The quality appraisal of massive open online courses using decision support model

Abstract

Massive open online courses (MOOCs) are becoming increasingly popular because of the impact of COVID-19 on teaching activities. Quality appraisal of MOOCs is a critical procedure for enhancing their performance. Moreover, the quality assessment of MOOCs can be acknowledged as an uncertain decision-making problem with multi-attribute and multiple decision-makers. Accordingly, a hybrid compromise ranking of alternatives from distance to ideal solution (CRADIS) model-based decision framework is developed to address the quality appraisal issue of MOOCs. In this framework, the complex and uncertain information utilized for quality appraisal is the interval-valued spherical fuzzy set (IVSFS). Then, a minimization-deviation model-based weighted Bonferroni mean operator for the IVSFS is introduced to generate the group assessment matrix considering the interactions between each pair of input preferences. Next, a novel CRADIS model based on the Heronian distance operator and IVSFS is presented to rank the quality of MOOCs, in which the IVSFS-based Cronbach's coefficient is proposed to compute the weights for the interactive criteria. Finally, a numerical example of the quality assessment of MOOCs was implemented to demonstrate the application of the proposed hybrid decision framework in a real-world appraisal problem. Subsequently, a sensitivity study of Heronian indices was conducted to test the stability and rationality of the proposed framework. Subsequently, to further display the advantages of the decision framework, a comparison analysis was conducted to discuss the assessment results derived from the proposed framework and those of existing similar approaches. The analysis results of the numerical example indicate that the proposed decision framework can provide an effective and feasible means of addressing the quality appraisal problem of MOOCs.