Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

(394.75KB)
This is an Open Access publication published under CSC-OpenAccess Policy.
Publications from CSC-OpenAccess Library are being accessed from over 74 countries worldwide.
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
MORE INFORMATION
KEYWORDS
Deep Learning, Prediction, Student Performance, Fuzzy Clustering Neural Networks.
ABSTRACT
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.
1 Norazah Yusof, Nur Ariffin Mohd Zin, Noraniah Mohd Yassin, Paridah Samsuri, Evaluation of Student's Performance and Learning Efficiency based on ANFIS, International Conference of Soft Computing and Pattern Recognition, pp. 460-465, 2009.
2 Sevindik Tuncay, Prediction of student academic performance by using an adaptive neuro-fuzzy inference system, Energy Education Science and Technology Part B-Social and Educational Studies, vol. 3, Issue: 4, pp. 635-646, October 2011.
3 J. K. Alenezi, M. M. Awny and M. M. M. Fahmy, The Effectiveness of Artificial Neural Networks in Forecasting the Failure Risk: Case Study on Pre-medical Students of Arabian Gulf University, International Conference on Computer Engineering & Systems, pp. 135-138, 14-16 Dec. 2009.
4 Ioannis E. Livieris, Konstantina Drakopoulou, Panagiotis Pintelas, Predicting Students' performance using artificial neural networks, 8th PanHellenic Conference with International Participation Information and Communication Technologies in Education, pp. 321-328, September 2012, Volos, Greece.
5 Victor Oladokun, A T Adebanjo, O E Charles-Owaba, Predicting Students Academic Performance using Artificial Neural Network: A Case Study of an Engineering Course, he Pacific Journal of Science and Technology, vol. 9, no. 1, pp. 72-79, May-June 2008.
6 Jennifer L. Sabourin, Lucy R. Shores, Bradford W. Mott & James C. Lester, Understanding and Predicting Student Self-Regulated Learning Strategies in Game-Based Learning Environments, International Journal of Artificial Intelligence in Education, vol. 23, pp. 94-114, 2013.
7 Meryem Karlik, Artificial Neural Networks Methodology for Second Language Learning, LAP LAMBERT Academic Publishing, May 5, 2017.
8 Meryem Karlik & Azamat Akbarov, Investigating the Effects of Personality on Second Language, Learning through Artificial Neural Networks, International Journal of Artificial Intelligence and Expert Systems, vol. 7, Issue. 2, pp. 25-36, 2016.
9 Dorina Kabakchieva, Student Performance Prediction by Using Data Mining Classification Algorithms, International Journal of Computer Science and Management Research, vol. 1, Issue 4, pp. 686-690, November 2012.
10 Aysha Ashraf, Sajid Anwer, and Muhammad Gufran Khan, A Comparative Study of Predicting Student's Performance by use of Data Mining Techniques, American Scientific Research Journal for Engineering, Technology, and Sciences, vol. 44(1), pp. 122-136, 2018.
11 Zeba Parveen, Mohatesham Pasha Quadri, Classification and Prediction of Student Academic Performance using Machine Learning: A Review, International Journal of Computer Sciences and Engineering, vol.7, Issue.3, pp.607-614, 2019.
12 Ozbay Yuksel, Karlik Bekir, A Fast Training Back-Propagation Algorithm on Windows, 3rd International Symposium on Mathematical & Computational Applications, pp. 204-210, 4-6 September, 2002, Konya, Turkey.
13 Karlik Bekir, Differentiating Type of Muscle Movement via AR Modeling and Neural Networks Classification of the EMG, Turkish Journal of Electrical Engineering & Computer Sciences, vol. 7, no.1-3, pp. 45-52, 1999.
14 Yann LeCun, Yoshua Bengio, and Geoffrey Hinton, Deep learning, Nature, vol. 521, pp. 436-444, 2015.
15 Karlik Bekir, Uzam Murat, Cinsdikici Muhammet, and Jones A. H., Neurovision-Based Logic Control of an Experimental Manufacturing Plant Using Convolutional Neural Net Le-Net5 and Automation Petri Nets, Journal of Intelligent Manufacturing, vol. 16(4-5), 527-548, 2005.
16 Hameed Alaa Ali, Karlik Bekir, Salman M. Shukri, Back-propagation Algorithm with Variable Adaptive Momentum, Knowledge-Based Systems, vol.114, pp.79-87, December 2016.
17 Erik Berglund and Joaquin Sitte, The parameter-less self-organizing map algorithm, IEEE Transactions on Neural Networks, vol. 17(2), pp. 305-316, March 2006.
18 Erik Berglund, Improved PLSOM algorithm, Applied Intelligence, vol. 32, Issue 1, pp 122-130, February 2010.
19 Hameed Alaa Ali, Karlik Bekir, Salman M. Shukri, Eleyan Gulden, Robust Adaptive Learning Approach of Self-Organizing Maps, Knowledge-Based Systems, vol. 171 - May 1, pp. 25-36, 2019.
20 Hameed Alaa Ali, Ajlouni Naim, Karlik Bekir, Robust Adaptive SOMs Challenges in a Varied Datasets Analytics, Springer Nature Switzerland, Vellido et al. (Eds.): WSOM 2019, AISC 976, pp. 110-119, https://doi.org/10.1007/978-3-030-19642-4_11
21 Y. LeCun, Y. Bengio, and G. Hinton, Deep learning, Nature, vol. 521, no. 7553, pp. 436-444, 2015.
22 Chigozie Enyinna Nwankpa, Winifred Ijomah, Anthony Gachagan, and Stephen Marshall, Activation Functions: Comparison of Trends in Practice and Research for Deep Learning, arXiv.org > cs > arXiv:1811.03378
23 Karlik Bekir and Olgac Ahmed Vehbi, Performance Analysis of Various Activation Functions in Generalized MLP Architectures of Neural Networks, International Journal of Artificial Intelligence and Expert Systems, vol. 1, no. 4, pp. 111-122, 2011.
24 Ceylan Rahime, Ozbay Yuksel, Karlik Bekir, Comparison of Type-2 Fuzzy Clustering Based Cascade Classifier Models for ECG Arrhythmias, Biomedical Engineering: Applications, Basis and Communications (BME), vol. 26, no. 6, 2014, 1450075
25 Karlik Bekir, The Positive Effects of Fuzzy C-Mean Clustering on Supervised Learning Classifiers, International Journal of Artificial Intelligence and Expert Systems, vol. 7(1), pp. 1-8, 2016.
26 Karlik Bekir, Myoelectric control using artificial neural networks for multifunctional prostheses, PhD Thesis, Yildiz Technical University, Istanbul, Turkey, March 1994.
27 Karlik Bekir, Tokhi Osman, Alci Musa, A Fuzzy Clustering Neural Network Architecture for Multi-Function Upper-Limb Prosthesis, IEEE Trans. on Biomedical Engineering, vol. 50, no:11, pp.1255-1261, 2003.
28 Karlik Bekir, Koçyiğit Yücel, Korürek Mehmet, differentiating types of muscle movements using a wavelet based fuzzy clustering neural network, Expert Systems, vol. 26 (1), pp. 49-59, 2009.
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
bkarlik@hotmail.com