Enhancing Academic Success Prediction: An Ensemble Approach

Abstract

The process of extracting meaning and knowledge from large volumes of data is termed as Data mining. It also refers to the way of inferring information from databases, which can be used in diverse fields including educational domain. Educational data mining plays a key role in finding ways to discover knowledge from data in the education sector. Educational Data Mining has evolved as a significant element in prediction of students’ academic performance. The most important goal of the paper is to analyze and evaluate the engineering students’ performance by applying stacking, an ensemble method in orange tool. Ensemble Stacking combines several base classifier models in order to create one optimal prediction model. Engineering students’ dataset was used to build predictive model using traditional classifiers SVM, Logistic Regression, Naïve Bayes and then stacking technique was implemented. The results showed that the proposed stacking technique obtains a high performance, which has a superior result compared to the other base classifiers techniques. Therefore, conclusion could be reached that the stacking performance is better than that of different algorithms.