Prioritizing e-learning websites evaluation and selection criteria using fuzzy set theory

Garg, R.; Jain, D.

Abstract: E-learning websites evaluation and selection is extremely important for the establishment of ef-fective E-learning. The E-learning website selection has crucial importance for the educational sector. The selection of E-learning website problem is generally considered as a Multi-Criteria Decision Making (MCDM) problem which mainly consists of both qualitative and quantitative criteria. The development of an E-learning website mainly depends on the success of the E-learning website selection along with various alternatives. So, for the effective evaluation and se-lection of E-learning websites, a set of selection criteria should be obtained. This paper consists of two steps, the first step is the identification of E-learning website selection criteria, second step provides the linguistic variables against the selection criteria and then fuzzy set theory (FST) is adopted for the calculation of the priority weights of each selection criteria. To show the rela-tive importance of each selection criteria, they ranked according to their global weights.

How to cite this paper

 Garg, R & Jain, D. (2017). Prioritizing e-learning websites evaluation and selection criteria using fuzzy set theory.Management Science Letters , 7(4), 177-184.

References

Alias, N., Zakariah, Z., Ismail, N. Z., & Aziz, M. N. A. (2012). E-Learning successful elements for higher learning institution in Malaysia. Procedia-Social and Behavioral Sciences, 67, 484-489.
Govindasamy, T. (2002). Successful implementation of e-learning: Pedagogical considerations. The internet and higher education, 4(3), 287-299.
Jain, D., Garg, R., & Bansal, A. (2015). A Parameterized Selection and Evaluation of E-Learning Websites Using Topsis Method. International Journal of Research & Development in Technology and Management Science, 22(3), 12-26.
Jain, D., Garg, R., Bansal, A., & Saini, K. K. (2016). Selection and ranking of E-learning websites us-ing weighted distance-based approximation. Journal of Computers in Education, 3(2), 193-207.
Wands, M., & Le Blanc, A. (2001). Critical Success Factors: eLearning Solutions. The Official e-Newsletter of the Change and Learning Practice, Internet Time Group, Berkeley, California.
MacDonald, C. J., & Thompson, T. L. (2005). Structure, content, delivery, service, and outcomes: Quality e-learning in higher education. The International Review of Research in Open and Distrib-uted Learning, 6(2).
Mehregan, M. R., Jamporazmey, M., Hosseinzadeh, M., & Mehrafrouz, M. (2011, September). Pro-posing an approach for evaluating e-learning by integrating critical success factor and fuzzy AHP. In International conference on innovation, management and service, Singapore.
Nokelainen, P. (2006). An empirical assessment of pedagogical usability criteria for digital learning material with elementary school students. Educational Technology & Society, 9(2), 178-197.
Ozkan, S., & Koseler, R. (2009, October). Multi-dimensional evaluation of e-learning systems in the higher education context: an empirical investigation of a computer literacy course. In Frontiers in Education Conference, 2009. FIE'09. 39th IEEE (pp. 1-6). IEEE.
Selim, H. M. (2007). Critical success factors for e-learning acceptance: Confirmatory factor mod-els. Computers & Education, 49(2), 396-413.
Sun, P. C., Tsai, R. J., Finger, G., Chen, Y. Y., & Yeh, D. (2008). What drives a successful e-Learning? An empirical investigation of the critical factors influencing learner satisfac-tion. Computers & Education, 50(4), 1183-1202.
Wang, Y. S. (2003). Assessment of learner satisfaction with asynchronous electronic learning sys-tems. Information & Management, 41(1), 75-86.
Zadeh L. A. (1965). Fuzzy sets. Information and Control, 8, 338–353.