Detecting Student Inattention Using Deep Learning and Behavioral Analysis

Abstract

Student inattention in classrooms negatively impacts learning outcomes and academic performance, posing a significant challenge for educators. Traditional methods of monitoring engagement rely on subjective teacher observations, which can be inconsistent, labor-intensive, and prone to bias. To address these limitations, this paper presents an AI-driven framework that uses deep learning and behavioral analysis to detect student inattention in real time. The proposed system integrates computer vision techniques including facial expression recognition, posture analysis, head pose estimation, and eye-gaze analysis, employing convolutional neural networks (CNNs) to extract spatial features and recurrent neural networks (RNNs) to model temporal patterns. The framework was evaluated using annotated classroom video data collected from real teaching sessions, capturing natural student behavior under typical classroom conditions. Experimental results demonstrate that the proposed approach achieves high accuracy in distinguishing attentive from inattentive states, outperforming traditional machine learning baselines while maintaining real-time performance. Beyond detection, the system provides actionable insights for educators by highlighting patterns of disengagement across time and students. By combining CNN-based spatial analysis with RNN-based temporal modeling, the framework offers an objective, scalable, and practical solution for monitoring classroom engagement, enabling timely interventions, personalized instruction, and improved learning outcomes.