Abstract
Knowledge tracing aims to diagnose the student’s knowledge status and predict the responses to the next questions, which is a critical task in personalized learning. The existing studies consider more academic features, while this paper introduces DKCT, a deep knowledge tracing model with concept trees, to integrate the hierarchical concept tree that describes the structure of concepts in a question. DKCT casts the knowledge concept tree (KCT) in a question from the views of feature, breadth, and difficulty into a KCT representation at first. Then, DKCT is composed of an encoder network with multi-head attention on the question representations and a decoder network with multi-head attention on the interaction embeddings. Finally, DKCT integrates the student embeddings by using fully connected networks to predict the responses to the next questions. Extensive experiments conducted on two real-world educational datasets show that DKCT has a higher prediction accuracy than the currently popular KT models. This work paves the way to consider KCT for knowledge tracing.
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
References
Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016)
Choi, Y., et al.: Towards an appropriate query, key, and value computation for knowledge tracing. In: Proceedings of the Seventh ACM Conference on L@S, pp. 341–344 (2020)
Emanuel, E.J.: MOOCs taken by educated few. Nature 503(7476), 342–342 (2013)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. SSS, Springer, New York (2009). https://doi.org/10.1007/978-0-387-84858-7
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on CVPR, pp. 770–778 (2016)
Karimi, H., Derr, T., Huang, J., Tang, J.: Online academic course performance prediction using relational graph convolutional neural network. In: EDM (2020)
Liu, Q., et al.: EKT: exercise-aware knowledge tracing for student performance prediction. IEEE TKDE 33(1), 100–115 (2021)
Liu, Q., Shen, S., Huang, Z., Chen, E., Zheng, Y.: A survey of knowledge tracing. arXiv preprint arXiv:2105.15106 (2021)
McInnes, L., Healy, J., Melville, J.: UMAP: uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:1802.03426 (2018)
Nayak, G., Dutta, S., Ajwani, D., Nicholson, P., Sala, A.: Automated assessment of knowledge hierarchy evolution: comparing directed acyclic graphs. Inf. Retrieval J. 22(3), 256–284 (2019)
Nguyen, T.: The effectiveness of online learning: beyond no significant difference and future horizons. MERLOT J. Online Learn. Teach. 11, 309–319 (2015)
Nguyen, X.P., Joty, S., Hoi, S.C., Socher, R.: Tree-structured attention with hierarchical accumulation. arXiv preprint arXiv:2002.08046 (2020)
Pandey, S., Karypis, G.: A self-attentive model for knowledge tracing. arXiv preprint arXiv:1907.06837 (2019)
Piech, C., et al.: Deep knowledge tracing. arXiv preprint arXiv:1506.05908 (2015)
Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D., Lillicrap, T.: One-shot learning with memory-augmented neural networks. arXiv preprint arXiv:1605.06065 (2016)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)
Wang, Z., et al.: Instructions and guide for diagnostic questions: the NeurIPS 2020 education challenge. arXiv preprint arXiv:2007.12061 (2020)
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)
Zaremba, W., Sutskever, I., Vinyals, O.: Recurrent neural network regularization. arXiv preprint arXiv:1409.2329 (2014)
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)
Zhang, Y., An, R., Cui, J., Shang, X.: Undergraduate grade prediction in Chinese higher education using convolutional neural networks. In: LAK21, pp. 462–468 (2021)
Zhang, Y., Dai, H., Yun, Y., Liu, S., Lan, A., Shang, X.: Meta-knowledge dictionary learning on 1-bit response data for student knowledge diagnosis. Knowl. Based Syst. 205, 106290 (2020)
Acknowledgment
This study was funded in part by the National Natural Science Foundation of China (62272392, U1811262, 61802313), the Key Research and Development Program of China (2020AAA0108500), the Reformation Research on Education and Teaching at Northwestern Polytechnical University (2022JGY62), the Higher Research Funding on International Talent cultivation at Northwestern Polytechnical University (GJGZZD202202).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, Y., An, R., Zhang, W., Liu, S., Shang, X. (2023). Deep Knowledge Tracing with Concept Trees. In: Yang, X., et al. Advanced Data Mining and Applications. ADMA 2023. Lecture Notes in Computer Science(), vol 14177. Springer, Cham. https://doi.org/10.1007/978-3-031-46664-9_26
Download citation
DOI: https://doi.org/10.1007/978-3-031-46664-9_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-46663-2
Online ISBN: 978-3-031-46664-9
eBook Packages: Computer ScienceComputer Science (R0)