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The Domains For The Multi-Criteria Decisions About E-Learning Systems

Year 2012, Volume: 13 Issue: 2, 198 - 211, 01.06.2012

Abstract

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.

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, –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, , 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. –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.
  • 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 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.
Year 2012, Volume: 13 Issue: 2, 198 - 211, 01.06.2012

Abstract

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, –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, , 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. –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.
  • 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 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.
There are 20 citations in total.

Details

Primary Language English
Journal Section Articles
Authors

Murat Pasa Uysal This is me

Publication Date June 1, 2012
Submission Date February 27, 2015
Published in Issue Year 2012 Volume: 13 Issue: 2

Cite

APA Uysal, M. P. (2012). The Domains For The Multi-Criteria Decisions About E-Learning Systems. Turkish Online Journal of Distance Education, 13(2), 198-211.