A survey on automatic engagement recognition methods: online and traditional classroom

Student engagement in a learning environment is directly related to students’ perception and involvement of the educational activities class, along with their physical mental health. This paper presents an extensive survey various automatic detection approaches algorithms based on computer vision, physiological neurological signals analysis-based methods. The vision-based techniques depend traits captured by image sensors such as facial expressions, gesture posture analysis, gaze direction. signal approach depends sensor data, like heart rate (HR), electroencephalogram (EEG), blood pressure (BP), galvanic skin response (GSR). A brief analysis available datasets for Engagement Recognition its features are also summarized. study highlights few commercially wearables which provides that helps student’s attentivity recognition. Our reveal accuracy recognition system will increase if we number modalities used. In this survey, intend support upcoming researchers well tutors smart education set up providing overview existing or proposed different scenarios.