Personalized Learning Pathways Using Machine Learning in Educational Portals

Traditional e-learning platforms are often one-size-fits-all and do not cater to individualized learning needs. Personalized learning pathways developed through Machine Learning (ML) can provide real-time adaptive learning experiences by evaluating student performance and finding areas of weakness to modify the content. In this work, we present an intelligent educational framework with ML algorithms that allows student performance tracking and AI-enabled assessment generation and adaptive content recommendations. The system utilizes real-time data analytics to categorize each student based on performance, and therefore adjusts learning materials as learning progress dynamically changes. Additionally, Natural Language Processing (NLP) is used for personalized quiz questions that offer an enhanced experience for engagement and retention. Overall, the intelligent learning framework allows for improved student learning outomes through an interactive, data-intensive, personalized environment for learning. Experimental evaluations demonstrate the viability of the system for student engagement and optimizing students' learning pathways. Continued development includes utilizing predictive analytics with deep learning to increase effectiveness in personalization and engagement for students.