Towards Smarter E-Learning: Real-Time Analytics and Machine Learning for Personalized Education

Abstract

E-Learning platforms change fast, and real-time behavioural analytics with machine learning provides the most powerful means to enhance learner outcomes. The datasets undergo preprocessing techniques like Z-score outlier detection, Min-Max scaling for feature normalization, and Ridge-RFE (Ridge regression and Recursive Feature Elimination) for feature selection in order to improve the accuracy and reliability of the predictions. Applying the Gradient Boosting Machine, classification accuracy up to a 94% level with respect to the model about predictions on learner outcomes was achievable. Thus, applying this, feedback systems may offer timely recommendations or directions in class that propel students toward better understanding on how to raise participation and success percentages. However, this approach has some potential benefits but there are still various challenges such as managing the data imbalance for models that generalize in a dynamic environment. Though hybrid methods mitigate this problem, real-time data pipelines with behaviour analytics incorporation call for significant computer-intensive resources and infrastructure. This integration has very high paybacks. It makes possible more responsive E-Learning platforms with individual needs almost met in real-time manners, thus giving instantaneous feedback, content suggestions, and timely interventions. Finally, convergence of real-time analytics with ML models culminates in adaptive learning environments which improve student engagement, retention, and quality of academic results.