Survival Analysis Approach for Early Prediction of Student Dropout Using Enrollment Student Data and Ensemble Models

Abstract

The Universal Access to Quality Tertiary Education Act is a law in the Philippines that provides college students with free tuition and other fees in Philippine state universities and local universities. People's tax is used to finance this law and the government should ensure that student retention or persistence is attained throughout the duration of their stay. To effectively decrease student dropout, it is necessary to understand which students are at risk of dropping out. In addition, this study proposed a model that detects and predicts student success in tertiary education through the right selection of the suitable program utilizing the enrollment data that may have a significant on the study outcome of the students. This study experimented single classifier and added ensemble approach classifiers to propose a predictive model to detect early dropout of first-year college students. The study utilized tree algorithms and then applied the ensemble algorithm to identify student attributes that distinguish potential dropouts from college. The result reveals a very interesting prediction that if their average grade is less than 85, there is a high tendency of dropping any program they enrolled in. Evaluation results in the final stage of the model construction process reveal that applying bagging ensemble into j-48 tree attained the highest accuracy as matched with other tree algorithms, however, forest tree algorithm achieved the highest value in terms of dropout precision and graduated recall. The result also shows that applying ensemble approaches have a marginal increase in classification performance.