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Prediction of Student's Performance with Deep Neural Networks
Meryem Karlik, Bekir KARLIK
Pages - 39 - 48     |    Revised - 31-05-2020     |    Published - 30-06-2020
Volume - 9   Issue - 2    |    Publication Date - June 2020  Table of Contents
Deep Learning, Prediction, Student Performance, Fuzzy Clustering Neural Networks.
The performance of education has a big part in people's life. The prediction of student's performance in advance is very important issue for education. School administrators and students' parents impact on students' performance. Hence, academic researchers have developed different types of models to improve student performance. The main goal to reveal of this study is to search the best model of neural network models for the prediction of the performance of the high school students. For this purpose, five different types of neural network models have been developed and compared to their results. The data set obtained from Taldykorgan Kazakh Turkish High School (in Kazakhstan) students was used. Test results show that proposed two types of neural network model are predicted students' real performance efficiently and provided better accuracy when the test of today’s and future’s samples have similar characteristics.
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26 Karlik Bekir, Myoelectric control using artificial neural networks for multifunctional prostheses, PhD Thesis, Yildiz Technical University, Istanbul, Turkey, March 1994.
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29 Alaka Halil, Master Thesis, Evaluation of Student Performance in Kazakhistan than Using Neural Networks, Kazakhstan Suleyman Demirel University, Almaty, Kazakhstan, July 2013.
Dr. Meryem Karlik
Department of Foreign Languages, Silk Road International University of Tourism, Samarkand - Uzbekistan
Professor Bekir KARLIK
McGill University, Neurosurgical Simulation, Research & Artificial Intelligence Learning Centre, Montréal - Canada