Skip to main content
SpringerLink
Log in
Book cover

Pacific-Asia Conference on Knowledge Discovery and Data Mining

PAKDD 2022: Advances in Knowledge Discovery and Data Mining pp 380–392Cite as

Dual-State Knowledge Tracing Model with Mutual Information Maximization

  • Conference paper
  • First Online:

  • 3043 Accesses

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

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.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

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.

References

  1. Cen, H., Koedinger, K., Junker, B.: Learning factors analysis – a general method for cognitive model evaluation and improvement. In: Ikeda, M., Ashley, K.D., Chan, T.-W. (eds.) ITS 2006. LNCS, vol. 4053, pp. 164–175. Springer, Heidelberg (2006). https://doi.org/10.1007/11774303_17

    Chapter  Google Scholar 

  2. Cho, K., et al.: Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)

  3. Choi, Y., et al.: Towards an appropriate query, key, and value computation for knowledge tracing. In: Proceedings of the Seventh ACM Conference on Learning@ Scale, pp. 341–344 (2020)

    Google Scholar 

  4. Choi, Y., et al.: EdNet: a large-scale hierarchical dataset in education. In: Bittencourt, I.I., Cukurova, M., Muldner, K., Luckin, R., Millán, E. (eds.) AIED 2020. LNCS (LNAI), vol. 12164, pp. 69–73. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-52240-7_13

    Chapter  Google Scholar 

  5. Corbett, A.T., Anderson, J.R.: Knowledge tracing: modeling the acquisition of procedural knowledge. User Model. User-Adapt. Interact. 4(4), 253–278 (1994)

    Article  Google Scholar 

  6. Ghosh, A., Heffernan, N., Lan, A.S.: Context-aware attentive knowledge tracing. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2330–2339 (2020)

    Google Scholar 

  7. Hjelm, R.D., et al.: Learning deep representations by mutual information estimation and maximization. In: International Conference on Learning Representations (2018)

    Google Scholar 

  8. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Article  Google Scholar 

  9. Liu, Q., Shen, S., Huang, Z., Chen, E., Zheng, Y.: A survey of knowledge tracing. arXiv preprint arXiv:2105.15106 (2021)

  10. Van der Maaten, L., Hinton, G.: Visualizing data using t-sne. J. Mach. Learn. Res. 9(11), 1–27 (2008)

    MATH  Google Scholar 

  11. Miller, A.H., Fisch, A., Dodge, J., Karimi, A.H., Bordes, A., Weston, J.: Key-value memory networks for directly reading documents. In: EMNLP (2016)

    Google Scholar 

  12. Nagatani, K., Zhang, Q., Sato, M., Chen, Y.Y., Chen, F., Ohkuma, T.: Augmenting knowledge tracing by considering forgetting behavior. In: The World Wide Web Conference, pp. 3101–3107 (2019)

    Google Scholar 

  13. Nakagawa, H., Iwasawa, Y., Matsuo, Y.: Graph-based knowledge tracing: modeling student proficiency using graph neural network. In: 2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI), pp. 156–163. IEEE (2019)

    Google Scholar 

  14. Oord, A.V.d., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)

  15. Pandey, S., Karypis, G.: A self-attentive model for knowledge tracing. arXiv preprint arXiv:1907.06837 (2019)

  16. Pavlik Jr, P.I., Cen, H., Koedinger, K.R.: Performance factors analysis-a new alternative to knowledge tracing. Online Submission (2009)

    Google Scholar 

  17. Pelánek, R.: Bayesian knowledge tracing, logistic models, and beyond: an overview of learner modeling techniques. User Model. User-Adapt. Interact. 27(3), 313–350 (2017)

    Article  Google Scholar 

  18. Piech, C., Bassen, J., Huang, J., Ganguli, S., Sahami, M., Guibas, L.J., Sohl-Dickstein, J.: Deep knowledge tracing. Adv. Neural Inf. Process. Syst. 28, 505–513 (2015)

    Google Scholar 

  19. Sabour, S., Frosst, N., Hinton, G.E.: Dynamic routing between capsules. arXiv preprint arXiv:1710.09829 (2017)

  20. Shen, S., et al.: Learning process-consistent knowledge tracing. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 1452–1460 (2021)

    Google Scholar 

  21. Simons, P.R.J.: Transfer of learning: Paradoxes for learners. Int. J. Educ. Res. 31(7), 577–589 (1999)

    Article  Google Scholar 

  22. Su, Y., et al.: Exercise-enhanced sequential modeling for student performance prediction. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)

    Google Scholar 

  23. l Tong, S., et al.: Structure-based knowledge tracing: an influence propagation view. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 541–550. IEEE (2020)

    Google Scholar 

  24. Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)

    Google Scholar 

  25. Yeung, C.K., Yeung, D.Y.: Addressing two problems in deep knowledge tracing via prediction-consistent regularization. In: Proceedings of the Fifth Annual ACM Conference on Learning at Scale, pp. 1–10 (2018)

    Google Scholar 

  26. Zhang, J., Shi, X., King, I., Yeung, D.Y.: Dynamic key-value memory networks for knowledge tracing. In: Proceedings of the 26th International Conference on World Wide Web, pp. 765–774 (2017)

    Google Scholar 

  27. Zhang, L., Xiong, X., Zhao, S., Botelho, A., Heffernan, N.T.: Incorporating rich features into deep knowledge tracing. In: Proceedings of the Fourth (2017) ACM Conference on Learning@ Scale, pp. 169–172 (2017)

    Google Scholar 

Download references

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
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Changzhi Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hongyu Yi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaofeng He
    View author publications

    You can also search for this author in PubMed Google Scholar

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

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

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

Download citation

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

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-05932-2

  • Online ISBN: 978-3-031-05933-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation