Student’s Academic Performance Prediction Using Factor Analysis Based Neural Network
International Journal of Data Science and Analysis
Volume 5, Issue 4, August 2019, Pages: 61-66
Received: Jul. 6, 2019; Accepted: Jul. 26, 2019; Published: Aug. 26, 2019
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Shamsuddeen Suleiman, Department of Mathematics, Statistics Unit, Usmanu Danfodiyo University, Sokoto, Nigeria
Ahmad Lawal, Department of Mathematics, Statistics Unit, Usmanu Danfodiyo University, Sokoto, Nigeria
Umar Usman, Department of Mathematics, Statistics Unit, Usmanu Danfodiyo University, Sokoto, Nigeria
Shehu Usman Gulumbe, Department of Mathematics, Statistics Unit, Usmanu Danfodiyo University, Sokoto, Nigeria
Aminu Bui Muhammad, Department of Mathematics, Computer Science Unit, Usmanu Danfodiyo University, Sokoto, Nigeria
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This study focused on the statistical technique using the neural network, hybrid models and factor analysis on constructing the new factors affecting students learning styles of the survey done among university students in predicting academic performance. The data were collected using survey questionnaires and students’ academic records. The methodologies used were descriptive statistics, factor analysis, neural network and hybrid models technique using the following Learning algorithms; Levenberg-Marquardt (LM), Bayesian Regularization (BR), BFGS Quasi-Newton (BFG), Scaled Conjugate Gradient (SCG), Gradient Descent (GD) in artificial neural network model while for the second Hybrid model only the best two algorithms where use; Levenberg-Marquardt (LM), Bayesian Regularization (BR). The results showed ten new factors were successfully constructed using factor analysis and the proposed hybrid models show that though it took longer time and number of epochs to train the hybrid models by Bayesian Regularization Algorithms, and it gives more accurate predictions than both the Levenberg-Marquadrt, Scaled Conjugate Gradient, Gradient Descent and BFGS Quasi-Newton (BFG) Algorithms. In a nutshell, the finding indicates that Bayesian Regularization is the best learning algorithms in both Neural Network and Hybrid models for predicting students’ academic performance.
Neural Network, Hybrid, Factor Analysis, Prediction, Learning Algorithms
To cite this article
Shamsuddeen Suleiman, Ahmad Lawal, Umar Usman, Shehu Usman Gulumbe, Aminu Bui Muhammad, Student’s Academic Performance Prediction Using Factor Analysis Based Neural Network, International Journal of Data Science and Analysis. Vol. 5, No. 4, 2019, pp. 61-66. doi: 10.11648/j.ijdsa.20190504.12
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Bendangnuksung and Prabu P (2018) Students Performance Prediction using Deep Neural Net International Journal of Applied Engineering Research (13) 2 pp. 1171-1176.
Bresfelean, N. and Ghisoiu, N. (2005) Determining Students’ Academic Failure Profile Founded on Data Mining Methods,” Proceedings of the ITI 2005 30th Int. Conf. on Information Interfaces, 2005, Cavtat, Croatia, June 23rd - 26th, 2008, pp. 317-322.
Johnson R. A. and Wichern D. W., (1998), Applied Multivariate Statistical Analysis. Fifth Edition. Prentice-Hall, Inc, Upple Saddle River.
Zou, J., Han, Y., & So, S. S. (2009). Overview of artificial neural networks. In Artificial Neural Networks (pp. 14-22). Humana Press.
Shebany, M. et al. (2014). Artificial neural network: a brief overview. In International Journal of Engineering Research and Applications, Volume 4 (Issue 2), Version 1, pp. 07-12.
Hajek, Milan (2005). Neural networks, University of KwaZulu-Natal.
MarijanaZekić-Sušac, NatašaŠarlija and Sanja Pfeifer (2013) Combining PCA Analysis And Artificial Neural Networks in Modelling Entrepreneurial Intentions of Students Croatian Operational Research Review (CRORR), Vol. 4, pp. 306-317.
Hu S, Yan G. and Jiang H (2015) Study of Classification Model for College Students’ M-Learning Strategies Based on PCA-LVQ Neural Network 8th International Conference on BioMedical Engineering and Informatics (BMEI 2015) pp. 742-746.
Ahamed A. T. M. S,, Tanzeem N. M. and Rahman R. M (2017), An intelligent system to predict academic performance based on different factors during adolescence, Journal of Information and Telecommunication, 1: 2, 155-175.
Asogwa O. C. and Oladugba A. V., (2015) “Of Students Academic Performance Rates Using Artificial Neural Networks (ANNs).” American Journal of Applied Mathematics and Statistics, 3 (4), 151-155. doi: 10.12691/ajams-3-4-3.
Zacharis N. Z. (2016), predicting student academic performance in blended learning using artificial neural networks, International Journal of Artificial Intelligence and Application (IJAIA), Vol. 7, No. 5, September 2016 pp 17-29.
Reid, J. (1984). Perceptual Learning Style Preference Questionnaire. Retrieved October 28, 2010 from
Anders, U. (1996) Model selection in neural networks, ZEW Discussion Papers 96-21. Retrieved from
Hagan, M. T., & Menhaj, M., (1994) “Training feed-forward networks with the Marquardt algorithm”, IEEE Trans. Neural Networks, Vol. 5, No. 6, pp 989-993.
Foresee, F. D. & Hagan, M. T., (1997) “Gauss-Newton approximation to Bayesian regularization”, International Joint Conference on Neural Networks.
Mackay, D. J. C., (1992) “Bayesian interpolation”, Neural Computation, Vol. 4, No. 3, pp 415-447.
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