Machine Learning-Based Student Modeling Methodology for Intelligent Tutoring Systems. (October 2021)

• Record Type: Journal Article
• Authors: Yang, Chunsheng; Chiang, Feng-Kuang; Cheng, Qiangqiang; Ji, Jun
• Is Part Of: Journal of educational computing research. Volume 59:Number 6(2021)
• Page Start: 1015
• Page End: 1035
• Publication Date: 2021-10
• Subjects: machine learning -- intelligent tutoring systems -- modeling -- artificial intelligence; Computer literacy -- Periodicals; Computer-assisted instruction -- Periodicals; Computer managed instruction -- Periodicals; Education -- Data processing -- Periodicals; 371.334
• DOI: 10.1177/0735633120986256
• Languages: English
• ISSNs: 0735-6331

Abstract

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.