Predicting College Students Optimal Course Load to Achieve Higher Academic Grades
This research addresses the gap in predictive models for improving students' grades by considering student demographics and course load. Using longitudinal data from a four-year university, the study explores the relationship between course load and academic performance. It considers two theories: one suggesting a negative impact of course load due to time constraints, and another suggesting a positive link due to increased commitment. Objectives include identifying demographic factors affecting course load, correlating course load with grades, and determining optimal course loads for different demographics. While acknowledging limitations, such as unaccounted demographics and external factors, the study's significance lies in its potential to enhance higher education success through proactive interventions using machine learning algorithms. By leveraging data mining techniques like educational data mining and deep learning, the research aims to contribute to improving learning and teaching environments and overall educational quality.