Comparison Between Artificial Neural Network Learning Algorithms for Prediction of Student Average Considering Effective Factors in Learning and Educational Progress

Volume 8, Issue 3, pp 215 - 225 http://dx.doi.org/10.22436/jmcs.08.03.04

Download PDF

Download XML

• Export citations
  • CITATIONS & REFERENCES
  • EndNote (.ENW)
  • Mendeley, JabRef (.BIB)
  • Papers, Zotero, Reference Manager, RefWorks (.RIS)
  • ARTICLE CITATION
  • EndNote (.ENW)
  • Mendeley, JabRef (.BIB)
  • Papers, Zotero, Reference Manager, RefWorks (.RIS)
  • REFERENCES
  • EndNote (.ENW)
  • Mendeley, JabRef (.BIB)
  • Papers, Zotero, Reference Manager, RefWorks (.RIS)

• 3204 Downloads
• 4307 Views

Authors

Saeed Ayat - Department of Computer Engineering and Information Technology, Payame Noor University, Iran. Zabihollah Ahmad Pour - Department of Science, Islamic Azad University – Ayatollah Amoli Branch, Iran.

Abstract

In this project, by using different learning algorithms in the form of 37 input parameters of network for predicting average considering effective factors in learning and educational progress, the Perceptron artificial neural network have been studied. The requisite data have been obtained through handing out questionnaires between 400 students of Payame Noor University majoring in computer engineering, information technology and computer science. For recognizing the best learning algorithm, 13 common algorithms considering factors such as training time, the percentage of accountability, the index of efficiency ( the mean squared errors), and the number of epoch have been studied after error propagation. Finally the LM algorithm was recognized as the best learning algorithm for prediction of average.

Share and Cite

Keywords

• learning algorithm
• artificial neural network
• MLP
• prediction of average.

MSC

•  94A60
•  68T27

References

• [1] M. R. Mustafa, R. Rezaur, S. Saiedi, M. He, River Suspended Sediment Prediction Using Various Multilayer Perceptron Neural Network Training Algorithms_A case Study Malaysia, Water Resour manage, 26 (2012), 1879-1897.
  • View Article
  • Google Scholar


• [2] N. I. Daliakopoulos, P. Couliblay, H. K. Tsanis, Ground water level forcasting using artificial neural networks, J. Hydrol., 309 (2005), 229-240.
  • View Article
  • Google Scholar


• [3] M. T. Hagan, H. B. Demuth, M. H. Beal, Neutral Network Design, PWS, Beston (2005)
  • Google Scholar


• [4] B. D. Ripley, R. M. Reipley, Neural networks as statistical methods in survival analysis, Journal of statistics, (2006), 409-456.
  • Google Scholar


• [5] G. L. Dee, N. Bakhary, A. Abdul Rahman, B. Hisham Ahmad, A Comparison of Artificial Neural Network Learning Algorithms for Vibration- Based Damage Detection, Journal of Advanced Materials Research, 163-167 (2011), 2756-60.
  • View Article
  • Google Scholar


• [6] A. Fbrizzio, L. Edna, D. Christian, A. Gilberto, C. Antonio, V. Paschoarelli, Recursive diameter Prediction And Volume Calculation of Eucalyptus Trees Using Multilayer Perceptron Networks, Applied Soft Comuting, 12 (2012), 2030-2039.


• [7] H. Demuth, M. Beale, Neural Network Toolbox User's Guide , Copyright 1992-2002, By The Math Works. Inc., 4 (2000), 480
  • Google Scholar


• [8] K. Abhishek, M. P. Singh, S. Ghosh, A. Anand, Weather Forecasting Model Using Artificial Neural Network, Procedia Technology, 4 (2012), 311-318.
  • View Article
  • Google Scholar


• [9] A. Lakdashti, R. Yousedi, Kh. Khatiri , Effect of Simulated Education Software on Student Learining and Compare it With Traditional Method of Teaching, Journal of Information and communication technology in education, in Persian. , 3 (1390), 5-21


• [10] A. Akbari , Methods of Study, Faiz Kashani, in Persian. , Tehran (1382)


• [11] M. Parsa, Psychology of Learning, Fourth Edition, Payame Noor, in Persian. , Tehrn (1385)


• [12] S. Sattari, E. Jaafar Nezhad, Factors affecting the utilization of visual aids in the teaching - learning from the perspectives of teachers in Mazandaran, Journal of Information and communication technology in education, in Persian , 2 (1389), 6-20


• [13] E. Khalkhali, Z. Shakibaee, M. Andesh, Meta-analysis of the effects of ICT on professional development of teachers, Journal of Information and communication technology in education, in Persian. , 3 (1390), 165-183


• [14] M. Behrangi, Teaching models, Kamal Tarbiat, in Persian , Tehran (1387)


• [15] M. kantardzic, Data Mining: Concepts, Models, Methods and Algorithms, Second Edition, John Wiley (2011)
  • Google Scholar
  • MATH


• [16] M. Akbari, A. Ranaee, H. Namaghi, Assessment of desertification sensitivity parameters using artificial neural network , Journal of Soil and Water (Agricultural Sciences and Technology), in Persian. , 25 (1390), 398-410


• [17] T. Sabzevari, R. Mohammad Pour, M. E. Zemorrodian, Evaluation parameters of earth dam failures using neural networks, First National Congress on Civil Engineering, , 12-14 May, in Persian, Sharif University (2003)


• [18] M. B. Menhaj, Foundations of Neural Networks , Vol. 1, Amir Kabir University, in Persian. , Tehran (1387)
  • MathSciNet
  • Google Scholar


• [19] S. Shokr Beygi, A. Afkari Sayyah, H. Shayeghi Moghanloo, E. Shokouhian, Effects of temperature and Cushioned surface on the apple contusion volume Created by Tension and Predict it using artificial neural network, Journal of Food Science and Technology, in Persian., 31 (1390), 85-94


• [20] A. Ali Pour, S. Kalantarian, The relationship between handedness with academic achievement of middle school students, Journal of School Psychology, in Persian, 1 (1391), 7-26