Journal Article

Real-Time Attention Monitoring System for Classroom: A Deep Learning Approach for Student’s Behavior Recognition

Parambil, Medha Mohan Ambali, Gochoo, Munkhjargal, Ali, Luqman, Trabelsi, Zouheir, Alnajjar, Fady

2023

Overview

Effective classroom instruction requires monitoring student participation and interaction during class, identifying cues to simulate their attention. The ability of teachers to analyze and evaluate students’ classroom behavior is becoming a crucial criterion for quality teaching. Artificial intelligence (AI)-based behavior recognition techniques can help evaluate students’ attention and engagement during classroom sessions. With rapid digitalization, the global education system is adapting and exploring emerging technological innovations, such as AI, the Internet of Things, and big data analytics, to improve education systems. In educational institutions, modern classroom systems are supplemented with the latest technologies to make them more interactive, student centered, and customized. However, it is difficult for instructors to assess students’ interest and attention levels even with these technologies. This study harnesses modern technology to introduce an intelligent real-time vision-based classroom to monitor students’ emotions, attendance, and attention levels even when they have face masks on. We used a machine learning approach to train students’ behavior recognition models, including identifying facial expressions, to identify students’ attention/non-attention in a classroom. The attention/no-attention dataset is collected based on nine categories. The dataset is given the YOLOv5 pre-trained weights for training. For validation, the performance of various versions of the YOLOv5 model (v5m, v5n, v5l, v5s, and v5x) are compared based on different evaluation measures (precision, recall, mAP, and F1 score). Our results show that all models show promising performance with 76% average accuracy. Applying the developed model can enable instructors to visualize students’ behavior and emotional states at different levels, allowing them to appropriately manage teaching sessions by considering student-centered learning scenarios. Overall, the proposed model will enhance instructors’ performance and students at an academic level.

• Publisher: MDPI AG
• Subject: Age groups / Artificial intelligence / attention assessment / Cameras / Classrooms / Datasets / Deep learning / Digitization / Education / Electroencephalography / emotion recognition / Emotional factors / Emotions / Harnesses / Internet of Things / Machine learning / Monitoring / Monitoring systems / Neural networks / object detection / Public speaking / Real time / Recognition / Schools / Sensors / Student behavior / student behavior dataset / Student participation / Students / Teachers / Teaching