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Deep Knowledge Tracing on Skills with Small Datasets

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Intelligent Tutoring Systems (ITS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13284))

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Abstract

Deep Knowledge Tracing (DKT), as well as other machine learning approaches, is biased toward data used during the training step. Thus, for problems where we have few amounts of data for training, the generalization power will be low, and the models will tend to work well on classes containing many samples and poorly on those with few. This situation is quite common in educational data where some skills are very difficult to master while others are very easy. As a result, there will be less data on students who correctly answered questions related to difficult skills, but also on those who provided incorrect answers to questions related to easy skills. In those cases, the DKT is unable to correctly predict the student’s answers to questions associated with these skills. To improve DKT performance under these conditions, we have developed a two-fold approach. Firstly, the loss function is modified so that some skills are masked to force the model’s attention on those that are difficult to generalize. Secondly, to cope with the limited amount of data on some skills, we proposed a hybrid architecture that integrates a priori (expert) knowledge with DKT through an attentional mechanism. The resulting model accurately tracks student Knowledge in the Logic-Muse Intelligent Tutoring System (ITS), compared to the traditional Bayesian Knowledge Tracing (BKT) and the original DKT.

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Notes

  1. 1.

    https://github.com/mmkhajah/dkt.

  2. 2.

    https://github.com/angetato/Deep-Knowledge-Tracing-On-Skills-With-Limited-Data.

References

  1. Ange, T., Roger, N., Aude, D.: Hybrid deep neural networks to predict socio-moral reasoning skills. In: Proceedings of the 12th International Conference on Educational Data Mining, pp. 623–626 (2019)

    Google Scholar 

  2. Ange, T., Roger, N., Aude, D., Claude, F.: Semi-supervised multimodal deep learning model for polarity detection in arguments. In: 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2018)

    Google Scholar 

  3. Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)

  4. Beck, J.E., Chang, K.: Identifiability: a fundamental problem of student modeling. In: Conati, C., McCoy, K., Paliouras, G. (eds.) UM 2007. LNCS (LNAI), vol. 4511, pp. 137–146. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-73078-1_17

    Chapter  Google Scholar 

  5. Chorowski, J.K., Bahdanau, D., Serdyuk, D., Cho, K., Bengio, Y.: Attention-based models for speech recognition. In: Advances in Neural Information Processing Systems, pp. 577–585 (2015)

    Google Scholar 

  6. Collobert, R., Weston, J.: A unified architecture for natural language processing: deep neural networks with multitask learning. In: Proceedings of the 25th International Conference on Machine learning, pp. 160–167. ACM (2008)

    Google Scholar 

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

    Article  Google Scholar 

  8. Graves, A.: Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850 (2013)

  9. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  10. Huang, C., Li, Y., Change Loy, C., Tang, X.: Learning deep representation for imbalanced classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5375–5384 (2016)

    Google Scholar 

  11. Kasurinen, J., Nikula, U.: Estimating programming knowledge with bayesian knowledge tracing. In: ACM SIGCSE Bulletin, vol. 41, pp. 313–317. ACM (2009)

    Google Scholar 

  12. Khajah, M., Lindsey, R.V., Mozer, M.C.: How deep is knowledge tracing? arXiv preprint arXiv:1604.02416 (2016)

  13. Lu, H., Setiono, R., Liu, H.: Effective data mining using neural networks. IEEE Trans. Knowl. Data Eng. 8(6), 957–961 (1996)

    Article  Google Scholar 

  14. Luong, M.T., Pham, H., Manning, C.D.: Effective approaches to attention-based neural machine translation. arXiv preprint arXiv:1508.04025 (2015)

  15. Martin, J., VanLehn, K.: Student assessment using bayesian nets. Int. J. Hum Comput Stud. 42(6), 575–591 (1995)

    Article  Google Scholar 

  16. Nguyen, L., Do, P.: Combination of bayesian network and overlay model in user modeling. In: Allen, G., Nabrzyski, J., Seidel, E., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2009. LNCS, vol. 5545, pp. 5–14. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-01973-9_2

    Chapter  Google Scholar 

  17. Nkambou, R., Brisson, J., Kenfack, C., Robert, S., Kissok, P., Tato, A.: Towards an intelligent tutoring system for logical reasoning in multiple contexts. In: Conole, G., Klobučar, T., Rensing, C., Konert, J., Lavoué, É. (eds.) EC-TEL 2015. LNCS, vol. 9307, pp. 460–466. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24258-3_40

    Chapter  Google Scholar 

  18. Nkambou, R., Mizoguchi, R., Bourdeau, J.: Advances in Intelligent Tutoring Systems, vol. 308. Springer, Berlin (2010). https://doi.org/10.1007/978-3-642-14363-2

  19. Oquab, M., Bottou, L., Laptev, I., Sivic, J.: Learning and transferring mid-level image representations using convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1717–1724 (2014)

    Google Scholar 

  20. Piech, C., et al.: Deep knowledge tracing. In: Advances in Neural Information Processing Systems, pp. 505–513 (2015)

    Google Scholar 

  21. Russell, S.J., Norvig, P.: Artificial Intelligence: a Modern Approach. Pearson Education Limited, Malaysia (2016)

    MATH  Google Scholar 

  22. Sabourin, J., Mott, B., Lester, J.C.: Modeling learner affect with theoretically grounded dynamic bayesian networks. In: D’Mello, S., Graesser, A., Schuller, B., Martin, J.-C. (eds.) ACII 2011. LNCS, vol. 6974, pp. 286–295. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-24600-5_32

    Chapter  Google Scholar 

  23. Shen, W., Wang, X., Wang, Y., Bai, X., Zhang, Z.: Deepcontour: a deep convolutional feature learned by positive-sharing loss for contour detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3982–3991 (2015)

    Google Scholar 

  24. Tato, A., Nkambou, R., Brisson, J., Kenfack, C., Robert, S., Kissok, P.: A bayesian network for the cognitive diagnosis of deductive reasoning. In: Verbert, K., Sharples, M., Klobučar, T. (eds.) EC-TEL 2016. LNCS, vol. 9891, pp. 627–631. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45153-4_78

    Chapter  Google Scholar 

  25. Tato, A., Nkambou, R., Brisson, J., Robert, S.: Predicting learner’s deductive reasoning skills using a bayesian network. In: André, E., Baker, R., Hu, X., Rodrigo, M.M.T., du Boulay, B. (eds.) AIED 2017. LNCS (LNAI), vol. 10331, pp. 381–392. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-61425-0_32

    Chapter  Google Scholar 

  26. Wang, L., Sy, A., Liu, L., Piech, C.: Deep knowledge tracing on programming exercises. In: Proceedings of the Fourth (2017) ACM Conference on Learning@ Scale, pp. 201–204. ACM (2017)

    Google Scholar 

  27. Xu, K., et al.: Show, attend and tell: Neural image caption generation with visual attention. In: International Conference on Machine Learning, pp. 2048–2057 (2015)

    Google Scholar 

  28. Yeung, C.K., Yeung, D.Y.: Addressing two problems in deep knowledge tracing via prediction-consistent regularization. arXiv preprint arXiv:1806.02180 (2018)

  29. Yudelson, M.V., Koedinger, K.R., Gordon, G.J.: Individualized bayesian knowledge tracing models. In: Lane, H.C., Yacef, K., Mostow, J., Pavlik, P. (eds.) AIED 2013. LNCS (LNAI), vol. 7926, pp. 171–180. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39112-5_18

    Chapter  Google Scholar 

  30. Zappone, A., Di Renzo, M., Debbah, M., Lam, T.T., Qian, X.: Model-aided wireless artificial intelligence: Embedding expert knowledge in deep neural networks towards wireless systems optimization. arXiv preprint arXiv:1808.01672 (2018)

  31. 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. ACM (2017)

    Google Scholar 

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Authors and Affiliations

  1. Université du Québec à Montréal, Montréal, Canada

    Ange Tato & Roger Nkambou

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  1. Ange Tato
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  2. Roger Nkambou
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Correspondence to Roger Nkambou .

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Editors and Affiliations

  1. Georgia State University, Atlanta, GA, USA

    Scott Crossley

  2. University of Craiova, Craiova, Romania

    Elvira Popescu

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Tato, A., Nkambou, R. (2022). Deep Knowledge Tracing on Skills with Small Datasets. In: Crossley, S., Popescu, E. (eds) Intelligent Tutoring Systems. ITS 2022. Lecture Notes in Computer Science, vol 13284. Springer, Cham. https://doi.org/10.1007/978-3-031-09680-8_12

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  • DOI: https://doi.org/10.1007/978-3-031-09680-8_12

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