Development of AIS Using Simulated Learners, Bayesian Networks and Knowledge Elicitation Methods

Emond, Bruno; Smith, Jennifer; Musharraf, Mashrura; Torbati, Reza Zeinali; Billard, Randy; Barnes, Joshua; Veitch, Brian

doi:10.1007/978-3-031-05887-5_11

Cited by 4 publications

(1 citation statement)

References 35 publications

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“…Li et al proposed a student behaviour simulation method based on a Decision Transformer, to generate student behaviour data for ITS training [6,33]. Emond et al [44] proposed an adaptive instructional system (AIS) as a self-improvement system. It presented a methodological approach that incorporates three concurrent research activities: Bayesian networks modelling of learning processes, knowledge elicitation from expert instructors, and the use of simulated learners and tutors for exploring AIS design options.…”

Section: Data-driven Methodsmentioning

confidence: 99%

Sim-GAIL: A generative adversarial imitation learning approach of student modelling for intelligent tutoring systems

Li,

Shi,

Wang

et al. 2023

Neural Comput & Applic

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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.

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Section: Data-driven Methodsmentioning

confidence: 99%