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Agenda Business Journal

Volume no. 11 | 2018/11
Issue no. 1


Title
DROP OUT RISK ANALYSIS USING PREDICTIVE ANALYTICS: BASIS FOR IMPROVING STUDENT RETENTION OF HEIS IN BATANGAS PROVINCE
Author
Dr. Niña B. Macarandang, Ms. Maryrose Elizabeth I. Magdalena, Mrs. Leove G. Mutiangpili, Mrs. Minerva I. Magdalena and Mrs. Gorgonia L. Famadico
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Abstract
Filipino parents value education as one of the most important legacies they can impart to their children. They believe that having a good education opens opportunities that would ensure a good future and eventually lift them out of poverty. Thus, they are willing to make enormous sacrifices to send their children to school. However, with a poor family’s severely limited resources, education tends to be less prioritized over more basic needs such as food and shelter. Hence, the chances of the family to move out of poverty are unlikely. It is therefore, important that the poor be given equitable access to education. Preventing school dropouts and promoting successful school completion is a national concern that poses a significant challenge for schools and educational communities working with youth at risk for school failure. This research analyzed why students drop out of school and what can be done about it. The purpose of this research was to identify the factors affecting student drop-­outs and determined why learners stayed in the university. This study also focused on the use of predictive analytics to improve student retention of selected HEIs in Batangas Province. The research was carried out during the 2017-­2018 academic year. A total of 270 college students from six (6) private and public HEIs in Batangas Province participated in this research. The results of this study correlated with existing literature that there are many factors that lead to student dropouts like loss of financial aid, the rising cost of college tuition, the feeling of boredom, working to help the family, lack of parental support, and academic unpreparedness. The researchers also found out that personal-­related and program-related predictors have no significant effect to student retention. The results of the study served as basis for a proposed dropout prevention plan that will help students who are at risk of dropping out.
Keywords
Drop out risk, predictive analytics, student retention, Batangas Province
References
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