Abstract
With the ongoing development of online education platforms, knowledge tracing (KT) has become a critical task that can help online education platforms provide personalized education. KT aims to find out students’ knowledge states and predict whether students can correctly answer the question according to their exercise history. However, existing works fail to incorporate question information and ignore some useful contextual information. In this paper, we propose a novel Sequential Self-Attentive model for Knowledge Tracing (SSAKT). SSAKT utilizes question information based on Multidimensional Item Response Theory (MIRT) which can capture the relations between questions and skills. Then SSAKT uses a self-attention layer to capture the relations between questions. Unlike traditional self-attention networks, the self-attention layer in SSAKT uses Long Short-Term Memory networks (LSTM) to perform positional encoding. Moreover, a context module is designed to capture the contextual information. Experiments on four real-world datasets show that SSAKT outperforms existing KT models. We also conduct a case study that shows our model can effectively capture the relations between questions and skills.
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Notes
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Source code will be available at https://github.com/zxlzxlzxlzxlzxl/SSAKT.
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Acknowledgements
This work is partially supported by National Natural Science Foundation of China Nos. U1811263, 62072349, National Key Research and Development Project of China No. 2020YFC1522602.
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Zhang, X., Zhang, J., Lin, N., Yang, X. (2021). Sequential Self-Attentive Model for Knowledge Tracing. In: Farkaš, I., Masulli, P., Otte, S., Wermter, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2021. ICANN 2021. Lecture Notes in Computer Science(), vol 12891. Springer, Cham. https://doi.org/10.1007/978-3-030-86362-3_26
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