Abstract
Engagement plays an essential role in the learning process. Recognition of learning engagement in the classroom helps us understand the student’s learning state and optimize the teaching and study processes. Traditional recognition methods such as self-report and teacher observation are time-consuming and obtrusive to satisfy the needs of large-scale classrooms. With the development of big data analysis and artificial intelligence, applying intelligent methods such as deep learning to recognize learning engagement has become the research hotspot in education. In this paper, based on non-invasive classroom videos, first, a multi-cues classroom learning engagement database was constructed. Then, we introduced the power IoU loss function to You Only Look Once version 5 (YOLOv5) to detect the students and obtained a precision of 95.4%. Finally, we designed a bimodal learning engagement recognition method based on ResNet50 and CoAtNet. Our proposed bimodal learning engagement method obtained an accuracy of 93.94% using the KNN classifier. The experimental results confirmed that the proposed method outperforms most state-of-the-art techniques.
Funder
National Collaborative Innovation Experimental Base Construction Project for Teacher Develop-ment of Central China Normal University
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference38 articles.
1. Engagement, achievement, and teacher classroom practices in mathematics: Insights from TIMSS 2011 and PISA 2012
2. Multi-modal Learning Analysis for Group Multi Engagement Feature Portrait of Collaborative Learning;Ma;J. Distance Educ.,2022
3. School Engagement: Potential of the Concept, State of the Evidence
4. Predicting affective states expressed through an emote-aloud procedure from AutoTutor’s mixed-initiative dialogue;D’Mello;Int. J. Artif. Intell. Educ.,2006
5. Multimodal analysis of the implicit affective channel in computer-mediated textual communication;Grafsgaard;Proceedings of the 14th ACM International Conference on Multimodal Interaction,2012
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