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Learning Performance Prediction and Alert Method in Hybrid Learning

Author

Listed:
  • 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)

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.

Suggested Citation

  • Huijuan Zhuang & Jing Dong & Su Mu & Haiming Liu, 2022. "Learning Performance Prediction and Alert Method in Hybrid Learning," Sustainability, MDPI, vol. 14(22), pages 1-16, November.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:22:p:14685-:d:966371
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    References listed on IDEAS

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    1. Xuesong Zhai & Xiaoyan Chu & Ching Sing Chai & Morris Siu Yung Jong & Andreja Istenic & Michael Spector & Jia-Bao Liu & Jing Yuan & Yan Li & Ning Cai, 2021. "A Review of Artificial Intelligence (AI) in Education from 2010 to 2020," Complexity, Hindawi, vol. 2021, pages 1-18, April.
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