Design of an Improved Method for Personalized Learning Using Deep Q-Learning and CNN-Based Engagement Recognition

Adoption of personalized learning systems is on the rise. Traditional approaches to education, in most cases, cannot fulfill the diversified requirements of different learners and target various levels of engagement. Current methods are mainly focused on defined learning paths or limited information regarding levels of engagement, hence failing the adaptability test, which prevents real-time understanding and leveraging of data in such cases. This paper fills in the identified gaps by proposing a holistic, multi-faceted learning system and incorporating three advanced machine learning techniques: Deep Q-Learning for personalized content recommendations, Random Forest Classifier for predictive academic analytics, and Convolutional Neural Networks (CNNs) for real-time engagement tracking. Deep Q-Learning is used for adaptive providing learning resources by using the performance metrics of students in quizzes and the timestamp spent on tasks to optimize learning paths based on real-time feedback. Random Forest Classifiers will be used as they are robust for predicting academic success by using historical academic data as well as the engagement metrics gathered from smart classrooms. This includes the application of CNNs in visual engagement recognition, such as detecting a child's attention levels based on facial expressions and body language. Real-time engagement data is fed back into the learning system to further refine the contents recommended and academic predictions made. A dynamic and highly personal environment in the learning process is created through these practices by virtue of the continuous optimization of content and predictions based on individual student interactions. Preliminary results so far indicate a likely 20-30% improvement in learning outcomes, with around 85-90% accuracy in academic predictions, and a 15-20% increase in classroom engagement. This is highly sophisticated intelligent learning and offers best tools for faculty to improve performance and engagement.