Abstract
Monitoring student knowledge states or skill acquisition levels known as knowledge tracing, is a fundamental part of intelligent tutoring systems. Despite its inherent challenges, recent deep neural networks based knowledge tracing models have achieved great success, which is largely from models’ ability to learn sequential dependencies of questions in student exercise data. However, in addition to sequential information, questions inherently exhibit side relations, which can enrich our understandings about student knowledge states and has great potentials to advance knowledge tracing. Thus, in this paper, we exploit side relations to improve knowledge tracing and design a novel framework DTKS. The experimental results on real education data validate the effectiveness of the proposed framework and demonstrate the importance of side information in knowledge tracing.
Z. Wang and X. Feng—Work was done when the authors did internship in TAL AI Lab.
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Acknowledgements
Zhiwei Wang and Jiliang Tang are supported by the National Science Foundation (NSF) under grant numbers IIS-1714741, IIS-1715940, IIS-1845081 and CNS-1815636, and a grant from Criteo Faculty Research Award.
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Wang, Z., Feng, X., Tang, J., Huang, G.Y., Liu, Z. (2019). Deep Knowledge Tracing with Side Information. In: Isotani, S., Millán, E., Ogan, A., Hastings, P., McLaren, B., Luckin, R. (eds) Artificial Intelligence in Education. AIED 2019. Lecture Notes in Computer Science(), vol 11626. Springer, Cham. https://doi.org/10.1007/978-3-030-23207-8_56
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DOI: https://doi.org/10.1007/978-3-030-23207-8_56
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