An Efficient Data Analysis Model Integrating Blended Learning and Learner Engagement in Higher Education Institutions
Keywords:
Data analysis, Learning analytics, Learners engagement, blended learning.Abstract
As the digital age propels forward, the engagement of learners within these hybrid settings becomes increasingly critical to their academic success and overall retention rates. The effective integration of Educational Data Mining (EDM) and Learning Analytics (LA) emerges as a paramount strategy in understanding and optimizing learner engagement and academic progression. This study presents a pioneering model that integrates blended learning and learner engagement through the application of Educational Data Mining (EDM) and Learning Analytics (LA) in higher education institutions. By employing advanced data analytics techniques, including a hybrid boosting classifier, the research identifies critical factors influencing student academic progression and retention rates. The analysis of an extensive dataset covering various aspects of learner engagement—such as technology usage, instructor interaction, and feedback quality—reveals significant insights. These insights enable the prediction of student outcomes, offering a novel approach to enhance educational delivery and support mechanisms. The findings highlight the potential of machine learning models in transforming educational strategies and fostering a deeper understanding of student engagement within blended learning environments.
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