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

Li, Zhaoxing and Shi, Lei and Cristea, Alexandra and Zhou, Yunzhan and Xiao, Chenghao and Pan, Ziqi (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. , pp. 348-351. Lecture Notes in Computer Science., 13356

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.

Item Type:Book chapter
Full text:Publisher-imposed embargo until 26 July 2023.
(AM) Accepted Manuscript
File format - PDF
(617Kb)
Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1007/978-3-031-11647-6_67
Publisher statement:The final authenticated version is available online at https://doi.org/10.1007/978-3-031-11647-6_67
Date accepted:25 April 2022
Date deposited:01 September 2022
Date of first online publication:26 July 2022
Date first made open access:26 July 2023

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