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Towards Student Behaviour Simulation: A Decision Transformer Based Approach

Lookup NU author(s): Dr Lei ShiORCiD



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


With the rapid development of Artificial Intelligence (AI), an increasing number of Machine Learning (ML) technologies have been widely applied in many aspects of life. In the field of education, Intelligence Tutoring Systems (ITS) have also made significant advancements using these technologies. Developing different teaching strategies automatically, according to mined student characteristics and learning styles, could significantly enhance students’ learning efficiency and performance. This requires the ITS to recommend different learning strategies and trajectories for different individual students. However, one of the greatest challenges is the scarcity of data sets providing interactions between students and ITS, for training such ITS. One promising solution to this challenge is to train “sim students”, which imitate real students’ behaviour while using the ITS. The simulated interactions between these sim students and the ITS can then be generated and used to train the ITS to provide personalised learning strategies and trajectories to real students. In this paper, we thus propose SimStu, built upon a Decision Transformer, to generate learning behavioural data to improve the performance of the trained ITS models. The experimental results suggest that our SimStu could model real students well in terms of action frequency distribution. Moreover, we evaluate SimStu in an emerging ITS technology, Knowledge Tracing. The results indicate that SimStu could improve the efficiency of ITS training.

Publication metadata

Author(s): Li Zhaoxing, Shi Lei, Zhou Yunzhan, Wang Jindi

Editor(s): Frasson C; Mylonas P; Troussas C

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: Augmented Intelligence and Intelligent Tutoring Systems, 19th International Conference, ITS 2023

Year of Conference: 2023

Pages: 553–562

Print publication date: 16/05/2023

Online publication date: 22/05/2023

Acceptance date: 13/03/2023

Date deposited: 27/05/2023

ISSN: 0302-9743

Publisher: Springer


DOI: 10.1007/978-3-031-32883-1_49

ePrints DOI: 10.57711/9rd9-fk86

Library holdings: Search Newcastle University Library for this item

Series Title: Lecture Notes in Computer Science

ISBN: 9783031328824