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Dual-State Knowledge Tracing Model with Mutual Information Maximization

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Advances in Knowledge Discovery and Data Mining (PAKDD 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13280))

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Abstract

Knowledge tracing aims to trace students’ knowledge states and predict their future performance based on their historical learning processes. Most existing methods of characterizing a student’s state are not effective enough, using only global representation or knowledge concept level representation. Such representation methods cannot consider the characteristics of knowledge concepts and the relations between concepts at the same time. In this paper, we propose a Dual-State Knowledge Tracing (DSKT) Model with Mutual Information Maximization. DSKT uses dynamic routing to extract knowledge commonalities from original knowledge concepts, updates the knowledge state at the concept and commonality levels, and predicts future performance by fusing two states. In addition, to incorporate the relationship between exercises and knowledge concepts, we use the principle of mutual information maximization to learn their representations. Extensive experimental results show the effectiveness of our model.

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Notes

  1. 1.

    https://sites.google.com/site/assistmentsdata/home/assistment-2009-2010-data.

  2. 2.

    https://sites.google.com/site/assistmentsdata/home/2012-13-school-data-with-affect.

  3. 3.

    https://sites.google.com/view/assistmentsdatamining/dataset.

  4. 4.

    https://github.com/riiid/ednet.

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Author information

Authors and Affiliations

  1. School of Computer Science and Technology, East China Normal University, Shanghai, China

    Haodong Meng & Xiaofeng He

  2. Born to Learn Education Technology, Chengdu, China

    Changzhi Chen & Hongyu Yi

  3. Shanghai Key Laboratory of Trustworthy Computing, Shanghai, China

    Xiaofeng He

Authors
  1. Haodong Meng
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  2. Changzhi Chen
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  3. Hongyu Yi
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  4. Xiaofeng He
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Corresponding author

Correspondence to Xiaofeng He .

Editor information

Editors and Affiliations

  1. Laboratory of Artificial Intelligence and Decision Support, University of Porto, Porto, Portugal

    João Gama

  2. School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, China

    Tianrui Li

  3. National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China

    Yang Yu

  4. School of Computer Science and Technology, University of Science and Technology of China, Hefei, China

    Enhong Chen

  5. JD iCity, JD Technology & JD Intelligent Cities Research, Beijing, China

    Yu Zheng

  6. School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, China

    Fei Teng

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Meng, H., Chen, C., Yi, H., He, X. (2022). Dual-State Knowledge Tracing Model with Mutual Information Maximization. In: Gama, J., Li, T., Yu, Y., Chen, E., Zheng, Y., Teng, F. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2022. Lecture Notes in Computer Science(), vol 13280. Springer, Cham. https://doi.org/10.1007/978-3-031-05933-9_30

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  • DOI: https://doi.org/10.1007/978-3-031-05933-9_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-05932-2

  • Online ISBN: 978-3-031-05933-9

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