Binary Logistic Predictive Model in Determining Students’ Intention to Take Higher Education

Students’ choice to take higher education is defined by the interaction among behavior and cognition, personal factors, and environmental factors. Using data mining technique and binary regression analysis, this study aims to uncover factors that significantly predict the likelihood for a student to take higher education. Analysis revealed that (i) students whose parents are educated with high income are 1.77 times more likely to pursue higher education than not; and (ii) Older Female Students are less likely to pursue higher education. The model explains that parents should encouraged their young children to embrace education to its highest level by capacitating themselves economically to provide their basic educational and financial needs. Lastly, the study concludes that the pursuit for higher education among young students can be defined primarily by their parents’ educational and financial capability.

Keywords –Higher Education, Access to Education, Finance, Model, Binary Logistic Regression Analysis