An evaluation model based on procedural behaviors for predicting MOOC learning performance: students’ online learning behavior analytics and algorithms construction
Interactive Technology and Smart Education
ISSN: 1741-5659
Article publication date: 6 February 2023
Issue publication date: 1 September 2023
Abstract
Purpose
The purpose of this study is to set up an evaluation model to predict massive open online courses (MOOC) learning performance by analyzing MOOC learners’ online learning behaviors, and comparing three algorithms – multiple linear regression (MLR), multilayer perceptron (MLP) and classification and regression tree (CART).
Design/methodology/approach
Through literature review and analysis of data correlation in the original database, a framework of online learning behavior indicators containing 26 behaviors was constructed. The degree of correlation with the final learning performance was analyzed based on learners’ system interaction behavior, resource interaction behavior, social interaction behavior and independent learning behavior. A total of 12 behaviors highly correlated to learning performance were extracted as major indicators, and the MLR method, MLP method and CART method were used as typical algorithms to evaluate learners’ MOOC learning performance.
Findings
The behavioral indicator framework constructed in this study can effectively analyze learners’ learning, and the evaluation model constructed using the MLP method (89.91%) and CART method (90.29%) can better achieve the prediction of MOOC learners’ learning performance than using MLR method (83.64%).
Originality/value
This study explores the patterns and characteristics among different learning behaviors and constructs an effective prediction model for MOOC learners’ learning performance, which can help teachers understand learners’ learning status, locate learners with learning difficulties promptly and provide targeted instructional interventions at the right time to improve teaching quality.
Keywords
Acknowledgements
This research was financially supported by the National Natural Science Foundation in China (62277018; 62237001), Ministry of Education in China Project of Humanities and Social Sciences (22YJC880106), the Major Project of Social Science in South China Normal University (ZDPY2208), the Major basic research and applied research projects of Guangdong Education Department (#2017WZDXM004).
Word count of the manuscript: 8,817.
Conflicts of interest: The authors have no financial or proprietary interests in any material discussed in this article.
Ethics approval: All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.
Consent: Informed consent was obtained from all individual participants included in the study.
Citation
Tong, Y. and Zhan, Z. (2023), "An evaluation model based on procedural behaviors for predicting MOOC learning performance: students’ online learning behavior analytics and algorithms construction", Interactive Technology and Smart Education, Vol. 20 No. 3, pp. 291-312. https://doi.org/10.1108/ITSE-10-2022-0133
Publisher
:Emerald Publishing Limited
Copyright © 2023, Emerald Publishing Limited