A Computer Integrated Framework for E-learning Control Systems Based on Data Flow Diagrams

Journal Name:

Publication Year:

Abstract (2. Language): 
E-learning is currently considered as a valid and effective didactic methodology in courses at different levels such as high-school or university education, or net-based teaching. In scientific fields the adoption of e-Learning is more complex since courses have to include not only theoretical concepts but also practical activities on specific instrumentation. Over the past two decades, diverse research efforts have been made towards the personalization of e-learning platforms. This feature increases remarkably the quality of the provided learning services, since the users’ special needs and capabilities are respected. The idea of predicting the users’ preferences and adapting the e-learning platform accordingly is the focal point of this paper. This paper discusses different control systems in virtual educational system and highlights their properties. In conclusion, we derive data flow diagram (DFD) of a control system in e-learning environment designed to aid the managers for better control and decision making.



[1] E-Class Webpage 2007. /http://www.eclass.net/S, 2007.
[2] FLE3 Webpage. /http://fle3.uiah.fi/S, 2007.
[3] Moodle webpage 2007. /http://moodle.org/S, 2007.
[4] A, Turker, Go¨ rgu¨ n I, Conlan O. The challenge of content creation to facilitate personalized e-learning experiences. Int. J E-Learning 2006;5(1):11–7.
[5] P. Brusilovsky, Sosnovsky SA, Shcherbinina O. User modeling in a distributed e-learning architecture. In: Ardissono L, Brna P, Mitrovic A, editors. User modeling 2005. Lecture notes in artificial intelligence. Berlin: Springer; 2005. p. 387–91.
[6] A. Christea, Evaluating adaptive hypermedia authoring while teaching adaptive systems. In: Proceedings of the 2004 ACM symposium on applied computing table of contents. Nicosia, Cyprus, 2004. p. 929–34.
Journal of Control Engineering and Technology (JCET)
JCET Vol. 2 No. 2 April 2012 PP. 97-105 www.ijcet.org ○C World Academic Publishing
[7] M. Alrifai, Dolog P, Nejdl W. Learner profile management for collaborating adaptive e-earning alications. In: Proceedings of APS’2006 joint international workshop on adaptivity, personalization and the semantic web at the 17th ACM Hypertext’06 conference, Odense, Denmark. New York: ACM Press; 2006.
[8] F. Garzotto, Cristea A. ADAPT Major design dimensions for educational adaptive hypermedia. In: Kommers P, Richards G, editors. Proceedings of world conference on educational multimedia, hypermedia and telecommunications. Chesapeake, VA: AACE; 2004. p. 1334–9.
[9] A. O’Connor, Wade V, Conlan O. Context-informed adaptive hypermedia. In: Proceedings of the sixth international conference on ubiquitous computing (Ubicomp’04), 2004.
[10] I. Mahdavi, Fazlollahtabar, H., Tajdin, A. and Shirazi, B. (2008). Adaptive statistical analysis on higher educational systems, Journal of Applied Sciences, 8 (17), 2998-3004.
[11] H. Fazlollahtabar, and Sharma, N.K. (2008). E-Learning versus Face-to-Face Learning: An Economic Analysis of Higher Educational Systems in Iran, International Journal of Cyber Society and Education, 1 (1), 49-60.
[12] I. Mahdavi, Fazlollahtabar, H. and Yousefpoor, N. (2008). Applying mathematical programming and GRA technique to optimize e-learning based educational systems: implementation and teaching skills, Proceedings of the 5th WSEAS/IASME international conference on Engineering education, Heraklion, Greece, 127-131.
[13] I. Mahdavi, Fazlollahtabar, H. and Yousefpoor, N. (2008). An Integrated Mathematical Programming Approach and Artificial Intelligence Techniques for Optimizing Learning Elements in E-Learning Based Educational Systems, International Journal of Education and Information Technologies, 2 (1), 87-94.
[14] H. Fazlollahtabar, and Yousefpoor, N. (2009). Cost Optimization in E-learning-Based Education Systems: implementation and learning sequence, E-Learning, 6(2), 198-205.
[15] H. Fazlollahtabar, (2008). A Dynamic Programming Approach to Identifying the Shortest Path in Virtual Learning Environments, E-Learning, 5(1), 89-96.
[16] H. Fazlollahtabar, and Mahdavi, I. (2009). User/tutor optimal learning path in e-learning using comprehensive neuro-fuzzy approach, Educational Research Review, 4 (2), 142-155.
[17] A. Tajdin, Mahdavi, I., Shirazi, B., Sahebjamnia, N. and Fazlollahtabar, H. (2008). Designing an Assessment Method for E-Learning Environment Using Real-Time Simulators, Journal of Applied Sciences, 8 (19), 3491-3496.
[18] I. Mahdavi, Fazlollahtabar, H., Heidarzade, A., Mahdavi-Amiri, N. and Rooshan, Y.I. (2008). A heuristic methodology for multi-criteria evaluation of web-based E-learning systems based on user satisfaction, Journal of Applied Sciences, 8 (24), 4603-4609.
[19] I. Juvina van Oostendorp H. Individual differences and behavioral metrics involved in modeling web navigation. Submitted to UAIS, Published on-line by Springer at /http://www.springerlink.com/index/ 3VGBEC2G8DG346MJS, 2004. p. 77.
[20] [20] LSA webpage, 2006. Latent semantic analysis (LSA), Official web site, /http://lsa.colorado.edu/S, 2006.
[21] K. Neville, Heavin, C., & Walsh, E. (2005). A case in customizing e-learning. Journal of Information Technology, 20(2), 117–129.
[22] M. Parikh, and Verma, S. (2002), Utilizing internet technologies to support learning: An empirical analysis. International Journal of Information Management, 22, 27–46.
[23] B. Hackett, (2000). Beyond knowledge management: New ways to work and learn. New York: Conference Board, p. 24.
[24] A. Scigliano, and Laurie P. Dringus., (2000), A lifecycle model for online learning management: 21 critical metrics for the 21st century, The Internet and Higher Education, Volume 3, Issues 1-2, 1st Quarter-2nd Quarter Pages 99-115.

Thank you for copying data from http://www.arastirmax.com