Predicting Students’ Mobility using Different Statistical Tools : Basis for Students’ Success

Abstract

This paper investigates students’ success at Pangasinan State University by identifying patterns and models that might be used to correctly classify and predict if a student will transfer or finish their studies. In this study, three categorical variables or attributes and one continuous variable were considered independent variables due to the availability of the data. The results from the binary logistic regression model with the high school general average and course as independent variables (Model 3), and the decision tree model with transition gain as a splitting criterion were fitted to the dataset to generate a model that possibly best describes the students’ mobility in Pangasinan State University Urdaneta City Campus. The decision tree model is better than the binary logistic regression model based on accuracy, AUC, and sensitivity values. This implies that the decision tree model is better at correctly classifying observations as "transferred" than Model 3. Thus, it was concluded that the decision tree model with information gain as the splitting criterion best describes the mobility of PSU students. The results of this paper can be used for school administration involving students’ mobility/success, particularly in classifying whether a student will transfer based on other.