SimStu-transformer: a transformer-based approach to simulating student behaviour

Li, Zhaoxing, Shi, Lei, Cristea, Alexandra, Zhou, Yunzhan, Xiao, Chenghao and Pan, Ziqi (2022) SimStu-transformer: a transformer-based approach to simulating student behaviour. Rodrigo, Maria Mercedes, Matsuda, Noburu, Cristea, Alexandra I. and Dimitrova, Vania (eds.) In Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium: 23rd International Conference, AIED 2022, Durham, UK, July 27–31, 2022, Proceedings,. vol. 13356, Springer Cham. pp. 348-351 . (doi:10.1007/978-3-031-11647-6_67).

Abstract

Lacking behavioural data between students and an Intelligent Tutoring System (ITS) has been an obstacle for improving its personalisation capability. One feasible solution is to train “sim students”, who simulate real students’ behaviour in the ITS. We can then use their generated behavioural data to train the ITS to offer real students personalised learning strategies and trajectories. In this paper, we thus propose SimStu-Transformer, developed based on the Decision Transformer algorithm, to generate learning behavioural data.

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Contributors

Author: Zhaoxing Li

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