level. Moreover, the interpreting results also capture the skill-level semantic information, including the skill-specific difference, distance and inner relationships. This work is a solid step towards fully explainable and practical knowledge tracing models for intelligent education.

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Acknowledgements

This research is partially supported by National Natural Science Foundation of China (No. 62077006 and No.62177009), Open Project of the State Key Laboratory of Cognitive Intelligence (No. iED2021-M007) and the Fundamental Research Funds for the Central Universities.

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Authors and Affiliations

  1. Advanced Innovation Center for Future Education, Beijing Normal University, Beijing, China

    Yu Lu, Deliang Wang, Penghe Chen, Qinggang Meng & Shengquan Yu

  2. School of Educational Technology, Faculty of Education, Beijing Normal University, Beijing, China

    Yu Lu, Deliang Wang, Penghe Chen, Qinggang Meng & Shengquan Yu

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  1. Yu Lu
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  2. Deliang Wang
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  3. Penghe Chen
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  4. Qinggang Meng
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All authors contributed to the system design and implementation. Algorithm design, data collection and analysis were performed by all authors. The first draft of the manuscript was written by Yu Lu and Deliang Wang, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Shengquan Yu.

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Lu, Y., Wang, D., Chen, P. et al. Interpreting Deep Learning Models for Knowledge Tracing. Int J Artif Intell Educ 33, 519–542 (2023). https://doi.org/10.1007/s40593-022-00297-z

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