Enhancing MOOCs through Real-time Learner Engagement and Emotion Detection Using Computer Vision and Machine Learning

• Main Authors: Mrayhi, Salwa, Khribi, Mohamed Koutheair, Jemni, Mohamed
• Format: Conference Proceeding
• Language: English
• Subjects: Affective computing; CNN; Computer aided instruction; Computer vision; Education; Electronic learning; Engagement detector; Inclusivity; Machine learning; Machine learning algorithms; MOOC; student-centered approach
• ISSN: 2161-377X
• DOI: 10.1109/ICALT61570.2024.00007

In the dynamically evolving of Massive Open Online Courses (MOOCs), the imperative for real-time and the efficient evaluation of learner engagement has never been more pronounced. Traditional methodologies, while providing foundational insights, often fall short in terms of objectivity and immediacy. This paper introduces an innovative system that uses advanced computer vision and machine learning algorithms to dynamically detect and analyze learner emotions and engagement levels during MOOC sessions. Additionally, this system facilitates the identification of areas for improvement and supports the design of personalized and engaging learning experiences, particularly for learners with disabilities. Our findings reveal that this system not only monitors the duration and intensity of learner engagement but also actively identifies moments of peak engagement and discerns learning patterns. This information enables the personalization of educational paths to suit individual learning styles, significantly enhancing engagement and overall MOOC effectiveness. Powered by affective computing, this technology seeks to make a difference in the field of education technology, transforming MOOCs into personalized learning environments that match the specific interests, goals and needs of each user.