SimStu-Transformer: A Transformer-Based Approach to Simulating Student Behaviour

Li, Zhaoxing; Shi, Lei; Cristea, Alexandra; Zhou, Yunzhan; Xiao, Chenghao; Pan, Ziqi

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.

Citation

Li, Z., Shi, L., Cristea, A., Zhou, Y., Xiao, C., & Pan, Z. (2022). SimStu-Transformer: A Transformer-Based Approach to Simulating Student Behaviour. In Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium (348-351). Springer, Cham. https://doi.org/10.1007/978-3-031-11647-6_67

• Online Publication Date: Jul 26, 2022
• Publication Date: 2022
• Deposit Date: Aug 31, 2022
• Publicly Available Date: Jul 27, 2023
• Pages: 348-351
• Series Title: Lecture Notes in Computer Science
• Series Number: 13356
• Book Title: Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium
• ISBN: 978-3-031-11646-9
• DOI: https://doi.org/10.1007/978-3-031-11647-6_67
• Public URL: https://durham-repository.worktribe.com/output/1620863
• Contract Date: Apr 25, 2022