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


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.


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ISRP Style

Saeed Ayat, Zabihollah Ahmad Pour, Comparison Between Artificial Neural Network Learning Algorithms for Prediction of Student Average Considering Effective Factors in Learning and Educational Progress, Journal of Mathematics and Computer Science, 8 (2014), no. 3, 215 - 225

AMA Style

Ayat Saeed, Pour Zabihollah Ahmad, Comparison Between Artificial Neural Network Learning Algorithms for Prediction of Student Average Considering Effective Factors in Learning and Educational Progress. J Math Comput SCI-JM. (2014); 8(3):215 - 225

Chicago/Turabian Style

Ayat, Saeed, Pour, Zabihollah Ahmad. "Comparison Between Artificial Neural Network Learning Algorithms for Prediction of Student Average Considering Effective Factors in Learning and Educational Progress." Journal of Mathematics and Computer Science, 8, no. 3 (2014): 215 - 225


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