Measuring Student Engagement through Behavioral and Emotional Features Using Deep-Learning Models

Students’ behavioral and emotional engagement in the classroom environment may reflect the students’ learning experience and subsequent educational outcomes. The existing research has overlooked the measurement of behavioral and emotional engagement in an offline classroom environment with more students, and it has not measured the student engagement level in an objective sense. This work aims to address the limitations of the existing research and presents an effective approach to measure students’ behavioral and emotional engagement and the student engagement level in an offline classroom environment during a lecture. More precisely, video data of 100 students during lectures in different offline classes were recorded and pre-processed to extract frames with individual students. For classification, convolutional-neural-network- and transfer-learning-based models including ResNet50, VGG16, and Inception V3 were trained, validated, and tested. First, behavioral engagement was computed using salient features, for which the self-trained CNN classifier outperformed with a 97%, 91%, and 83% training, validation, and testing accuracy, respectively. Subsequently, the emotional engagement of the behaviorally engaged students was computed, for which the ResNet50 model surpassed the others with a 95%, 90%, and 82% training, validation, and testing accuracy, respectively. Finally, a novel student engagement level metric is proposed that incorporates behavioral and emotional engagement. The proposed approach may provide support for improving students’ learning in an offline classroom environment and devising effective pedagogical policies.