Classification models for higher learning scholarship award decisions - Wirawati Dewi Ahmad,; Azuraliza Abu Bakar, (Penerbit Universiti Kebangsaan Malaysia, 2018)
Scholarship is a financial facility given to eligible students to extend Higher Education. Limited funding sources with the growing number of applicants force the Government to find solutions to help speed up and facilitate the selection of eligible students and then adopt a systematic approach for this purpose. In this study, a data mining approach was used to propose a classification model of scholarship award result determination. A dataset of successful and unsuccessful applicants was taken and processed as training data and testing data used in the modelling process. Five algorithms were employed to develop a classification model in determining the award of the scholarship, namely J48, SVM, NB, ANN and RT algorithms. Each model was evaluated using technical evaluation metric, such contingency table metrics, and accuracy, precision, and recall measures. As a result, the best models were classified into two different categories: The best model classified for ‘Eligible’ status, and the best model classified for ‘Not Eligible’ status. The knowledge obtained from the rules-based model was evaluated through knowledge analysis conducted by technical and domain experts. This study found that the classification model from SVM algorithm provided the best result with 86.45% accuracy to correctly classify ‘Eligible’ status of candidates, while RT was the weakest model with the lowest accuracy rate of for this purpose, with only 82.9% accuracy. The model that had the highest accuracy rate for ‘Not Eligible’ status of scholarship offered was NB model, whereas SVM model was the weakest model to classify ‘Not Eligible’ status. In addition, the knowledge analysis of the decision tree model was also made and found that some new information derived from the acquisition of this research information may help the stakeholders in making new policies and scholarship programs in the future.