Student’s Academic Performance Prediction Using Factor Analysis Based Neural Network

Shamsuddeen Suleiman; Ahmad Lawal; Umar Usman; Shehu Usman Gulumbe; Aminu Bui Muhammad

doi:doi:10.11648/j.ijdsa.20190504.12

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Student’s Academic Performance Prediction Using Factor Analysis Based Neural Network

Shamsuddeen Suleiman, Ahmad Lawal, Umar Usman, Shehu Usman Gulumbe, Aminu Bui Muhammad

Published in International Journal of Data Science and Analysis (Volume 5, Issue 4)

Received: 6 July 2019 Accepted: 26 July 2019 Published: 26 August 2019

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Abstract

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.

Published in	International Journal of Data Science and Analysis (Volume 5, Issue 4)
DOI	10.11648/j.ijdsa.20190504.12
Page(s)	61-66
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

Neural Network, Hybrid, Factor Analysis, Prediction, Learning Algorithms

References

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[3]	Johnson R. A. and Wichern D. W., (1998), Applied Multivariate Statistical Analysis. Fifth Edition. Prentice-Hall, Inc, Upple Saddle River.
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[5]	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.
[6]	Hajek, Milan (2005). Neural networks, University of KwaZulu-Natal.
[7]	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.
[8]	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.
[9]	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.
[10]	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.
[11]	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.
[12]	Reid, J. (1984). Perceptual Learning Style Preference Questionnaire. Retrieved October 28, 2010 from http://lookingahead.heinle.com/filing/l-styles.htm.
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[16]	Mackay, D. J. C., (1992) “Bayesian interpolation”, Neural Computation, Vol. 4, No. 3, pp 415-447.

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APA Style

Shamsuddeen Suleiman, Ahmad Lawal, Umar Usman, Shehu Usman Gulumbe, Aminu Bui Muhammad. (2019). Student’s Academic Performance Prediction Using Factor Analysis Based Neural Network. International Journal of Data Science and Analysis, 5(4), 61-66. https://doi.org/10.11648/j.ijdsa.20190504.12

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ACS Style

Shamsuddeen Suleiman; Ahmad Lawal; Umar Usman; Shehu Usman Gulumbe; Aminu Bui Muhammad. Student’s Academic Performance Prediction Using Factor Analysis Based Neural Network. Int. J. Data Sci. Anal. 2019, 5(4), 61-66. doi: 10.11648/j.ijdsa.20190504.12

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AMA Style

Shamsuddeen Suleiman, Ahmad Lawal, Umar Usman, Shehu Usman Gulumbe, Aminu Bui Muhammad. Student’s Academic Performance Prediction Using Factor Analysis Based Neural Network. Int J Data Sci Anal. 2019;5(4):61-66. doi: 10.11648/j.ijdsa.20190504.12

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@article{10.11648/j.ijdsa.20190504.12,
  author = {Shamsuddeen Suleiman and Ahmad Lawal and Umar Usman and Shehu Usman Gulumbe and Aminu Bui Muhammad},
  title = {Student’s Academic Performance Prediction Using Factor Analysis Based Neural Network},
  journal = {International Journal of Data Science and Analysis},
  volume = {5},
  number = {4},
  pages = {61-66},
  doi = {10.11648/j.ijdsa.20190504.12},
  url = {https://doi.org/10.11648/j.ijdsa.20190504.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20190504.12},
  abstract = {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.},
 year = {2019}
}

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TY  - JOUR
T1  - Student’s Academic Performance Prediction Using Factor Analysis Based Neural Network
AU  - Shamsuddeen Suleiman
AU  - Ahmad Lawal
AU  - Umar Usman
AU  - Shehu Usman Gulumbe
AU  - Aminu Bui Muhammad
Y1  - 2019/08/26
PY  - 2019
N1  - https://doi.org/10.11648/j.ijdsa.20190504.12
DO  - 10.11648/j.ijdsa.20190504.12
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 61
EP  - 66
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20190504.12
AB  - 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.
VL  - 5
IS  - 4
ER  -

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Author Information

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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APA Style

Shamsuddeen Suleiman, Ahmad Lawal, Umar Usman, Shehu Usman Gulumbe, Aminu Bui Muhammad. (2019). Student’s Academic Performance Prediction Using Factor Analysis Based Neural Network. International Journal of Data Science and Analysis, 5(4), 61-66. https://doi.org/10.11648/j.ijdsa.20190504.12

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ACS Style

Shamsuddeen Suleiman; Ahmad Lawal; Umar Usman; Shehu Usman Gulumbe; Aminu Bui Muhammad. Student’s Academic Performance Prediction Using Factor Analysis Based Neural Network. Int. J. Data Sci. Anal. 2019, 5(4), 61-66. doi: 10.11648/j.ijdsa.20190504.12

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AMA Style

Shamsuddeen Suleiman, Ahmad Lawal, Umar Usman, Shehu Usman Gulumbe, Aminu Bui Muhammad. Student’s Academic Performance Prediction Using Factor Analysis Based Neural Network. Int J Data Sci Anal. 2019;5(4):61-66. doi: 10.11648/j.ijdsa.20190504.12

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@article{10.11648/j.ijdsa.20190504.12,
  author = {Shamsuddeen Suleiman and Ahmad Lawal and Umar Usman and Shehu Usman Gulumbe and Aminu Bui Muhammad},
  title = {Student’s Academic Performance Prediction Using Factor Analysis Based Neural Network},
  journal = {International Journal of Data Science and Analysis},
  volume = {5},
  number = {4},
  pages = {61-66},
  doi = {10.11648/j.ijdsa.20190504.12},
  url = {https://doi.org/10.11648/j.ijdsa.20190504.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20190504.12},
  abstract = {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.},
 year = {2019}
}

Copy | Download

TY  - JOUR
T1  - Student’s Academic Performance Prediction Using Factor Analysis Based Neural Network
AU  - Shamsuddeen Suleiman
AU  - Ahmad Lawal
AU  - Umar Usman
AU  - Shehu Usman Gulumbe
AU  - Aminu Bui Muhammad
Y1  - 2019/08/26
PY  - 2019
N1  - https://doi.org/10.11648/j.ijdsa.20190504.12
DO  - 10.11648/j.ijdsa.20190504.12
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 61
EP  - 66
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20190504.12
AB  - 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.
VL  - 5
IS  - 4
ER  -

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