Multi-Criteria Evaluation of eLearning Attributes using the Fuzzy TOPSIS Method

Various models of evaluating eLearning system success have been identified in the past and the need for effective evaluation of eLearning systems has been highlighted during the COVID-19 pandemic. The purpose of the present work is to elicit how both academic staff and students (the evaluators) view the performance of eLearning attributes when being taught using an eLearning system. The attributes are ranked using a multi-criteria evaluation algorithm called the Fuzzy Technique for Order Preference by Similarity to the Ideal Solution (Fuzzy TOPSIS). Here, using linguistic-response expert questionnaires, a set of eLearning system attributes and a set of eLearning system criteria are evaluated. The Fuzzy TOPSIS algorithm yields weightings for each of the attributes which can then be ranked to arrive at the optimal solution in terms of how well they contributed to the success of the current eLearning system. IT service quality is found to rank highest, followed by technical system quality, information quality and finally the consideration of different learning styles. Large agreement is seen between academic staff and student evaluators, with minor disagreement between students of two different disciplines. As regards practical implications, it is shown from the rankings that the eLearning system must be reorganized and consideration of different learning styles must be improved. The Fuzzy TOPSIS method has been found to be a reliable and economic evaluation approach of eLearning systems, since it does not require large numbers of evaluators and provides a ranking of attributes which translate directly into priorities for improvement.