Predicting at-risk students in the early stage of a blended learning course via machine learning using limited data

Predicting at-risk students in the early stage of a blended learning course via machine learning using limited data

Academic failure is a persistent challenge in education. Despite the limited available data, in this study, we focus on identifying at-risk students in a blended learning (BL) course. Several motivational variables are analyzed to determine their effect on student performance. We use a machine-learn...

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Bibliographic Details
Main Authors: Zahra Azizah, Tomoya Ohyama, Xiumin Zhao, Yuichi Ohkawa, Takashi Mitsuishi
Format: Article
Language:English
Published: Elsevier 2024-12-01
Series:Computers and Education: Artificial Intelligence
Subjects:
Academic failure
Academic performance prediction
Motivation
SRL
Learning analytics
Blended learning
Online Access:http://www.sciencedirect.com/science/article/pii/S2666920X2400064X
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