Recently, online learning has been gradually accepted and approbated by the public. In this context, an effective prediction of students’ engagement can help teachers obtain timely feedback and make adaptive adjustments to meet learners’ needs. In this paper, we present a novel model called the Deep Facial Spatiotemporal Network (DFSTN) for engagement prediction. The model contains two modules: the pretrained SE-ResNet-50 (SENet), which is used for extracting facial spatial features, and the Long Short Term Memory (LSTM) Network with Global Attention (GALN), which is employed to generate an attentional hidden state. The training strategy of the model is different with changes of the performance metric. The DFSTN can capture facial spatial and temporal information, which is helpful for sensing the fine-grained engaged state and improving the engagement prediction performance. We evaluate the methods on the Dataset for Affective States in E-Environments (DAiSEE) and obtain an accuracy of 58.84% in four-class classification and a Mean Square Error (MSE) of 0.0422. The results show that our method outperforms many existing works in engagement prediction on DAiSEE. Additionally, the robustness of our method is also exhibited by experiments on the EmotiW-EP dataset.

最近，在线学习已逐渐被公众接受和认可。在这种情况下，对学生参与度的有效预测可以帮助教师及时获得反馈，并进行适应性调整以满足学习者的需求。在本文中，我们提出了一种用于参与度预测的新型模型，称为“深面部时空网络”（DFSTN）。该模型包含两个模块：用于提取面部空间特征的预训练SE-ResNet-50（SENet），以及用于产生注意力的具有全球注意力的长期短期记忆（LSTM）网络（GALN）。隐藏状态。该模型的训练策略随性能指标的变化而不同。DFSTN可以捕获面部时空信息，这有助于感知细粒度的参与状态并提高参与预测性能。我们评估了电子环境中情感状态数据集（DAiSEE）上的方法，在四类分类中的准确性为58.84％，均方误差（MSE）为0.0422。结果表明，在DAiSEE的参与度预测中，我们的方法优于许多现有工作。此外，EmotiW-EP数据集上的实验还显示了我们方法的鲁棒性。