A FRAMEWORK FOR STATISTICAL ANALYSIS OF STUDENT ENGAGEMENT IN OBE BASED CLASSROOMS ENVIRONMENT USING YOLO MODEL

Abstract

The most important aspect of effective learning in an Outcome-Based Education (OBE) classroom environment is student involvement and attentiveness during the lecture. This study examines various student behaviors and evaluates attention of students during lectures using deep learning techniques, YOLO v8. The classroom representative visuals were perceived from online available dataset containing student’s images that has been recorded during the lecture. These photos are subjected to YOLO, which classifies different student activities into those that contribute positively or negatively to attention in class room. Positive markers of attention include actions like raising hands, concentrating on the front, reading, writing, and interacting with the teacher. On the other hand, distractions like eating, drinking, using a phone, or seeming drowsy have a detrimental impact. The results assist teachers to enhance their teaching methodologies and offer insights into patterns of classroom involvement.