Given that approximately half of science, technology, engineering, and mathematics (STEM) undergraduate students in U.S. colleges and universities leave by the end of the first year [15], it is crucial to improve the quality of classroom environments. This study focuses on monitoring students' emotions in the classroom as an indicator of their engagement and proposes an approach to address this issue. The impact of different facial parts on the performance of an emotional recognition model is evaluated through experimentation. To test the proposed model under partial occlusion, an artificially occluded dataset is introduced. The novelty of this work lies in the proposal of an occlusion-aware architecture for facial action units (AUs) extraction, which employs attention mechanism and adaptive feature learning. The AUs can be used later to classify facial expressions in classroom settings. This research paper's findings provide valuable insights into handling occlusion in analyzing facial images for emotional engagement analysis. The proposed experiments demonstrate the significance of considering occlusion and enhancing the reliability of facial analysis models in classroom environments. These findings can also be extended to other settings where occlusions are prevalent.

翻译：鉴于美国高校中约一半的STEM（科学、技术、工程与数学）专业本科生在一年级结束时辍学[15]，改善课堂环境质量至关重要。本研究聚焦于通过监测课堂中学生情绪作为参与度的指标，并提出相应解决方案。通过实验评估不同面部区域对情绪识别模型性能的影响。为测试模型在部分遮挡条件下的表现，引入人工遮挡数据集。本研究创新点在于提出一种基于注意力机制和自适应特征学习的遮挡感知面部动作单元（Action Units, AUs）提取架构，该架构可后续用于课堂场景中的面部表情分类。研究结果为处理遮挡条件下面部图像的情感参与度分析提供了重要启示。所提实验证实了考虑遮挡因素的重要性，并提升了课堂环境中面部分析模型的可靠性。这些发现同样可推广至其他存在普遍遮挡的应用场景。