ERIC Number: EJ1311500
Record Type: Journal
Publication Date: 2021-Oct
Pages: 21
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-0735-6331
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Machine Learning-Based Student Modeling Methodology for Intelligent Tutoring Systems
Yang, Chunsheng; Chiang, Feng-Kuang; Cheng, Qiangqiang; Ji, Jun
Journal of Educational Computing Research, v59 n6 p1015-1035 Oct 2021
Machine learning-based modeling technology has recently become a powerful technique and tool for developing models for explaining, predicting, and describing system/human behaviors. In developing intelligent education systems or technologies, some research has focused on applying unique machine learning algorithms to build the ad-hoc student models for specific educational systems. However, systematically developing the data-driven student models from the educational data collected over prior educational experiences remains a challenge. We proposed a systematic and comprehensive machine learning-based modeling methodology to develop high-performance predictive student models from the historical educational data to address this issue. This methodology addresses the fundamental modeling issues, from data processing, to modeling, to model deployment. The said methodology can help developing student models for intelligent educational systems. After a detailed description of the proposed machine learning-based methodology, we introduce its application to an intelligent navigation tutoring system. Using the historical data collected in intelligent navigation tutoring systems, we conduct large-scale experiments to build the student models for training systems. The preliminary results proved that the proposed methodology is useful and feasible in developing the high-performance models for various intelligent education systems.
Descriptors: Artificial Intelligence, Intelligent Tutoring Systems, Data Use, Models, Data Collection, Data Analysis
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Publication Type: Journal Articles; Reports - Research
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Language: English
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