Exploring Predictive Factors of Academic Probation Using Data Mining Approach

Abstract

The purpose of this study is to develop the early prediction model for academic probation to encourage retention of students at universities. For this study, various data from the administration system and learning environment of G University in South Korea were collected. We constructed the predictive model by applying logistic regression to col-lected data using new variables related to campus activities. To solve the class-imbalance problem, we applied data mining techniques. This study is significant in that the model is based on structured real data by using education data mining approach from the academic administrative system we can access. Predictive factors of academic probation were revealed and educational implications of developed predictive were discussed.