Enhancing student engagement through webcam-based eye tracking and machine learning: A focus on English reading comprehension

This study presents an innovative approach to monitoring student attention in real-time using unmodified webcams and machine learning algorithms, with a specific focus on improving English reading comprehension. The proposed system captures eye movement patterns and facial features to detect attentiveness levels, providing immediate feedback to both students and instructors. Additionally, the research explores how font optimization based on individual attention data can enhance reading efficiency. The system was implemented in Shenzhen Polytechnic University's School of Foreign Languages and Business, with participants showing a 32.7% improvement in focus during English reading tasks and a 28.4% increase in reading efficiency after font adjustments. These findings highlight the potential of non-intrusive attention monitoring systems combined with personalized typography optimization to enhance language learning outcomes.