Modeling the Impact of Engagement Parameters on Student Performance in Blended Learning by using Machine Learning

Abstract The expeditious growth of blended learning has created a demand for analytical methods that can quantify how digital engagement affects educational outcomes across various academic programs. This study will reveal a cross-course evaluation that combine supervised machine learning, engagement Modeling and comparative performance analysis to better understand the pupil’s behaviour within blended and traditional instructional conditions. By using the dataset partitions representing four academic programs- B.Tech 2nd year, B.Tech 3rd year, B.Sc 3rd year and BCA 3rd year-the study examines the relationship between structured engagement in blended learned subjects and resulting academic performance. The analysis comprise performance averages, engagement-to-performance correlations, and model-driven predictions created through ML algorithms such as Linear Regression, Decision Trees, Random Forests, and Support Vector Regression. Findings show variations in blended learning effectiveness across these courses, with engagement demonstrating powerful predictive value in computer engineering programs and modest or inconsistent alliance in other disciplines. The recent work emphasizing learner analytics and AI-supported evaluation setup in higher education. The study highlights how engagement parameters and algorithmic modeling can support an extensive understanding of blended learning dynamics, offering evidence-based understanding for instructional design, policy decisions, and future adaptive learning research. Keywords Machine Learning, Model Prediction, Blended Learning, Parameter Influence, Performance Analysis.