Development of a model for estimating and classifying the educational performance of undergraduate students using a combination of multilayer neural networks (Case study: Qom University)



Students’ growth and development have always been considered important by the education system as they are the future assets of their country. Although many students can flourish their talents and creativity, we are faced with a large number of students each year whose talents are squandered leading them to a position far from success. Today, with the more students entering in different academic levels and the variety of study disciplines, the necessity to properly guide students is felt more than ever. For this purpose, educational data mining has received special attention from the educational system’s officials in recent years. So far, various classification methods and techniques in data mining and machine learning have been used to predict student performance. However, these individual classifiers have limitations such as complexity and instability for predicting performance in the education process. To tackle this problem, ensemble classification has been proposed as a new and efficient method. Ensemble classification systems combine the results of several individual classifiers to provide a model with better performance. In this paper, a new Ensemble classification system is presented using multilayer neural networks and SOM clustering in order to estimate and classify the grade point average of undergraduate students. In addition, we used averaging and majority voting as combination methods for aggregating the results of individual classifiers. Evaluation results on real university data show that our proposed ensemble system provides better accuracy and performance compared to prevalent individual classification methods. Also, the proposed ensemble system obviously outperforms other popular ensemble methods in classifying students’ GPA.


Agarwal, S., G. N. Pandey, and M. D. Tiwari. 2012. Data mining in education: data classification and decision tree approach. International Journal of e-Education, e-Business, e-Management and e-Learning 2 (2): 140.
Aluko, R. O., O. A. Adenuga, P. O. Kukoyi, A. A. Soyingbe, and J. O. Oyedeji. 2016. Predicting the academic success of architecture students by pre-enrolment requirement: using machine-learning techniques. Construction Economics and Building 16 (4): 86-98.
Amini, M., & J. Rezaeenour. 2014. Early Fraud Detection for Online Auctions Using A Multiple-phased Modeling Method with a Neural Networks Ensemble Classifier. Applied mathematics in Engineering, Management and Technology?: 560-567.‏
Amini, M., J. Rezaeenour, & E. Hadavandi. 2016. A neural network ensemble classifier for effective intrusion detection using fuzzy clustering and radial basis function networks. International Journal on Artificial Intelligence Tools 25 (02): 1550033.
_____. 2014. Effective intrusion detection with a neural network ensemble using fuzzy clustering and stacking combination method. Journal of Computing and Security, 1 (4): 293-305.
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_____. 2015. A cluster-based data balancing ensemble classifier for response modeling in Bank Direct Marketing. International Journal of Computational Intelligence and Applications 14 (04): 1550022.‏
Amrieh, E. A., T. Hamtini, & I. Aljarah. 2016. Mining Educational Data to Predict Student’s academic Performance using Ensemble Methods. International Journal of Database Theory and Application 9 (8): 119-136.
Araque, F., C. Roldn, and A. Salguero. 2009. Factors inuencing university dropout rates. Computers & Education 53 (3):563-574.
Asif, R., A. Merceron, S. A. Ali, & N. G. Haider. 2017. Analyzing undergraduate students' performance using educational data mining. Computers & Education 113: 177-194.
Bydžovská, H. 2016. Course Enrollment Recommender System. In 9th International Conference on Educational Data Mining (EDM), Raleigh, USA, Jun 29-Jul 2, 2016.,
Chau, V. T. N., & N. H. Phung. 2013, November. Imbalanced educational data classification: An effective approach with resampling and random forest. In The 2013 RIVF International Conference on Computing & Communication Technologies-Research, Innovation, and Vision for Future (RIVF) (pp. 135-140). IEEE. Hanoi, Vietnam
Cortez, P., & A. M. G. Silva. 2008. Using data mining to predict secondary school student performance. In Proceedings of 5th Annual Future Business Technology Conference, Porto, 2008, p. 5-12.
Dzeroski, S., & B. Zenko. 2002. Is combining classifiers better than selecting the best one? In International Conference on Machine Learning (ICML) (Vol. 2002, p. 123e30).
ElDen, A. S., M. A. Moustafa, H. M. Harb, & A. H. Emara. 2013. AdaBoost ensemble with simple genetic algorithm for student prediction model. International Journal of Computer Science & Information Technology 5 (2): 73.
Gray, G., C. McGuinness, and P. Owende. 2014. An application of classi_cation models to predict learner progression in tertiary education. In Advance Computing Conference (IACC), 2014 IEEE International, pages.  In 2014 IEEE International Advance Computing Conference (IACC) urgaon, India, (pp. 549-554).
Guruler, H., A. Istanbullu, and M. Karahasan. 2010. A new student performance analyzing system using knowledge discovery in higher educational databases. Computers & Education 55 (1): 247-254.
Hansen, L. K., & P. Salamon. 1990. Neural network ensembles. IEEE Transactions on Pattern Analysis & Machine Intelligence 10: 993-1001.
Hara, K., & M. Okada. 2005. Ensemble learning of linear perceptrons: on-line learning theory. Journal of the Physical Society of Japan 74 (11): 2966-2972.
Ho, T. K., J. J. Hull, & S. N. Srihari. 1994. Decision combination in multiple classifier systems. IEEE Transactions on Pattern Analysis & Machine Intelligence 1: 66-75.
Hoffait, A.-S., and M. Schyns. 2017. Early detection of university students with potential diculties. Decision Support Systems 101: 1-11.
Huang, C.T., W. T. Lin, S. T. Wang, and W. S. Wang. 2009. Planning of educational training courses by data mining: Using China Motor Corporation as an example. Expert Systems with Applications 36 (3): 7199-7209.
Iam-On, N., & T. Boongoen. 2017. Improved student dropout prediction in Thai University using ensemble of mixed-type data clusterings. International Journal of Machine Learning and Cybernetics 8 (2): 497-510.
Ji, C., & S. Ma. 1997. Combinations of weak classifiers. In Advances in Neural Information Processing Systems 9: 494-500.
Kittler, J., M. Hatef, R. P. W. Duin, & J. Matas. 1998. On combining classifiers, IEEE transactions on pattern analysis and machine intelligence 20 (3): 226-239.
Kotsiantis, S., K. Patriarcheas, & M.enos. 2010. A combinational incremental ensemble of classifiers as a technique for predicting students’ performance in distance education. Knowledge-Based Systems 23 (6): 529-535.
Krogh, A., & J. Vedelsby. 1995. Neural network ensembles, cross validation, and active learning. In Advances in neural information processing systems 7: 231-238.‏
Lam, L., & S. Y. Suen. 1997. Application of majority voting to pattern recognition: an analysis of its behavior and performance. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans 27 (5): 553-568.
Laugerman, M., D. Rover, M. Shelley, and S. Mickelson. 2015. Determining Graduation Rates in Engineering for Community College Transfer Students Using Data Mining. International Journal of Engineering Education 31 (6): 1448-1457.
Lykourentzou, I., I. Giannoukos, G. Mpardis, V. Nikolopoulos, & V. Loumos. 2009. Early and dynamic student achievement prediction in e-learning courses using neural networks. Journal of the American Society for Information Science and Technology 60 (2): 372–380.
M_arquez-Vera, C., A. Cano, C. Romero, A. Y. M. Noaman, H. Mousa Fardoun, and S. Ventura. 2016. Early dropout prediction using data mining: a case study with high school students. Expert Systems 33 (1): 107-124.
Miguéis, V. L., A. Freitas, P. J. Garcia, & A. Silva. 2018. Early segmentation of students according to their academic performance: A predictive modelling approach. Decision Support Systems 115: 36-51.
Moschopoulos, C. N., P. Tsiatsis, G. N.  Beligiannis, D. Fotakis, & S. D. Likothanassis. 2009. Dealing with large datasets using an artificial intelligence clustering tool. In Tools and Applications with Artificial Intelligence (pp. 105-120). Berlin, Heidelberg: Springer.
Mueen, A., B. Zafar, and U. Manzoor. 2016. Modeling and Predicting Students' Academic Performance Using Data Mining Techniques. International Journal of Modern Education and Computer Science, 8 (11): 36-55.
Naftaly, U., N. Intrator, & D. Horn. 1997. Optimal ensemble averaging of neural networks. Network: Computation in Neural Systems 8 (3): 283-296.
Natek, S. and M. Zwilling. 2014. Student data mining solution–knowledge management system related to higher education institutions. Expert systems with applications 41 (14): 6400-6407.
Oza, N. C., & K. Tumer. 2001. Input decimation ensembles: Decorrelation through dimensionality reduction. In International Workshop on Multiple Classifier Systems (pp. 238-247). Berlin, Heidelberg: Springer.
Pandey, M., & S. Taruna. 2014. A comparative study of ensemble methods for students' performance modeling. International Journal of Computer Applications 103 (8): 0975 – 8887.
Pandey, M., & S. Taruna. 2018. An Ensemble-Based Decision Support System for the Students’ Academic Performance Prediction. In ICT Based Innovations (pp. 163-169). Singapore: Springer.
Paris, I. H. M., L. S. Affendey, & N. Mustapha. 2010. Improving academic performance prediction using voting technique in data mining. World Academy of Science, Engineering and Technology 62: 820-823.
Polat, K., & S. Güneş. 2009. A novel hybrid intelligent method based on C4. 5 decision tree classifier and one-against-all approach for multi-class classification problems. Expert Systems with Applications 36 (2): 1587-1592.
Punlumjeak, W., S. Rugtanom, S. Jantarat, & N. Rachburee. 2018. Improving Classification of Imbalanced Student Dataset Using Ensemble Method of Voting, Bagging, and Adaboost with Under-Sampling Technique. In IT Convergence and Security 2017 (pp. 27-34). Singapore: Springer.
Qiang, F., H. Shang-Xu, & Z. Sheng-Ying. 2005. Clustering-based selective neural network ensemble. Journal of Zhejiang University-Science A 6 (5): 387-392.
Ramaswami, M., & R. Bhaskaran. 2010. A CHAID based performance prediction model in educational data mining. arXiv preprint arXiv:1002.1144.
Richardson, M., C. Abraham, and R. Bond. 2012. Psychological correlates of university students' academic performance: a systematic review and meta-analysis. Psychological Bulletin 138 (2): 353-387.
Romero, C., M-L. Lpez, J.-M. Luna, and S. Ventura. 2013b. Predicting students' final performance from participation in on-line discussion forums. Computers & Education 68: 458-472.
Sanzana, M. B., S. S. Garrido, & C. M. Poblete. 2015. Profiles of Chilean students according to academic performance in mathematics: An exploratory study using classification trees and random forests. Studies in Educational Evaluation 44: 50-59.
Satyanarayana, A., & M. Nuckowski. 2016. Data mining using ensemble classifiers for improved prediction of student academic performance.  N-913, Dept. of Computer Systems Technology, NewYork City College of Technology (CUNY), 300 Jay St, Brooklyn NY – 11201.
Şen, B., E. Uçar, and D. Delen. 2012. Predicting and analyzing secondary education placement-test scores: A data mining approach. Expert Systems with Applications 39 (10): 9468-9476.
Superby, J. F., J. P. Vandamme, & N. Meskens. 2006. Determination of factors influencing the achievement of the first-year university students using data mining methods. In Workshop on educational data mining (Vol. 32, p. 234).
Tinto, V. 1982. Limits of Theory and Practice in Student Attrition. The Journal of HigherEducation 53 (6): 687-700.
Trivedi, S., Z. A. Pardos, & N. T. Heffernan. 2011, June. Clustering students to generate an ensemble to improve standard test score predictions. In International Conference on Artificial Intelligence in Education (pp. 377-384). Berlin, Heidelberg: Springer.
Twala, B. 2010. Multiple classifier application to credit risk assessment. Expert Systems with Applications 37 (4): 3326-3336.
Vesanto, J., & E. Alhoniemi. 2000. Clustering of the self-organizing map. IEEE Transactions on Neural Networks 11 (3): 586-600.