Hybrid ranking method for e-learning platform selection: A flexible approach

Abstract

E-Learning platforms comparison helps users select the most suitable platform according to their individual pedagogical needs and objectives. However, from decision support perspective, selecting the optimal platform in terms of tools and services that meet user’s requirements still remain difficult to achieve. Thus, we investigate in this paper an e-Learning evaluation method based on a symbolic approach using preference operators and Qualitative Weight and Sum method (QWS) [1] providing a total order among the considered e-Learning systems. However, even the totality ensured by the preference operators we developed, they are still not sufficient from decision making perspective, since they can return a ranking in which several alternatives are all equally and indistinguishably (un)satisfactory. Therefore, we combine our symbolic approach with a flexible ranking method based on linguistic quantifiers and fuzzy quantified propositions along with two new parameters for quality assessment refinement, called least satisfactory proportion and greatest satisfactory proportion, denoted by lsp and gsp respectively, to be able to discriminate among alternatives evaluated as equal. The hybrid method obtained can significantly refine the ranking providing users with valuable information to help them make decisions.