Analyzing Digital Education using Neutrosophic Sets

Abstract

Digital learning is a broad umbrella of any form of learning incorporating novel digital technology aspects in teaching and assessment to facilitate learning and engage students. Most education settings have moved from face–to–face to e-learning platforms worldwide ever since the beginning of the COVID-19 pandemic. Assessment of the efficiency of digital learning is essential, and assessment criteria may take several forms. Digital learning effectiveness assessment has received a significant amount of attention and work; nevertheless, a generalized quantitative evaluation model that considers the inter-affected link among criteria and the uncertainty of personal perception simultaneously is still absent. In this study, the hybrid MCDM model that was suggested handles the independent relations of assessment criteria with factor analysis, and it addresses the dependency relations of assessment via the use of AHP. The AHP and neutrosophic integral approaches are used for the purpose of creating synthetic usefulness in line with the environment of subjective perception. This quantitative research study explores students’ readiness to continue utilizing e-learning while measuring satisfaction levels with the e-learning system among higher education students in the Gulf area. Moreover, students’ behavior intention is measured here. Descriptive analysis was opted to achieve the aim of the study. Data analysis demonstrated that higher education students showed medium to high readiness levels towards e-learning and were identified with a medium level of satisfaction with the e-learning system. Moreover, students demonstrated a high intent to utilize the e-learning platform in case of offering parallel e-learning courses in the future.