A deep knowledge tracing model based on multi-head self-attention mechanism

Ya Zhou; Fengzhen Wu; Guimin Huang; Jianxing Lin; Nanxiao Deng; Qingkai Guo

doi:10.1117/12.3011559

7 December 2023 A deep knowledge tracing model based on multi-head self-attention mechanism

Ya Zhou, Fengzhen Wu, Guimin Huang, Jianxing Lin, Nanxiao Deng, Qingkai Guo

Proceedings Volume 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023); 129412N (2023) https://doi.org/10.1117/12.3011559
Event: Third International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 203), 2023, Yinchuan, China

Abstract

How to provide individualized instruction and personalized teaching for students and effectively track their knowledge status in a targeted manner is a key concern in the field of education. The target of Knowledge Tracing (KT) is to use the learner's behavior in historical learning activities to model the students' educational process and forecast the future performance of students. Recently, self-attention has been used several times in the KT field. Traditional models have the problem of losing information under long sequence data. Inspired by Transformer, in this paper, we present a deep knowledge tracing model (MA-DKT) based on the multi-head self-attention mechanism, which employs the LSTM model combined with the multi-head self-attention mechanism to implement a deep knowledge tracing model that is able to catch the data characteristics of the students at different temporal scales. Our model works well on publicly available datasets and achieves some improvement in AUC.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ya Zhou, Fengzhen Wu, Guimin Huang, Jianxing Lin, Nanxiao Deng, and Qingkai Guo "A deep knowledge tracing model based on multi-head self-attention mechanism", Proc. SPIE 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023), 129412N (7 December 2023); https://doi.org/10.1117/12.3011559

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