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@article{191447,
        author = {G. Yuvasri and V. Mageswari},
        title = {AI-ENHANCED E-LEARNING ANALYTICS & PREDICTION USING MACHINE LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {6988-6991},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=191447},
        abstract = {Abstract the rapid expansion of digital learning has created an ever-growing demand for intelligent learning support systems that can comprehend student behaviours and predict academic outcomes this ai-enhanced e-learning analytics system proposes the employment of machine learning algorithms on student interaction data such as attendance quiz performance assignment submissions study hours and participation to classify learners into different performance categories combining data analytics dashboards and personalized learning recommendations the system facilitates teachers and administrators
In early identification of at-risk students and improvement of overall learning outcomes the entire platform would be developed on a secure multi-user framework with facilities for admin teacher and student roles hence ensuring role-based access to analytics and insights the model will make use of visualizations and pdf reporting in order to facilitate decision-making and academic planning the research addresses the increased demand for predictive learning systems and aims at contributing to a reduction in dropout risks while enhancing learning quality in e-learning environments},
        keywords = {Learning Analytics, Student Performance Prediction, E-Learning Analytics System, Machine Learning in Education, Academic Performance Monitoring, Educational Technology},
        month = {January},
        }