Yan Lijuan, Wu Xiaotao, Wang Yi
College of Mathematics and Statistics, Huanggang Normal University, Huanggang, Hubei, China.
PLoS One. 2025 Jun 10;20(6):e0325377. doi: 10.1371/journal.pone.0325377. eCollection 2025.
Student engagement assessment plays an important role in enhancing students' positive performance and optimizing teaching methods. In this paper, a multimodal deep learning framework is proposed for student engagement assessment. Based on this framework, we propose a method for engagement assessment that utilizes data from three modalities: video, text, and logs. This method implements the extraction of engagement indicators, the fusion of asynchronous data, the use of deep learning models to evaluate engagement levels, and the use of gradient magnitude mapping to further distinguish subtle differences between engagement levels. In subsequent empirical studies, we explore the applicability of several popular deep CNN models in this method and validate the reliability of the engagement quantification results using statistical methods. The analysis results demonstrate that the framework, which combines multimodal asynchronous data fusion and deep learning models to assess engagement, is both effective and practical.
学生参与度评估在提高学生的积极表现和优化教学方法方面发挥着重要作用。本文提出了一种用于学生参与度评估的多模态深度学习框架。基于此框架，我们提出了一种参与度评估方法，该方法利用来自视频、文本和日志这三种模态的数据。此方法实现了参与度指标的提取、异步数据的融合、使用深度学习模型评估参与度水平以及使用梯度幅值映射来进一步区分参与度水平之间的细微差异。在后续的实证研究中，我们探索了几种流行的深度卷积神经网络模型在该方法中的适用性，并使用统计方法验证了参与度量化结果的可靠性。分析结果表明，该结合多模态异步数据融合和深度学习模型来评估参与度的框架既有效又实用。
