Considering learner engagement has a mutual benefit for both learners and instructors. Instructors can help learners increase their attention, involvement, motivation, and interest. On the other hand, instructors can improve their instructional performance by evaluating the cumulative results of all learners and upgrading their training programs. This paper proposes a general, lightweight model for selecting and processing features to detect learners' engagement levels while preserving the sequential temporal relationship over time. During training and testing, we analyzed the videos from the publicly available DAiSEE dataset to capture the dynamic essence of learner engagement. We have also proposed an adaptation policy to find new labels that utilize the affective states of this dataset related to education, thereby improving the models' judgment. The suggested model achieves an accuracy of 68.57\% in a specific implementation and outperforms the studied state-of-the-art models detecting learners' engagement levels.

翻译：考虑到学习者参与度对学习者和教学者均有裨益。教学者可以帮助学习者提升注意力、参与度、动力和兴趣。另一方面，教学者可以通过评估所有学习者的累积结果并优化培训方案来改进教学表现。本文提出了一种通用轻量级模型，用于选择和提取特征以检测学习者的参与度水平，同时保留随时间推移的序列时序关系。在训练和测试过程中，我们分析了公开可用的DAiSEE数据集中的视频，以捕捉学习者参与度的动态本质。我们还提出了一项适应策略，用于生成新标签，该策略利用该数据集中与教育相关的情感状态，从而改进模型的判断能力。所提出的模型在特定实现中达到了68.57%的准确率，并且优于研究中的最新模型对学习者参与度水平的检测效果。