Abstract
The field of Knowledge Tracing (KT) aims to understand how students learn and master knowledge over time by analyzing their historical behaviour data. To achieve this goal, many researchers have proposed KT models that use data from Intelligent Tutoring Systems (ITS) to predict students’ subsequent actions. However, with the development of ITS, large-scale datasets containing long-sequence data began to emerge. Recent deep learning based KT models face obstacles such as low efficiency, low accuracy, and low interpretability when dealing with large-scale datasets containing long-sequence data. To address these issues and promote the sustainable development of ITS, we propose a LSTM BERT-based Knowledge Tracing model for long sequence data processing, namely LBKT, which uses a BERT-based architecture with a Rasch model-based embeddings block to deal with different difficulty levels information and an LSTM block to process the sequential characteristic in students’ actions. LBKT achieves the best performance on most benchmark datasets on the metrics of ACC and AUC.
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References
Abdelrahman, G., Wang, Q., Nunes, B.: Knowledge tracing: a survey. ACM Comput. Surv. 55(11), 1–37 (2023)
Abdelrahman, G., Wang, Q., Nunes, B.P.: Knowledge tracing: a survey. ACM Comput. Surv. 55, 1–37 (2022)
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
Chang, H.S., Hsu, H.J., Chen, K.T.: Modeling exercise relationships in e-learning: a unified approach. In: EDM, pp. 532–535 (2015)
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
Corbett, A.T., Anderson, J.R.: Knowledge tracing: modeling the acquisition of procedural knowledge. User Model. User-Adap. Inter. 4(4), 253–278 (1994). https://doi.org/10.1007/BF01099821
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018)
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)
Jiang, Z.H., Yu, W., Zhou, D., Chen, Y., Feng, J., Yan, S.: ConvBERT: improving BERT with span-based dynamic convolution. Adv. Neural. Inf. Process. Syst. 33, 12837–12848 (2020)
Käser, T., Klingler, S., Schwing, A.G., Gross, M.: Dynamic Bayesian networks for student modeling. IEEE Trans. Learn. Technol. 10, 450–462 (2017)
Lee, U., Park, Y., Kim, Y., Choi, S., Kim, H.: MonacoBERT: monotonic attention based convBERT for knowledge tracing. arXiv preprint arXiv:2208.12615 (2022)
Li, Z.: Deep reinforcement learning approaches for technology enhanced learning. Ph. D. thesis, Durham University (2023)
Li, Z., Jacobsen, M., Shi, L., Zhou, Y., Wang, J.: Broader and deeper: a multi-features with latent relations BERT knowledge tracing model. In: Viberg, O., Jivet, I., Muñoz-Merino, P., Perifanou, M., Papathoma, T. (eds.) European Conference on Technology Enhanced Learning, pp. 183–197. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-42682-7_13
Li, Z., Shi, L., Cristea, A., Zhou, Y., Xiao, C., Pan, Z.: SimStu-transformer: a transformer-based approach to simulating student behaviour. In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds.) International Conference on Artificial Intelligence in Education, pp. 348–351. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-11647-6_67
Li, Z., Shi, L., Cristea, A.I., Zhou, Y.: A survey of collaborative reinforcement learning: interactive methods and design patterns. In: Proceedings of the 2021 ACM Designing Interactive Systems Conference, pp. 1579–1590 (2021)
Li, Z., Shi, L., Wang, J., Cristea, A.I., Zhou, Y.: Sim-GAIL: a generative adversarial imitation learning approach of student modelling for intelligent tutoring systems. Neural Comput. Appl. 35(34), 24369–24388 (2023)
Li, Z., Shi, L., Zhou, Y., Wang, J.: Towards student behaviour simulation: a decision transformer based approach. In: International Conference on Intelligent Tutoring Systems, pp. 553–562. Springer (2023). https://doi.org/10.1007/978-3-031-32883-1_49
Liu, Y., Zhou, J., Lin, W.: Efficient attentive knowledge tracing for long-tail distributed records. In: 2021 IEEE/ACIS 6th International Conference on Big Data, Cloud Computing, and Data Science (BCD), pp. 104–109. IEEE (2021)
Pandey, S., Karypis, G.: A self-attentive model for knowledge tracing. arXiv preprint arXiv:1907.06837 (2019)
Pavlik Jr, P.I., Cen, H., Koedinger, K.R.: Performance factors analysis–a new alternative to knowledge tracing. In: Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling, Proceedings of the 14th International Conference on Artificial Intelligence in Education, AIED 2009, July 6-10, 2009, Brighton, UK, vol. 200, pp. 531–538 (2009)
Rasch, G.: Probabilistic Models for Some Intelligence and Attainment Tests. (1993)
Shin, D., Shim, Y., Yu, H., Lee, S., Kim, B., Choi, Y.: Saint+: integrating temporal features for EdNet correctness prediction. In: LAK21: 11th International Learning Analytics and Knowledge Conference, pp. 490–496 (2021)
Sun, F., et al.: BERT4Rec: sequential recommendation with bidirectional encoder representations from transformer. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 1441–1450 (2019)
Sun, X., Zhao, X., Ma, Y., Yuan, X., He, F., Feng, J.: Muti-behavior features based knowledge tracking using decision tree improved DKVMN. In: Proceedings of the ACM Turing Celebration Conference-China, pp. 1–6 (2019)
Tiana, Z., Zhengc, G., Flanaganb, B., Mic, J., Ogatab, H.: BEKT: deep knowledge tracing with bidirectional encoder representations from transformers. In: Proceedings of the 29th International Conference on Computers in Education (2021)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Villano, M.: Probabilistic student models: Bayesian belief networks and knowledge space theory. In: International Conference on Intelligent Tutoring Systems, pp. 491–498. Springer (1992). https://doi.org/10.1007/3-540-55606-0_58
Wang, J., Ivrissimtzis, I., Li, Z., Shi, L.: Comparative efficacy of 2D and 3D virtual reality games in American sign language learning. In: The 31st IEEE Conference on Virtual Reality and 3D User Interfaces. Newcastle University (2024)
Wang, J., Ivrissimtzis, I., Li, Z., Shi, L.: Impact of personalised AI chat assistant on mediated human-human textual conversations: exploring female-male differences. In: Companion Proceedings of the 29th International Conference on Intelligent User Interfaces, pp. 78–83 (2024)
Wang, J., Ivrissimtzis, I., Li, Z., Zhou, Y., Shi, L.: Exploring the potential of immersive virtual environments for learning American sign language. In: European Conference on Technology Enhanced Learning, pp. 459–474. Springer (2023). https://doi.org/10.1007/978-3-031-42682-7_31
Wang, J., Ivrissimtzis, I., Li, Z., Zhou, Y., Shi, L.: User-defined hand gesture interface to improve user experience of learning American sign language. In: International Conference on Intelligent Tutoring Systems, pp. 479–490. Springer (2023). https://doi.org/10.1007/978-3-031-32883-1_43
Zhang, X., Zhang, J., Lin, N., Yang, X.: Sequential self-attentive model for knowledge tracing. In: Farkaš, I., Masulli, P., Otte, S., Wermter, S. (eds.) ICANN 2021. LNCS, vol. 12891, pp. 318–330. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-86362-3_26
Acknowledgments
This work was supported by the UK Engineering and Physical Sciences Research Council (EPSRC) through a Turing AI Fellowship (EP/V022067/1) on Citizen-Centric AI Systems (https://ccais.ac.uk/) and through the AutoTrust Platform Grant (EP/R029563/1).
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Li, Z., Yang, J., Wang, J., Shi, L., Feng, J., Stein, S. (2024). LBKT: A LSTM BERT-Based Knowledge Tracing Model for Long-Sequence Data. In: Sifaleras, A., Lin, F. (eds) Generative Intelligence and Intelligent Tutoring Systems. ITS 2024. Lecture Notes in Computer Science, vol 14799. Springer, Cham. https://doi.org/10.1007/978-3-031-63031-6_15
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