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Conference Papers Year : 2023

Deep Knowledge Tracing is an implicit dynamic multidimensional item response theory model

Abstract

Knowledge tracing consists in predicting the performance of some students on new questions given their performance on previous questions, and can be a prior step to optimizing assessment and learning. Deep knowledge tracing (DKT) is a competitive model for knowledge tracing relying on recurrent neural networks, even if some simpler models may match its performance. However, little is known about why DKT works so well. In this paper, we frame deep knowledge tracing as a encoderdecoder architecture. This viewpoint not only allows us to propose better models in terms of performance, simplicity or expressivity but also opens up promising avenues for future research directions. In particular, we show on several small and large datasets that a simpler decoder, with possibly fewer parameters than the one used by DKT, can predict student performance better.
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Dates and versions

hal-04180391 , version 1 (12-08-2023)
hal-04180391 , version 2 (04-12-2023)
hal-04180391 , version 3 (05-12-2023)

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Jill-Jênn Vie, Hisashi Kashima. Deep Knowledge Tracing is an implicit dynamic multidimensional item response theory model. ICCE 2023 - The 31st International Conference on Computers in Education, Asia-Pacific Society for Computers in Education, Dec 2023, Matsue, Shimane, Japan. ⟨hal-04180391v3⟩
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