Recognition of student engagement in classroom from affective states

Student engagement is positively related to comprehension in teaching-learning process. Student engagement is widely studied in online learning environments, whereas this research focuses on student engagement recognition in classroom environments using visual cues. To incorporate learning-centered affective states, we curated a dataset with six learning-centered affective states from four public datasets. A graph convolution network (GCN)-based deep learning model with attention was designed and implemented to extract more contributing features from input video for student engagement recognition. The proposed architecture was evaluated on curated as well as four public datasets. An ablation study was conducted on a curated dataset, the best performing model with minority oversampling and focal cross-entropy loss achieved 65.35% accuracy. We also estimated the student engagement in authentic classroom data, and it showed a positive correlation between students' engagement levels and post-lesson test scores with a Pearson's coefficient value of 0.64. The proposed method outperformed the existing state-of-the-art methods on two of the public datasets with accuracy scores of 99.20% and 56.17%, and it achieved accuracy scores of 64.92% and 56.17% on other two public datasets which are better than many baseline results on them.