Socio- Technical System for Effective Classroom Learning

Abstract

Analyzing attention enables educators to assess student engagement and enhance learning experiences. It provides valuable insights for optimizing teaching and managing classroom behavior. Several techniques have been proposed to analyze attention and provide feedback to the instructor for effective learning. These include intrusive and non-intrusive techniques which utilize EEG headsets, eye trackers, Kinect sensors, cameras, non-verbal cues etc. Intrusive techniques provide accurate results only for controlled environments prioritizing precise measurements. Moreover, they cause discomfort to the subjects involved. Whereas non-intrusive techniques using non-verbal features do not cause any discomfort to the user and can be used in any environment. However, none of the studies so far have addressed all non-verbal features simultaneously. This paper presents a multimodal architecture which integrates all non-verbal features including headpose orientation, body posture estimation, emotion detection and Eye Aspect Ratio (EAR) calculation to analyze attention. A deep learning model has been trained on the Facial Expression Recognition Plus (FERPlus) dataset with 94% accuracy. We used Euler angles to determine the head pose which includes up, down, left, right and forward directions. Further EAR is calculated for both eyes using eye key points and Euclidean distance which shows the opening and closing state of the eyes. Finally estimated the body pose of the student by training an SVM model & body key points which include shoulders, elbows, and wrists. The combined result of all these features is displayed in the form of a graph which reflects the level of attentiveness of the students to the teacher in real-time. This system can assist the teacher in addressing concerns such as poor academic performance, disengagement from studies, and high dropout rates among student