Sadat, M., Toloie Ashlaghi, A., Radfar, R. (2015). Predict the success of students in math’s course in final exams in Arak city with neural networks. International Journal of Information, Security and Systems Management, 4(1), 412-417.
Maryam Sadat; Abbas Toloie Ashlaghi; Reza Radfar. "Predict the success of students in math’s course in final exams in Arak city with neural networks". International Journal of Information, Security and Systems Management, 4, 1, 2015, 412-417.
Sadat, M., Toloie Ashlaghi, A., Radfar, R. (2015). 'Predict the success of students in math’s course in final exams in Arak city with neural networks', International Journal of Information, Security and Systems Management, 4(1), pp. 412-417.
Sadat, M., Toloie Ashlaghi, A., Radfar, R. Predict the success of students in math’s course in final exams in Arak city with neural networks. International Journal of Information, Security and Systems Management, 2015; 4(1): 412-417.
Predict the success of students in math’s course in final exams in Arak city with neural networks
1Department of Information Technology Management, Electronic Branch, Islamic Azad University, Tehran, Iran
2Prof. of Islamic Azad University, Science & Research Branch, Tehran, Iran
3Associate Prof. of Islamic Azad University, Science & Research Branch Tehran, Iran
Abstract
One very significant issues in most educational systems, the question is educational achievement levels of students in exams. The educational system is one of the most important indicators of education performance and evaluation of teaching and the learning is the fact that the performance of the system output in each year. The method of the research is descriptive-survey. The research community of the study is the third grade secondary school’ students of Arak city. The study sample was 192 students in the 1389-1392 timeframe. The factors affecting the success of students in the final exams at the high school was identified and then offered the artificial neural network model to predict the success of students in examinations. The results showed the selective model of MLP with three layers (input, hidden and output) respectively with (6-3-1) neurons and the estimated accuracy was (95.249).
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