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An Evaluation Model Based on Procedural Behaviors for Predicting MOOC Learning Performance: Students' Online Learning Behavior Analytics and Algorithms Construction

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Název: An Evaluation Model Based on Procedural Behaviors for Predicting MOOC Learning Performance: Students' Online Learning Behavior Analytics and Algorithms Construction
Jazyk: English
Autoři: Tong, Yao, Zhan, Zehui
Zdroj: Interactive Technology and Smart Education. 2023 20(3):291-312.
Dostupnost: Emerald Publishing Limited. Howard House, Wagon Lane, Bingley, West Yorkshire, BD16 1WA, UK. Tel: +44-1274-777700; Fax: +44-1274-785201; e-mail: emerald@emeraldinsight.com; Web site: http://www.emerald.com/insight
Peer Reviewed: Y
Page Count: 22
Datum vydání: 2023
Druh dokumentu: Journal Articles
Reports - Research
Descriptors: MOOCs, Online Courses, Learning Analytics, Prediction, Student Behavior, Correlation, Interaction, Resources, Social Influences, Independent Study, Electronic Learning, Foreign Countries
Geografický termín: China
DOI: 10.1108/ITSE-10-2022-0133
ISSN: 1741-5659
1758-8510
Abstrakt: 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.
Abstractor: As Provided
Entry Date: 2023
Přístupové číslo: EJ1391238
Databáze: ERIC
Popis
Abstrakt: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.
ISSN:1741-5659
1758-8510
DOI:10.1108/ITSE-10-2022-0133