Potential Using Artificial Neural of Network to Predict Student Success/ Failure for Preliminary Study | ||
| Menoufia Journal of Electronic Engineering Research | ||
| Article 6, Volume 19, Issue 1, January 2009, Pages 93-104 | ||
| Document Type: Original Article | ||
| DOI: 10.21608/mjeer.2009.65245 | ||
| Author | ||
| Maged M. M. Fahmy | ||
| Computer Department, College of Applied Studies, King Faisal University Saudi Arabia, Postal Code 31952, P.O.B. 40287, Al Khobar, KSA | ||
| Abstract | ||
| Academic failure among first-year university students has long raised a large number of arguing. Many educational psychologists and researchers have tried to understand and then explain it. The target of this research is to adapt a Neural Network Approach that can be used to predict student’s success-failure risk. Multilayer feed forward back error propagation artificial neural network model has been adapted and trained with available data driven from existing academic acceptance system results. A performance analysis is done to analyze the effectiveness of such model for this particular problem. The result of this study shows that the neural network model can predict students' performance, if the current and future samples have similar characteristics. | ||
| References | ||
|
[1] J. J. Droesbeke,, I. Hecquet, and C. Wattelar “La population étudiante.” Paris: Ellipses, 2001.
[2] B. Naik and S. Ragothaman, “Using neural network to predict MBA student success”, College Student Journal,38(1):143-149, 2004.
[3] A. K. Aggarwal, S. D. Travers, and C. E. H. Scott-Conner “Selection of surgical residents: A neural network approach” Cybernetics and Systems 31:417-430, 2000.
[4] N. Entwistle, “Motivational factors in students' approaches to learning”, In R.R. Schmeck (Ed.), Learning strategies and learning styles. 21-51. New York: Plenum, 1988.
[5] A. Ardila,‘‘Predictors of university academic performance in Colombia’’, International Journal of Educational Research, vol. 35, pp. 411-417, 2001.
[6] V.V. Busato, F.J. Prins, J.J. Elshout, and C. Hamaker, ‘‘The relation between learning styles, the Big Five personality traits and achievement motivation in higher education’’, Personality and Individual Differences, vol. 26, 1999.
[7] V.V. Busato, F.J. Prins, J.J Elshout and C. Hamaker, “Intellectual ability, learning style, personality, achievement motivation and academic success of psychology students in higher education”, Personality and Individual Differences, vol. 29, pp. 1057-1068, 2000.
[8] A. Furnham, C.J. Jackson, and T. Miller, ‘‘Personality, learning style and work performance" Personality and Individual Differences, vol.27, pp.1113-1122, 1999.
[9] B.L. Garton, J.E Dyer,.and B.O.King, "Factors associated with the academic performance and retention of college agriculture students" NACTA Journal, 2002.
[10] A.R. King, "Relationships between CATI personality disorder variables and measures of academic performance’’, Personality and Individual Differences, vol. 29, pp. 177-190, 2000.
[11] A. Minnaert, and P.J. Janssen, “The additive effect of regulatory activities on top of intelligence in relation to academic performance in higher education”, Learning and Instruction, vol. 9, pp. 77-91, 1999 .
[12] J.F. Superby, J.-P. Vandamme, and N. Meskens, “Determination of factors influencing the achievement of the first-year university students using data mining methods”, Education Economics, v15 n4, p405-419, Dec 2007. | ||
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