Internet of Behaviors-Based Focus State Classification for Enhancing Educational Engagement

The Internet of Behaviors (IoB) provides powerful tools for collecting, analyzing, and interpreting data on human actions and decision-making patterns. However, its application in education remains limited, despite the critical importance of being able to monitor and assess student focus, engagement, and participation in real time to improve learning outcomes and provide timely interventions. This study proposes an IoB-based system that employs deep learning and computer vision to assess and enhance student engagement in real-time. The system's architecture integrates two YOLOv8-based models: a multiclassifier for recognizing six classroom behaviors, and a binary classifier for distinguishing between focused and unfocused states. Anonymized identifiers are used to ensure a consistent and ethical tracking of individual behavior over time. These classifications trigger adaptive interventions designed to maintain or restore students' attention. Given the lack of standard benchmarks for assessing focus in classroom settings, especially under realistic conditions that include ambient noise and peer influence, a dedicated dataset was collected and used to train and evaluate the proposed system's performance in a university classroom context. The experimental results showed that the multi-class model achieved 97% accuracy, while the binary classification model reached 99% accuracy, both outperforming three state-of-the-art baseline methods. In a real-world classroom deployment, 75% of the students who received interventions subsequently re-engaged, underscoring the potential of the framework to improve learning outcomes through timely data-driven behavioral feedback.