Towards Student Behaviour Simulation: A Decision Transformer Based Approach

Li, Zhaoxing; Shi, Lei; Zhou, Yinsheng; Wang, J.

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

Cited by 3 publications

(3 citation statements)

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“…Limited by the quantity of high-quality datasets, the previous data-driven model struggled to keep up with the expanding requirements of ITS development. 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.…”

Section: Data-driven Methodsmentioning

confidence: 99%

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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%

“…Knowledge-based methods refer to utilising human knowledge to address issues that would normally require human intelligence [7,31]. Datadriven methods simulate students' learning trajectories through massive student learning records data [6,32,33].…”

Section: Student Modellingmentioning

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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“…BERT's bidirectional training and large pre-training corpus have set new benchmarks in understanding natural language, with applications extending into image processing, recommendation systems, and music generation [7,9,23]. Despite their success, BERT variants in KT have not achieved superior performance on complex, long-sequence datasets [25,13,14,17,12,16].…”

Section: Transformer-based Model and Applicationmentioning

confidence: 99%

LBKT: A LSTM BERT-Based Knowledge Tracing Model for Long-Sequence Data

Li,

Yang,

Wang

et al. 2024

Lecture Notes in Computer Science

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The field of Knowledge Tracing (KT) aims to understand how students learn and master knowledge over time by analyzing their historical behaviour data. To achieve this goal, many researchers have proposed KT models that use data from Intelligent Tutoring Systems (ITS) to predict students' subsequent actions. However, with the development of ITS, large-scale datasets containing long-sequence data began to emerge. Recent deep learning based KT models face obstacles such as low efficiency, low accuracy, and low interpretability when dealing with large-scale datasets containing long-sequence data. To address these issues and promote the sustainable development of ITS, we propose a LSTM BERT-based Knowledge Tracing model for long sequence data processing, namely LBKT, which uses a BERT-based architecture with a Rasch model-based embeddings block to deal with different difficulty levels information and an LSTM block to process the sequential characteristic in students' actions. LBKT achieves the best performance on most benchmark datasets on the metrics of ACC and AUC.

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