Abstract
Knowledge tracing aims to estimate students’ knowledge state or skill mastering level over time, which is evolving into an essential task in educational technology. Traditional knowledge tracing algorithms generally use one or a few features to predict students’ behaviour and do not consider the latent relations between these features, which could be limiting and disregarding important information in the features. In this paper, we propose MLFBK: A Multi-Features with Latent Relations BERT Knowledge Tracing model, which is a novel BERT based Knowledge Tracing approach that utilises multiple features and mines latent relations between features to improve the performance of the Knowledge Tracing model. Specifically, our algorithm leverages four data features (student_id, skill_id, item_id, and response_id, as well as three meaningful latent relations among features to improve the performance: individual skill mastery, ability profile of students (learning transfer across skills), and problem difficulty. By incorporating these explicit features, latent relations, and the strength of the BERT model, we achieve higher accuracy and efficiency in knowledge tracing tasks. We use t-SNE as a visualisation tool to analyse different embedding strategies. Moreover, we conduct ablation studies and activation function evaluation to evaluate our model. Experimental results demonstrate that our algorithm outperforms baseline methods and demonstrates good interpretability.
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Notes
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Source code and datasets are available at https://github.com/Zhaoxing-Li/MLFBK.
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Li, Z., Jacobsen, M., Shi, L., Zhou, Y., Wang, J. (2023). 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) Responsive and Sustainable Educational Futures. EC-TEL 2023. Lecture Notes in Computer Science, vol 14200. Springer, Cham. https://doi.org/10.1007/978-3-031-42682-7_13
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