We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 November 2024
Reseach Article

Modeling Information Technology Competency using Neural Networks

by Fadzilah Siraj, Hashim Asman, Md. Rajib Hasan
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 58 - Number 11
Year of Publication: 2012
Authors: Fadzilah Siraj, Hashim Asman, Md. Rajib Hasan
10.5120/9323-3623

Fadzilah Siraj, Hashim Asman, Md. Rajib Hasan . Modeling Information Technology Competency using Neural Networks. International Journal of Computer Applications. 58, 11 ( November 2012), 1-6. DOI=10.5120/9323-3623

@article{ 10.5120/9323-3623,
author = { Fadzilah Siraj, Hashim Asman, Md. Rajib Hasan },
title = { Modeling Information Technology Competency using Neural Networks },
journal = { International Journal of Computer Applications },
issue_date = { November 2012 },
volume = { 58 },
number = { 11 },
month = { November },
year = { 2012 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume58/number11/9323-3623/ },
doi = { 10.5120/9323-3623 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:02:09.372090+05:30
%A Fadzilah Siraj
%A Hashim Asman
%A Md. Rajib Hasan
%T Modeling Information Technology Competency using Neural Networks
%J International Journal of Computer Applications
%@ 0975-8887
%V 58
%N 11
%P 1-6
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Neural Network (NN) is one of the most important branches of AI that has been applied to an increasing number of real-world problems of considerable complexity from the financial markets to real estate, medicine and education. The most commonly used is multilayer perceptron with back propagation that is capable of representing non-linear functional mapping between inputs and outputs. In this paper, such a net is used to forecast the information technology competency among teacher trainees in the teaching institutes. The system has been developed as a web-based self-assessment information system that can be used to obtain a model for predicting the information technology competency. The system functions as an instrument that generates questionnaires as well as performing rubric assessment online. The data will be entered online using the web as a medium. This data will be fed into the NNsimulator to obtain a suitable model. Once the model is obtained, it will then be used to predict the information technology competency among the teacher trainees. The data was collected from various teachers' training institutions in Malaysia. The findings indicate that the most suitable forecasting model comprises of eleven input nodes, five hidden nodes and one output node. The performance of the selected model obtained with an accuracy of 99. 77%. Hence the results show that the developed system can be used as a tool to assist decision-making in education.

References
  1. Ministry of Education, "BahagianPendidikan Guru," 2001.
  2. UNESCO, "Competency Standards Modules," 2008. [Online]. Available: Web:http://www. unesdoc. unesco. org/imases/0015/001562/156207e. pdf. [Accessed: 05-Oct-2012].
  3. F. Siraj, E. A. O. A. Omer, and M. R. Hasan, Data Mining and Neural Networks?: The Impact of Data Representation. Croatia: Intech, 2012.
  4. F. Siraj, N. Abubakar, and M. R. Hasan, "Classification of Capital Expenditures and revenue expenditures: an Analysis of Correlation and Neural Network," in 2nd International Conference on computing and Informatics, 2009.
  5. F. Siraj and M. A. Abdoulha, "Uncovering hidden information within university's student enrollment data using data mining. ," in 3rd Asia International Conference on Modelling and Simulation, 2009, pp. 413–418.
  6. F. Siraj, N. Yusoff, and L. . Kee, "Emotion classification using neural network," in Proceedings of International Conference on Computing and Informatics,, 2006.
  7. F. Siraj and W. R. S. Osman, "Improving generalization of neural networks using MLP discriminant based on multiple classifiers failures," Proceedings of 2nd International Conference on Computational Intelligence, Modelling and Simulation, pp. 27–32, 2010.
  8. M. Y. ShahrulAzmi, F. Siraj, S. Yaacob, M. . P. Paulraj, and A. Nazri, "Improved Malay vowel feature extraction method based on first and second formants," 2nd International Conference on Computational Intelligence, Modelling and Simulation, pp. 339–344, 2010.
  9. K. Li, "Application of various modeling techniques to analyze a housing condition survey dataset," in IEEE International Conference on Systems, Man and Cybernetics, 2004, pp. 409–414.
  10. S. Naik, B. , &Ragothaman, "Using neural network to predict MBA student success," College Student Journal, vol. 38, no. 1, pp. 1–4, 2004.
  11. E. N. Ogor, "Student academic performance monitoring and evaluation using data mining techniques," in Electronics, Robotics and Automotive Mechanics Conference, 2007, pp. 354–359.
  12. O. E. Oladokun, V. O. , Adebanjo, A. T. , & Charles-Owaba, "Predicting students' academic performance using artificial neural network: a case study of an engineering course," The Pacific Journal of Science and Technology, vol. 6, no. 1, pp. 72–79, 2008.
  13. A. American Library Association, "Presidential Committee on Information Literacy," 1989.
  14. R. D. Westfall, "Evaluation and Assimilation Skills as Key Knowledge aspects of Information Technology Literacy," 1997. [Online]. Available: http://www. cyberg8t. com/westfalr/it_litrc. htm. [Accessed: 06-Sep-2012].
  15. J. Young, "Learning to Learn: Assessing Information Technology Literacy," 1997.
  16. F. Siraj and M. A. Abdoulha, "Uncovering Hidden Information Within University's Student Enrollment Data Using Data Mining," MASAUM Journal of Computing, vol. 1, no. 2, pp. 337–342, 2009.
  17. F. Siraj, N. Nordin, and N. Yusoff, "Quality Function Deployment Analysis Based on Neural Network and Statistical Results. International Journal of Simulation Systems, Science & Technology," Special Issue on: Advances in Mechatronics and AI Model, vol. 9, no. 2, pp. 73–81, 2008.
  18. F. Siraj, N. A. Bakar, and A. Abolgasim, "Classification of Capital Expenditures and Revenue Expenditures: An Analysis of Correlation and Neural Networks," Proceedings of the 2nd International Conference on Computing and Informatic, 2009.
  19. F. Siraj, N. Mustafa, M. F. Haris, S. R. M. Yusof, M. A. Salahuddin, and M. R. Hasan, "Pre-selection of Recruitment Candidates Using Case Based Reasoning," 2011 Third International Conference on Computational Intelligence, Modelling& Simulation, pp. 84–90, Sep. 2011.
  20. F. Siraj, M. H. Yusoff, M. F. Haris, M. A. Salahuddin, S. R. M. Yusof, and M. R. Hasan, "i-SME: Loan Decision Support System Using Neuro-CBR Approach," 2011 Third International Conference on Computational Intelligence, Modelling& Simulation, pp. 91–96, Sep. 2011.
  21. P. & M. A. Linnakyla, "Profiling students on the quality of school life by neural networks," the journal Social Indicators Research, pp. 25–32, 1998.
  22. D. Katz, I. R. , Williamson, D. M. , Nadelman, H. L, Kirsch, I. , Almond, R. G. , Copper, P. L. , Redman, M. L, & Zapata, "Assesing Information and communications Technology Literacy for Higherr Education," in International Association for Educational Assessment (IAEA), 2004, pp. 13–18.
  23. J. O'Connor, L. G. , Radcliff, C. J. , &Fedeon, "Assesing Information and communications Technology Literacy for Higherr Education," in Tenth National Conference of Association of College and Research Libraries, 2011, pp. 163–173.
  24. D. Descy, D. & Johnson, "Microsoft Rubrics," 1998. [Online]. Available: http://www. ga. k12. pa. us/curtech/stucours/offrubr. htm. [Accessed: 04-Apr-2010].
Index Terms

Computer Science
Information Sciences

Keywords

Modeling Information Technology Competency Neural Network Prediction