Abstract
Knowledge Tracing aims to model a student’s knowledge state from her past learning interactions and predict her performance in future. Although structures such as positional encoding or forgetting gate have already been used in Knowledge Tracing models, positional information with great potential is not fully utilized. In this paper, we propose a Position-aware Self-Attentive Knowledge Tracing (PAKT) model with a position supervision mechanism. Massive experimental results show that PAKT outperforms other benchmarks on several popular datasets.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
References
Abdelrahman, G., Wang, Q.: Knowledge tracing with sequential key-value memory networks. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 175–184 (2019)
Burns, H., Luckhardt, C.A., Parlett, J.W., Redfield, C.L.: Intelligent Tutoring Systems: Evolutions in Design. Psychology Press (2014)
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
Corbett, A.T., Anderson, J.R.: Knowledge tracing: modeling the acquisition of procedural knowledge. User Modeling User-Adapted Interact. 4(4), 253–278 (1994)
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)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, 7–9 May 2015, Conference Track Proceedings (2015)
Pandey, S., Karypis, G.: A self attentive model for knowledge tracing. In: Desmarais, M.C., Lynch, C.F., Merceron, A., Nkambou, R. (eds.) Proceedings of the 12th International Conference on Educational Data Mining, EDM 2019, Montréal, Canada, 2–5 July 2019. International Educational Data Mining Society (IEDMS) (2019)
Paszke, A., et al.: Automatic differentiation in pytorch (2017)
Piech, C., Bassen, J., Huang, J., Ganguli, S., Sahami, M., Guibas, L.J., Sohl-Dickstein, J.: Deep knowledge tracing. Advances in Neural Information Processing Systems 28, 505–513 (2015)
Thai-Nghe, N., Horváth, T., Schmidt-Thieme, L.: Factorization models for forecasting student performance. In: Educational Data Mining 2011 (2010)
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, WWW 2017, pp. 765–774. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE (2017). https://doi.org/10.1145/3038912.3052580
Zhao, J., Bhatt, S., Thille, C., Zimmaro, D., Gattani, N.: Interpretable personalized knowledge tracing and next learning activity recommendation. In: Proceedings of the Seventh ACM Conference on Learning @ Scale, pp. 325–328. L@S 2020. Association for Computing Machinery, New York (2020). https://doi.org/10.1145/3386527.3406739
Acknowledgements
This research was partially supported by the National Key R&D Program of China (No. 2018YFB2100800) and the National Natural Science Foundation of China (No. 61772132).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Ouyang, Y., Zhou, Y., Zhang, H., Rong, W., Xiong, Z. (2021). PAKT: A Position-Aware Self-attentive Approach for Knowledge Tracing. In: Roll, I., McNamara, D., Sosnovsky, S., Luckin, R., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2021. Lecture Notes in Computer Science(), vol 12749. Springer, Cham. https://doi.org/10.1007/978-3-030-78270-2_51
Download citation
DOI: https://doi.org/10.1007/978-3-030-78270-2_51
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-78269-6
Online ISBN: 978-3-030-78270-2
eBook Packages: Computer ScienceComputer Science (R0)