Predicting Time to Graduation of Open University Students: An Educational Data Mining Study

Abstract

The world’s move to a global economy has an impact on the high rate of student academic failure. Higher education, as the affected party, is considered crucial in reducing student academic failure. This study aims to construct a prediction (predictive model) that can forecast students’ time to graduation in developing countries such as Indonesia, as well as the essential factors (attributes) that can explain it. This research used a data mining method. The data set used in this study is from an Indonesian university and contains demographic and academic records of 132,734 students. Demographic data (age, gender, marital status, employment, region, and minimum wage) and academic (i.e., grade point average (GPA)) were utilized as predictors of students’ time to graduation. The findings of this study show that (1) the prediction model using the random forest and neural networks algorithms has the highest classification accuracy (CA), and area under the curve (AUC) value in predicting students’ time to graduation (CA: 76% and AUC: 79%) compared to other models such as logistic regression, Naïve Bayes, and k-nearest neighbor; and (2) the most critical variable in predicting students’ time to graduation along with six other important variables is the student’s GPA.

Keywords

• educational data mining
• machine learning
• open university
• time to graduation