Prediction of students' success employing data mining algorithms

Most higher education institutions now priorities student achievement as a key strategic goal. Academic institutions are focusing more on keeping students enrolled in their classes without sacrificing educational level in response to budget constraints and rising operational costs. The use of machine learning to forecast students' success using academic and behavioural data has been the subject of numerous initiatives and studies. We applied the popular machine learning algorithms in order to predict student achievement including Random Forest, Support Vector Machine, Logistic Regression etc. The employed Support Vector Machine has achieved to provide the best accuracy (93.8%) and sensitivity (98.75 %) scores. Additionaly, the implementation of stack-based ensemble model is very effective in the predicition of student success. Therefore, a stacked-based ensemble learning model is cerated in order to compare the efficiency of it in the prediction of students' succes with the other machine learning algorithms. At the end, the stacked-based ensemble model has provided the best accuracy score (93.9%) in the prediction of students' success.