Open Science Research Excellence

Open Science Index

Commenced in January 2007 Frequency: Monthly Edition: International Publications Count: 29286

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Classifying Students for E-Learning in Information Technology Course Using ANN
This research’s objective is to select the model with most accurate value by using Neural Network Technique as a way to filter potential students who enroll in IT course by Electronic learning at Suan Suanadha Rajabhat University. It is designed to help students selecting the appropriate courses by themselves. The result showed that the most accurate model was 100 Folds Cross-validation which had 73.58% points of accuracy.
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[1] Stamos T. Karamouzis, "Sensitivity Analysis of Neural Network Parameters for Identifying the Factors for College Student Success”, Journal of 2009 World Congress on Computer Science and Information Engineering, USA, April 2009, pp. 671-675.
[2] Naciye Hardalaç, Nevhiz Ercan, Fırat Hardalaç and Salih Ergüt, "Classification of Educational Backgrounds of Students Using Musical Intelligence and Perception with the Help of Artificial Neural Networks”, Journal of 36th ASEE/IEEE Frontiers in Education Conference, USA, October 2006, pp. 9-14.
[3] Dominic-Palmer Brown, Chrisina Draganova and Sin Wee Lee, "Snap- Drift Neural Network for Selecting Student Feedback”, Proceedings of International Joint Conference on Neural Networks, USA, June 2009, pp. 391-398.
[4] Xianmin Wei, "Student Achievement Prediction Based on Artificial Neural Network”, Journal of 2011 International Conference on Internet Computing and Information Services, Hong Kong, September 2011, pp. 485-487.
[5] Valquíria R. C. Martinho, Clodoaldo Nunes and Carlos Roberto Minussi, "An Intelligent System for Prediction of School Dropout Risk Group in Higher Education Classroom based on Artificial Neural Networks”, Journal of 2013 IEEE 25th International Conference on Tools with Artificial Intelligence, USA, November 2013, pp. 159-166.
[6] Xiujuan FAN, Runping HAN and Guifang WANG, "Fuzzy Neural Network Model for Comprehensive Quality Evaluation on College Students”, Journal of 2010 International Conference on Intelligent Computation Technology and Automation, Chaina, May 2010, pp. 375- 378.
[7] D.Marquardt, "An Algorithm for Least Squares Estimation of Non- Linear Parameter”, J. Soc. Ind. Appl.Math., pp. 1963.
[8] L.Fausett, "Fundamentals of Neural Networks Architecture.Algorithms and Applications”, Pearson Prentice Hall, USA, 1994.
[9] M.J. Diamantopoulou, V.Z. Antonopoulos and D.M. Papamichail "The Use of a Neural Network Technique for the Prediction of Water Quality Parameters of Axios River in Northern Greece”, Journal 0f Operational Research, Springer-Verlag, Jan 2005, pp. 115-125.
[10] D. Anguita, S.Ridella and F.Rivieccio, "K-folds Generalization Capability Assessment for Support Vector Classifiers”, Proceeding of International Joint Conference on Neural Network, Canada, 2005, pp. 855-858.
[11] Li-hua Chen, and Xiao-yun Zhang, "Application of Artificial Neural Network to Classify Water Quality of the Yellow River”, Journal 0f Fuzzy Information and Engineering, Springer-Verlag, Jan 2009, pp. 15- 23.
[12] M.J. Diamantopoulou, V.Z. Antonopoulos and D.M. Papamichail, "The Use of a Neural Network Technique for the Prediction of Water Quality Parameters of Axios River in Northern Greece”, Journal 0f Operational Research, Springer-Verlag, Jan 2005, pp. 115-125.
[13] S.H.Musavi and M.Golabi, "Application of Artificial Neural Networks in the River Water Quality Modeling: Karoon River,Iran”, Journal 0f Applied Sciences, A sian Network for Scientific Information, 2008, pp. 2324-2328.
[14] Chi Zhou, Liang Gao and Chuanyong Peng, "Pattern Classification and Prediction of Water Quality by Neural Network with Particle Swarm Optimization”, Proceedings of the 6th World Congress on Intelligent Control and Automation, China, June 2006, pp. 2864-2868.
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