A data-driven model for student retention in a Philippine higher education institution

Abstract

The impact of higher education inevitably enhances economic, social, and human capital development. In the Philippines, many students require assistance to complete their college degrees, resulting in high dropout rates. The study aimed to determine the attributes that contributed to students' degree completion, predict them using the Decision Tree (DT) algorithm, and assist university policymakers with information to help them create effective early intervention plans for at-risk students. The Knowledge Discovery in Databases (KDD) process extracts accurate information from 3,417 student records, which consist of academic and socio-economic attributes, after undergoing selection, preprocessing, transformation, data mining, and interpretation/evaluation. MS Excel and RapidMiner were used to explore data, build predictive models, and generate insights. The results show that the DT model achieves an accuracy of 82.50%. To finish a degree, students must have a grade point average (GPA) of 2.55 or lower and be no older than 19. Parents' educational background and living outside the city also affect the decision outcomes. Living outside the city and parents' academic backgrounds also influence GPA outcomes. The researchers recommend reviewing, analyzing, and, as necessary, revising previous policies to enhance university programs that address this long-standing issue.