A DATA-DRIVEN ANALYSIS OF STUDENT ACADEMIC PERFORMANCE AND DROPOUT RISK: EVIDENCE-BASED INSIGHTS FOR STUDENT RETENTION IN HIGHER EDUCATION
Abstract
Dropout among students is a prevalent challenge in the area of higher education, affecting the institutional performance
and academic outcomes of the learners. This research seeks to explore a data analysis of academic success and probability
of dropout among students in order to come up with empirical findings about ways of improving student retention. The
study conducts an analysis of a secondary dataset consisting of 4424 observations on students using the quantitative
methods of analysis and predictive modeling in order to identify the key determinants of student attrition. The findings
reveal that academic success measured in terms of number of units passed and grade point averages during semesters is
highly significant in determining student dropout. Other financial aspects such as the payment status for tuition fees and
debt status also greatly affect the student retention rate. Ensemble models especially the predictive models demonstrate
very high accuracy in predicting at-risk students, hence the significance of adopting evidence-based interventions. The
findings also underscore the need to incorporate academic surveillance and financial aid systems in order to enhance
student performance. The results provide viable implications to higher education institutions that aim at adopting datadriven retention policies.
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