Deep knowledge tracing model integrated with attention mechanism

Pei Pei; Rodolfo C. Raga Jr.; Mideth Abisado

doi:10.1117/12.2685085

19 October 2023 Deep knowledge tracing model integrated with attention mechanism

Pei Pei, Rodolfo C. Raga Jr., Mideth Abisado

Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 127090C (2023) https://doi.org/10.1117/12.2685085
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China

Abstract

In view of the lack of interpretability and data dependence of RNN-based deep knowledge tracking model, a deep knowledge tracing model integrating attention mechanism is proposed. First, learn the embedded representation of students' historical interaction, and then learn specific weights based on the topic's attention mechanism to identify and strengthen the impact of students' historical interaction on the knowledge state at each moment to different degrees; comparative experiments will be carried out on two data sets, and the best performance will be achieved in the ASSISTments2012 dataset, and the problem of long sequence dependence will be alleviated to some extent. This model can capture students' knowledge state more accurately and predict students' future performance more efficiently.

Citation Download Citation

Pei Pei, Rodolfo C. Raga Jr., and Mideth Abisado "Deep knowledge tracing model integrated with attention mechanism", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 127090C (19 October 2023); https://doi.org/10.1117/12.2685085

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