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Lookup NU author(s): Dr Lei ShiORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
The continuous application of Artificial Intelligence (AI) technologies in online education has led to significant progress, especially in the field of Intelligent Tutoring Systems (ITS), online courses and learning management systems (LMS). An important research direction of the field is to provide students with customised learning trajectories via student modelling. Previous studies have shown that customisation of learning trajectories could effectively improve students' learning experiences and outcomes. However, training an ITS that can customise students' learning trajectories suffers from cold-start, time-consumption, human labour-intensity, and cost problems. One feasible approach is to simulate real students' behaviour trajectories through algorithms, to generate data that could be used to train the ITS. Nonetheless, implementing high-accuracy student modelling methods that effectively address these issues remains an ongoing challenge. Traditional simulation methods, in particular, encounter difficulties in ensuring the quality and diversity of the generated data, thereby limiting their capacity to provide Intelligent Tutoring Systems (ITS) with high-fidelity and diverse training data. We thus propose Sim-GAIL, a novel student modelling method based on Generative Adversarial Imitation Learning (GAIL). To the best of our knowledge, it is the first method using GAIL to address the challenge of lacking training data, resulting from the issues mentioned above. We analyse and compare the performance of Sim-GAIL with two traditional Reinforcement Learning-based and Imitation Learning-based methods using action distribution evaluation, cumulative reward evaluation, and offline-policy evaluation. The experiments demonstrate that our method outperforms traditional ones on most metrics. Moreover, we apply our method to a domain plagued by the cold start problem, Knowledge Tracing (KT), and the results show that our novel method could effectively improve the KT model's prediction accuracy in a cold-start scenario.
Author(s): Li Z, Shi L, Wang J, Cristea A, Zhou Y
Publication type: Article
Publication status: Published
Journal: Neural Computing and Applications
Year: 2023
Volume: 35
Pages: 24369-24388
Print publication date: 01/12/2023
Online publication date: 03/10/2023
Acceptance date: 23/08/2023
Date deposited: 24/08/2023
ISSN (print): 0941-0643
ISSN (electronic): 1433-3058
Publisher: Springer Nature
URL: https://doi.org/10.1007/s00521-023-08989-w
DOI: 10.1007/s00521-023-08989-w
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