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Knowledge tracing based on multi-feature fusion

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Abstract

Knowledge tracing involves modeling student knowledge states over time so that we can accurately predict student performance in future interactions and recommend personalized student learning paths. However, existing methods, such as deep knowledge tracing and dynamic key-value memory networks (DKVMN), fail to comprehensively consider some key features that may influence the prediction results of knowledge tracing. To solve this problem, we propose a new model called knowledge tracing based on multi-feature fusion (KTMFF), which introduces features of the question text, the knowledge point difficulty, the student ability, and the duration time, etc., provides feature extraction methods, and uses a multi-head self-attention mechanism to combine the above features. This model predicts student mastery levels of knowledge points more accurately. Experiments show that the area under curve (AUC) of the KTMFF model is 3.06% higher than that of the DKVMN model. Furthermore, the ablation study indicates that each of the above features can improve the AUC of the model.

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Notes

  1. Synthetic: https://github.com/chrispiech/DeepKnowledgeTracing/tree/master/data/synthetic.

  2. ASSISTments2009: https://sites.google.com/site/assistmentsdata/home/assistment-2009-2010-data/skill-builder-data-2009-2010.

  3. ASSISTments2015: https://sites.google.com/site/assistmentsdata/home/2015-assistments-skill-builder-data.

  4. Statics2011: https://pslcdatashop.web.cmu.edu/DatasetInfo?datasetId=507.

  5. BnuUnit: http://www.bnu-ai.cn/download-unit.

  6. BnuTerm: http://www.bnu-ai.cn/download-term.

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Acknowledgements

This work was supported in part by National Natural Science Foundation of China under Grant 62077009 and in part by the State Key Laboratory of Cognitive Intelligence under Grant iED2019-Z04, Deep knowledge tracking model with multiple features (Corresponding author: Rong Xiao).

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Authors and Affiliations

  1. School of Artificial Intelligence, Beijing Normal University, Xinjiekouwai Street No. 19, Beijing, China

    Yongkang Xiao, Rong Xiao, Ning Huang, Yixin Hu, Huan Li & Bo Sun

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  1. Yongkang Xiao
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Correspondence to Rong Xiao.

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Xiao, Y., Xiao, R., Huang, N. et al. Knowledge tracing based on multi-feature fusion. Neural Comput & Applic 35, 1819–1833 (2023). https://doi.org/10.1007/s00521-022-07834-w

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