Learning Performance Prediction and Alert Method in Hybrid Learning

Huijuan Zhuang, Jing Dong, Su Mu () and Haiming Liu
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Huijuan Zhuang: International Business College, South China Normal University, Guangzhou 510631, China
Jing Dong: School of History and Culture, Qufu Normal University, Jining 273165, China
Su Mu: Institute of Artificial Intelligence in Education, South China Normal University, Guangzhou 510631, China
Haiming Liu: School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK

Sustainability, 2022, vol. 14, issue 22, 1-16

Abstract: In online learning, students’ learning data such as time and logs are commonly used to predict the student’s learning performance. In a hybrid context, learning activities occur both online and offline. Thus, how to integrate online and offline learning data effectively for an accurate learning performance prediction becomes very challenging. This paper proposes a “prediction and alert” model for students’ learning performance in a hybrid learning context. The model is developed and evaluated through analyzing the 16-week (one semester) attributes of English learning data of 50 students in the eighth grade. Six significant variables were determined as learning performance attributes, namely, qualified rate, excellent rate, scores, number of practice sessions, practice time, and completion. The proposed model was put into actual practice through four months of application and modification, in which a sample of 50 middle school students participated. The model shows the feasibility and effectiveness of data analysis for hybrid learning. It can support students’ continuous online and offline learning more effectively.

Keywords: learning performance prediction; hybrid learning; ridge regression; clustering algorithm (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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