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.

中文翻译：

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课堂视频双峰学习参与度识别

参与在学习过程中起着至关重要的作用。对课堂学习参与度的认可有助于我们了解学生的学习状态并优化教学和学习过程。传统的自我报告、教师观察等识别方式耗时且突兀，无法满足大型教室的需求。随着大数据分析和人工智能的发展，应用深度学习等智能方法识别学习投入已成为教育领域的研究热点。本文基于非侵入式课堂视频，首先构建了一个多线索课堂学习参与度数据库。然后，我们将 power IoU 损失函数引入 You Only Look Once 版本 5 (YOLOv5) 来检测学生，得到了 95.4% 的准确率。最后，我们设计了一种基于 ResNet50 和 CoAtNet 的双模学习参与识别方法。我们提出的双峰学习参与方法使用 KNN 分类器获得了 93.94% 的准确率。实验结果证实，所提出的方法优于大多数最先进的技术。