A Hybrid MCDM Approach for Evaluating Web-Based E-Learning Platforms

The outbreak of COVID-19 has promoted distance learning and rapidly increased the usage of online learning platforms. As a result, more and more IT companies are competing to offer high-quality Web-based E-Learning Platforms (WELPs). However, the problem facing educational institutions is how to evaluate the quality of WELPs to choose the one that best fulfills their needs. In order to select the most appropriate WELP among different alternatives, many evaluation criteria must be considered by the Decision Maker (DM). Hence, evaluating WELPs is a complex Multi-Criteria Decision Making (MCDM) problem that needs to be addressed efficiently. In literature, we have noticed that MCDM methods are rarely used for evaluating WELPs. In addition, traditional MCDM methods suffer from additive complexity and inconsistency due to the numerous pairwise comparisons of criteria. In contrast, Hybrid MCDM (HMCDM is a promising and more efficient decision-support tool. In this paper, we propose a HMCDM approach for evaluating and ranking WELPs which is more efficient and more reliable than traditional approaches. The proposed approach incorporates different techniques (i.e., BWM, SAW, and Delphi) and comprises the following three phases: 1) a Hierarchical Structure Quality Model (HSQM) is defined in which the evaluation criteria are identified; 2) a Criteria Preference Structure (CPS) is developed where the criteria identified in HSQM are weighted using the pairwise comparison Best-Worst Method (BWM); 3) the performance of alternative WELPs w.r.t criteria is estimated and integrated with the CPS using the Simple Additive Weighting (SAW) method to determine their ranking. The widely used consensus method, Delphi, has been utilized in phases 2 and 3 to estimate the relative preferences of the criteria and the scores of alternatives over these criteria. The proposed approach has been validated and compared to the widely accepted MCDM method, Analytical Hierarchy Process (AHP). The results revealed that the proposed approach surpasses AHP.