Abstract
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.
Abstract (translated)
考虑到学习者的参与对于学习者和教师都有 mutual利益。教师可以帮助学习者增加他们的注意力和参与度，提高他们的动机和兴趣。另一方面，教师可以通过评估所有学习者的累积结果来提高他们的培训计划。本文提出了一种通用的轻量级模型，用于选择和处理特征来检测学习者的参与度，同时保留有时间顺序关系。在训练和测试期间，我们分析了公开可用DAiSEE数据集中的视频，以捕捉学习者参与度的动态本质。我们还提出了一个适应策略，以查找与该数据集相关的情感状态，从而提高模型的判断。所提出的模型在特定实现中的准确率为68.57\%，并优于已研究的最先进的模型，这些模型用于检测学习者的参与度。
URL
https://arxiv.org/abs/2405.04251
