In recent years, rising interest in online education, including MOOCs and SPOCs, has brought forth challenges like student engagement and performance prediction. This review covers cutting-edge research on using machine and deep learning to predict online learners’ outcomes, categorizing features, strategies, and evaluation metrics while addressing challenges and limitations.

• Learner Performance Prediction
• Machine Learning in Education
• Online Education

Massive online courses (MOOCs) like Coursera, edX, and Udemy are transforming education globally. They’re crucial for populous countries like China and India, fostering a knowledge-based economy. However, MOOCs’ large class sizes can lead to high dropout rates. To address this, we propose a cloud-based personalized learning platform powered by an efficient facial analytics algorithm. This platform monitors learners’ real-time responses to resources like TED Talks, YouTube, or MERLOT on mobile devices, identifying challenging topics and allowing interactive quizzes. Data on learning progress is securely uploaded to a password-protected cloud platform, ensuring compatibility across various mobile devices. This innovation holds promise for the future of open learning and education.

• Cloud-Based Learning Platform
• Online Education
• Personalized Learning