Monitoring and analysing student attentiveness in traditional classrooms through computational intelligence

Abstract

Abstract In traditional face-to-face mode of education, keeping an eye on students’ concentration is still quite difficult, especially in large or packed lecture halls/classrooms where teachers are unable to consistently monitor minor behavioural indications of disengagement. In this paper, we propose a framework—Student Attentive Analysis using Computational Intelligence ( SAA-CI ). SAA-CI is a real-time, privacy-conscious framework that uses non-intrusive still-image sequences to evaluate students’ attentiveness using computer vision and deep learning. SAA-CI takes periodic snapshots and uses OpenCV and Dlib to extract important behavioural indications such as gaze direction, head attitude, eyelid movement, yawning, and facial expressions, in contrast to current systems that rely on continuous video or invasive sensors. These traits are recorded as temporal vectors and classified using a Long Short-Term Memory (LSTM) network that has been improved with an attention mechanism. Through an instructor dashboard and adaptive intervention engine, SAA-CI provides actionable pedagogical insights and binary attentiveness forecasts (Attentive/Not Attentive). Scientific benchmarking experiments on the DAiSEE dataset and a special SAA-CI -Local classroom dataset show that SAA-CI performs better than current baseline techniques, with 92.6% accuracy, a 9.4% gain due to the attention mechanism, and additional gains from data augmentation. The findings illustrate that SAA-CI maintains high dependability across a range of classroom behaviours, functions effectively in real time, and protects student privacy with optional anonymization and low data retention. By providing a scalable, morally sound, and pedagogically significant attentiveness monitoring solution for contemporary educational settings, SAA-CI improves intelligent classroom analytics.