Massive Open Online Course Outcomes with Hybrid DNN-CapsNet Model for Learning Behavior Analysis and Dropout Prediction

Massive open online courses have suddenly changed the way education is done by providing a panoply of learning opportunities. All this and more will accept the high dropout rate and a lack of personalization to slow down their effectiveness. This study investigates learning behavior patterns using big data and deep learning techniques to enhance MOOC outcomes. With several behavioral, demographic, and trace features in the dataset, this research examines a Stanford dataset from Harvard University and MIT. The hybrid DNN-CapsNet model is developed for feature extraction and classification. The model outperformed others, especially in engaging students and predicting dropout rates. Results show significant precision, recall, and F1-score improvements over prior models such as CNN and VSM. The findings point toward rigorous interventions and personalized support in online education. Recommendations include the use of adaptive systems providing personalized resources and feedback, which maximize student engagement and retention. This research contributes considerably toward understanding the complex dynamics of online learning and provides insights regarding effective strategies for educators and developers to optimize MOOCs. Including advanced learning analytics and machine learning approaches sets a tone for future research, bridging technology and pedagogy. This study aims to resolve many critical barriers by offering necessary recommendations toward redefining online learning with informed and data-driven practices which will help the learners in realizing their full potential.