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Domain adaptive knowledge tracing

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Abstract

Knowledge Tracing (KT) is one of the fundamental tasks in intelligent education, which mainly predicts students’ performance on new questions based on their historical learning interaction behavior. Although many deep learning methods have been successfully applied to KT tasks, there are still some problems in real scenarios. The data acquisition of student interactions is often the key problem in many KT tasks, since most KT models require a large amount of student interaction data for model training. However, such data is difficult to obtain because it may contain personal information about students. Our goal is to leverage existing student exercises from another subject/dataset (namely source domain) to train a KT model and adapt it to the current subject/dataset (namely target domain) using only a small amount of target domain data. Therefore, we propose a novel Domain Adaptive Knowledge Tracing model (DAKT). To achieve the adaptation of knowledge tracing, we design a domain-shared answer embedding module to capture the behavioral features of students’ past answers and a domain-adaptive knowledge state module to adapt the model to the target domain. We conduct extensive experiments on four benchmark datasets to demonstrate the effectiveness of our model.

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Data availability

The datasets used in this study’s experiments can be found at the following link: 1. https://sites.google.com/site/assistmentsdata/home/assistment2009-2010-data/skill-builder-data-2009-2010. 2. https://sites.google.com/site/assistmentsdata/home/2015assistments-skillbuilder-data. 3. https://sites.google.com/view/assistmentsdatamining/dataset?authuser=0. 4. https://pslcdatashop.web.cmu.edu/KDDCup/downloads.jsp.

Notes

  1. https://sites.google.com/site/assistmentsdata/home/assistment2009-2010-data/skill-builder-data-2009-2010

  2. https://sites.google.com/site/assistmentsdata/home/2015assistments-skill-builder-data

  3. https://sites.google.com/view/assistmentsdatamining/dataset?authuser=0

  4. https://pslcdatashop.web.cmu.edu/KDDCup/downloads.jsp

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 62276138 and Grant 62076135 and in part by the Jiangsu “Qing Lan” Project.

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  1. Nan**g Normal University, Nan**g, China

    Yumeng Tang, Wanqi Yang, Yuquan **e & Ming Yang

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  1. Yumeng Tang
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  2. Wanqi Yang
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Correspondence to Wanqi Yang.

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Tang, Y., Yang, W., **e, Y. et al. Domain adaptive knowledge tracing. Int. J. Mach. Learn. & Cyber. (2024). https://doi.org/10.1007/s13042-024-02219-y

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