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Online Learning Engagement Recognition Using Bidirectional Long-Term Recurrent Convolutional Networks

Author

Listed:
  • Yujian Ma

    (Hubei Research Center for Educational Informationization, Central China Normal University, Wuhan 430079, China
    Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China)

  • Yantao Wei

    (Hubei Research Center for Educational Informationization, Central China Normal University, Wuhan 430079, China
    Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China)

  • Yafei Shi

    (School of Educational Technology, Northwest Normal University, Lanzhou 730070, China)

  • Xiuhan Li

    (Hubei Research Center for Educational Informationization, Central China Normal University, Wuhan 430079, China
    Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China)

  • Yi Tian

    (Hubei Research Center for Educational Informationization, Central China Normal University, Wuhan 430079, China
    Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China)

  • Zhongjin Zhao

    (Hubei Research Center for Educational Informationization, Central China Normal University, Wuhan 430079, China
    Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China)

Abstract

Background: Online learning is currently adopted by educational institutions worldwide to provide students with ongoing education during the COVID-19 pandemic. However, online learning has seen students lose interest and become anxious, which affects learning performance and leads to dropout. Thus, measuring students’ engagement in online learning has become imperative. It is challenging to recognize online learning engagement due to the lack of effective recognition methods and publicly accessible datasets. Methods: This study gathered a large number of online learning videos of students at a normal university. Engagement cues were used to annotate the dataset, which was constructed with three levels of engagement: low engagement, engagement, and high engagement. Then, we introduced a bi-directional long-term recurrent convolutional network (BiLRCN) for online learning engagement recognition in video. Result: An online learning engagement dataset has been constructed. We evaluated six methods using precision and recall, where BiLRCN obtained the best performance. Conclusions: Both category balance and category similarity of the data affect the performance of the results; it is more appropriate to consider learning engagement as a process-based evaluation; learning engagement can provide intervention strategies for teachers from a variety of perspectives and is associated with learning performance. Dataset construction and deep learning methods need to be improved, and learning data management also deserves attention.

Suggested Citation

  • Yujian Ma & Yantao Wei & Yafei Shi & Xiuhan Li & Yi Tian & Zhongjin Zhao, 2022. "Online Learning Engagement Recognition Using Bidirectional Long-Term Recurrent Convolutional Networks," Sustainability, MDPI, vol. 15(1), pages 1-14, December.
  • Handle: RePEc:gam:jsusta:v:15:y:2022:i:1:p:198-:d:1012161
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