JSEA  Vol.8 No.1 , January 2015
E-Learning Optimization Using Supervised Artificial Neural-Network
Author(s) Mohamed Sayed1,2, Faris Baker1
ABSTRACT
Improving learning outcome has always been an important motivating factor in educational inquiry. In a blended learning environment where e-learning and traditional face to face class tutoring are combined, there are opportunities to explore the role of technology in improving student’s grades. A student’s performance is impacted by many factors such as engagement, self-regulation, peer interaction, tutor’s experience and tutors’ time involvement with students. Furthermore, e-course design factors such as providing personalized learning are an urgent requirement for improved learning process. In this paper, an artificial neural network model is introduced as a type of supervised learning, meaning that the network is provided with example input parameters of learning and the desired optimized and correct output for that input. We also describe, by utilizing e-learning interactions and social analytics how to use artificial neural network to produce a converging mathematical model. Then students’ performance can be efficiently predicted and so the danger of failing in an enrolled e-course should be reduced.

Cite this paper
Sayed, M. , Baker, F. (2015) E-Learning Optimization Using Supervised Artificial Neural-Network. Journal of Software Engineering and Applications, 8, 26-34. doi: 10.4236/jsea.2015.81004.
References
[1]   Soller, A. and Lesgold, A. (2003) A Computational Approach to Analyzing Online Knowledge Sharing Interaction. Proceedings of Artificial Intelligence in Education, Sydney, 20-24 July 2003, 253-260.

[2]   Sayed, M. and Baker, F. (2014) Blended Learning Barriers: An Investigation, Exposition and Solutions. Journal of Education and Practice, 5, 81-85.

[3]   Vohs, K.D. and Baumeister, R.F. (2011) Handbook of Self-Regulation: Research, Theory, and Applic-ations. 2nd Edition, Guilford Press, New York.

[4]   Zhao, C.M. and Kuh, G.D. (2004) Adding Value: Learning Communities and Student Engagement. Research in Higher Education, 45, 115-138. http://dx.doi.org/10.1023/B:RIHE.0000015692.88534.de

[5]   Zimmerman, B.J. (2008) Investigating Self-Regulation and Motivation: Historical Background Meth-odological Developments, and Future Prospects. American Educational Research Journal, 45, 166-183.http://dx.doi.org/10.3102/0002831207312909

[6]   Ferguson, R. and Shum, S.B. (2012) Social Learning Analytics: Five Approaches. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, Vancouver, 29 April-2 May 2012, ACM Press, New York.http://dx.doi.org/10.1145/2330601.2330616

[7]   Yoo, J. and Kim, J. (2014) Can Online Discussion Participation Predict Group Project Performance? Investigating the Roles of Linguistic Features and Participation Patterns. International Journal of Artificial Intelligence in Education, 24, 8-32. http://dx.doi.org/10.1007/s40593-013-0010-8

[8]   Edelstein, H.A. (1999) Introduction to Data Mining and Knowledge Discovery. 3rd Edition, Crows Corporation, Potomac.

[9]   Chang, C.Y., Wang, H.J. and Li, C.F. (2010) Image Content Analysis Using Modular RBF Neural Network. Journal of Computers, 21, 39-52.

[10]   Haykin, S. (1998) Neural Network: A Comprehensive Foundation. 2nd Edition, Prentice Hall, New York.

[11]   Srimani, P.K. and Kamath, A.S. (2012) Neural Networks Approach for the Performance Analysis of Learning Model— A Case Study. International Journal of Current Research, 4, 236-239.

[12]   Srimani, P.K. and Kamath, A.S. (2012) Data Mining Techniques for the Performance Analysis of a Learning Model— A Case Study. International Journal of Computer Applications, 53, 36-42.

[13]   Romero, C. and Ventura, S. (2007) Educational Data Mining: A Survey from 1995 to 2005. Expert Systems with Applications, 33, 135-146. http://dx.doi.org/10.1016/j.eswa.2006.04.005

[14]   Bigus, J.P. (1996) Data Mining with Neural Networks: Solving Business Problems from Application Development to Decision Support. McGraw Hill, New York.

[15]   Liu, H. (2010) On the Levenberg-Marquardt Training Method for Feed-Forward Neural Networks. Proceedings of the 6th International Conference on Natural Computation, Yantai, 456-460.

[16]   Guyon, I. and Elisseff, A. (2003) An Introduction to Variable and Feature Selection. The Journal of Machine Learning Research, 3, 1157-1182.

[17]   Mercer, N. (2004) Sociocultural Discourse Analysis: Analyzing Classroom Talk as a Social Mode of Thinking. Journal of Applied Linguistics, 1, 137-168. http://dx.doi.org/10.1558/japl.2004.1.2.137

[18]   Sayed, M. (2013) Blended Learning Environment: The Effectiveness in Developing Concepts and Thinking Skills. Journal of Education and Practice, 4, 12-17.

[19]   Tempelaar, D.T., Niculescu, A., Rienties, B., Gijselaers, W.H. and Giesbers, B. (2012) How Achievement Emotions Impact Students’ Decisions for Online Learning, and What Precedes Those Emotions. The Internet and Higher Education, 15, 161-169.
http://dx.doi.org/10.1016/j.iheduc.2011.10.003

[20]   Winne, P.H. and Baker, R.S. (2013) The Potentials of Educational Data Mining for Researching Metacognition, Motivation and Self-Regulated Learning. Journal of Educational Data Mining, 5, 1-8.

 
 
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