You are here

Home » Content

THE DOMAINS FOR THE MULTI-CRITERIA DECISIONS ABOUT E-LEARNING SYSTEMS

Journal Name:

  • The Turkish Online Journal of Distance Education

Publication Year:

  • 2012

Key Words:

Author Name
Abstract (2. Language): 
Developments in computer and information technologies continue to give opportunities for designing advanced E-learning systems while entailing objective and technical evaluation methodologies. Design and development of E-learning systems require time-consuming and labor-intensive processes; therefore any decision about these systems and their analysis needs systematic and structured guidance to lead to better decisions. Multi-Criteria Decision Analysis (MCDA) techniques are applicable in instructional technology-related research areas as well as in other academic disciplines. In this study, a conceptual domain model and a decision activity framework is proposed for E-learning systems. Instructional, technological, and administrative decision domains are included in this model. Finally, an illustrative example is given to show that AHP is an effective MCDA method for E-learning-related decisions.
Bookmark/Search this post with
FULL TEXT (PDF): 
PDF icon arastirmax-domains-multi-criteria-decisions-about-e-learning-systems.pdf
  • 2
198-211
  • English

REFERENCES

References: 

Belton, S., & Stewart, T. S. (2002). Multiple criteria decision analysis. An integrated approach. Kluwer Academic Publishers, Massachusetts.
Chang, C-T., & Lin-T-C. (2009). Interval goal programming for s-shaped penalty function. European Journal of Operational Research, 9, pp. 9–20.
Chao R-J., & Chen, Y-H. (2009). Evaluation of the criteria and effectiveness of distance e-learning with consistent fuzzy preference relations. Expert Systems with Applications 36, 10657–10662.
Clemen, R. T. (1996). Making hard decisions. Belmont, CA: Wadsworth Publishing Company.
Ho, W., Xu, X., & Dey, P.K. (2010). Multi-criteria decision making approaches for supplier evaluation and selection: A literature review. European Journal of Operational Research, 202, pp. 16–24.
Giannoulis, C., & Ishizaka, A. (2010). A web-based decision support system with ELECTRE III for a personalized ranking of British universities. Decision Support Systems, 48, pp. 488–497.
Lee, B-C., Yoon, J-O., & Lee I. (2008). Learners’ acceptance of e-learning in South Korea: Theories and results. Computers and Education, 53, 1320–1329.
211
Liaw, S. S. (2008). Investigating students’ perceived satisfaction, behavioral intention, and effectiveness of e-learning: A case study of the blackboard system. Computers and Education, 51, 864–873.
Kao, H.Y., Liu, M.C., Huang, C. L., & Chang, Y. C. (2009). E-learning systems evaluation with data envelopment analysis and Bayesian networks. 2009 Fifth International Joint Conference on INC, IMS and ID. IEEE DOI 10.1109/NCM.2009.28.
Mendoza, G.A., & Martins., H. (2006). Multi-criteria decision analysis in natural resource management: a critical review of methods and new modelling paradigms. Forest Ecology and Management, 230, 1–22.
Merrill L, M. D. (1996). Instructional transaction theory: An instructional design model based on knowledge objects. Retrieved February 3, 2011 from http://cito.byuh. edu/merril
Ray, S. C. (2004). Data envelopment analysis: Theory and techniques for economics and operations research, Cambridge University Press, New York, NY.
Shee, D. Y., & Wang, Y. (2008). Multi-criteria evaluation of the web-based e-learning system: A methodology based on learner satisfaction and its applications. Computers & Education, 50, 894–905.
Shih M., Feng J., & Tsai C-C. (2008). Research and trends in the field of e-learning from 2001 to 2005: A content analysis of cognitive studies in selected journals. Computers & Education, 51, 955–967.
Sun, P-C., Cheng H.K., & Finger G. (2009). Critical functionalities of a successful e-learning system: an analysis from instructors' cognitive structure toward system usage. Decision Support Systems 48, 293–302.
Turvey, K. (2010). Pedagogical-research designs to capture the symbiotic nature of professional knowledge and learning about e-learning in initial teacher education in the UK. Computers & Education 54, 783–790.
Uysal, M. P. (2010). Analytic hierarchy process approach to decisions on instructional software. 4. International Computer & Instructional Technologies Symposium, Konya, Turkey.
Vaidya, O. S., & Kumar, S. (2006). Analytic hierarchy process: An overview of applications. European Journal of Operations Research. 169, 1-29.

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