Non-intrusive Identification of Student Attentiveness and Finding Their Correlation with Detectable Facial Emotions

ABSTRACTTeachers use observational cues in the classroom to identify attentiveness of students and guide the pace of their lecture. However, effectiveness of this technique decreases with increasing class size. This paper presented an approach for automating these observational cues from the students' facial expressions and identifying their attentiveness via a neural network machine learning model. Results of the deep learning Convolutional Neural Network model were then compared with the range of confidence values obtained from a cloud-based emotion recognition service to identify the correlations with human observer. Real time videos of students were collected during classes and sample dataset were created for attentive and inattentive students in order to train the machine learning model. This system can be highly useful for inexperienced teachers for early identification of inattentive students in classroom and thereby taking necessary actions to enhance student learning.