Classification Models for Higher Learning Scholarship Award Decisions

Wirawati Dewi Ahmad, Azuraliza Abu Bakar


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, 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 programmes in the future.


scholarship award; classification model; knowledge discovery

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Gandomi A, Haider M. Beyond the hype: Big data concepts, methods, and analytics. Int J Inf Manage [Internet]. Elsevier Ltd; 2015;35:137–44. Available from:

Koturwar P, Girase S and, Debajyoti M. A Survey of Classification Techniques in the Area of Big Data. Int J Adv Found Res Comput. 2014;1:1–7.

Jiawei Han MK. Data Mining Concept and Techniques.

Alhassan JK, Lawal SA. Using Data Mining Technique for Scholarship Disbursement. 2015;9:1511–4.

Azuraliza, Arshad A. Rough Set and Decision Tree Model for Determining Scholarship Award Qualification. 2013;12:65–70.

Raharja YP. Rancang Bangun Sistem Rekomendasi Beasiswa Menggunakan ALgoritma Klasifikasi C4.5 pada Universitas Dian Nuswantoro. Undinus [Internet]. 2014;1–4. Available from:

Tun KT, Aye AM. Selection of Appropriate Candidates for Scholarship Application Form using KNN Algorithm. Int J Sci Eng Technol Res. 2014;3:1019–26.

Kaiwen W. A Quantitative Analysis Method of Ideological and Political Instructor;s Work Based on Data Mining. 2018;

Daud A, Aljohani NR, Abbasi RA, Lytras MD, Abbas F, Alowibdi JS. Predicting Student Performance using Advanced Learning Analytics. Proc 26th Int Conf World Wide Web Companion - WWW ’17 Companion [Internet]. 2017;415–21. Available from:

Kaur R, Gangwar R. A Review on Naive Baye ’ s ( NB ) , J48 and K-Means Based Mining Algorithms for Medical Data Mining. 2017;

Wang G, Hao J, Ma J, Jiang H. A comparative assessment of ensemble learning for credit scoring. Expert Syst Appl [Internet]. Elsevier Ltd; 2011;38:223–30. Available from:

Ayub M, Karnalim O. Predicting outcomes in introductory programming using J48 classification. World Trans Eng Technol Educ. 2017;15:132–6.

Hamsagayathri P, Sampath P. Decision Tree Classifiers for Classification of Breast Cancer. Int J Curr Pharm Res [Internet]. 2017;9:31. Available from:

Goyal A, Thakur S, Chowdhury R. Using Ensemble Learning and Association Rules to Help Car Buyers Make Informed Choices. Proc Int Conf Big Data Adv Wirel Technol - BDAW ’16 [Internet]. 2016;1–5. Available from:

Statnikov A, Aliferis CF, Tsamardinos I, Hardin D, Levy S. A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis. Bioinformatics. 2005;21:631–43.

Rathore SS, Gupta A. A comparative study of feature-ranking and feature-subset selection techniques for improved fault prediction. Proc 7th India Softw Eng Conf - ISEC ’14 [Internet]. 2014. p. 1–10. Available from:

Afram A, Janabi-Sharifi F, Fung AS, Raahemifar K. Artificial neural network (ANN) based model predictive control (MPC) and optimization of HVAC systems: A state of the art review and case study of a residential HVAC system. Energy Build [Internet]. Elsevier B.V.; 2017;141:96–113. Available from:

Thomas RW, Vidal JM. Toward detecting accidents with already available passive traffic information. 2017 IEEE 7th Annu Comput Commun Work Conf CCWC 2017. 2017;1–4.

Roy S, Garg A. Analyzing performance of students by using data mining techniques a literature survey. 2017 4th IEEE Uttar Pradesh Sect Int Conf Electr Comput Electron [Internet]. 2017;130–3. Available from:

Chaurasia V, Pal S. A Novel Approach for Breast Cancer Detection using Data Mining Techniques. Int J Innov Res Comput Commun Eng. 2014;2:2456–65.

Veerasamy R, Rajak H, Jain A, Sivadasan S, Varghese CP, Agrawal RK. Validation of QSAR Models - Strategies and Importance. Int J Drug Des Disocovery. 2011;2:511–9.


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