EMT: Ensemble meta-based tree model for predicting student performance in academics

Abstract

Data Mining is a field in which hidden information is extracted from a large database by using some algorithms implementation. These algorithms are further divided into some categories like classification, clustering, association rule mining etc according to information we want to extract. Data mining is a field which is widely spread over different areas like telecommunication, marketing, operation, hospitals, hotel industry, education etc. Predicting the academic's performance and progress of the students has revealed the attention of the young researchers. To facilitate the task of building an academic prediction model, historical student academic dataset is used. In this paper, the contributions are exhibited in two different folds. In the first fold, the main aim is to build the prediction model by different families of the Machine Learning Techniques on the selected dataset for consideration. In the second fold, implementations of different ensemble meta-based model are presented by combining with different classification algorithms of Machine Learning Techniques. Different ensemble meta-based model taken into consideration for implementation are Bagging, AdaBoostM1, RandomSubSpace. The implementation results demonstrate that the ensemble meta-based technique (AdaBoostM1) gained a superior accuracy performance with MultilayerPerceptron Machine Learning technique reaching up to 80.33%.