Learning Analytics in E-Learning: Predicting Student Success through Data
Keywords:
learning analytics, e-learning, predictive models, student success, education data, dashboards, ethicsAbstract
Learning analytics applies data-driven methods to optimize e-learning. This paper examines how analytics predicts student success through data. We review core concepts, data types, and analytics categories. We discuss predictive models and performance metrics used (e.g. accuracy, recall). Case studies include MOOCs (Coursera), university platforms (Open University), and Moodle. Visualization via dashboards and learning curves is shown. An experiment on dropout prediction is outlined. Ethical issues such as privacy, fairness, and transparency are considered. Finally, we identify key findings and future directions, including potential work in Libya.
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