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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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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