Neutrosophic BWM method: An effective approach for evaluating online learning platforms in the Covid-19 Era.

Nowadays, there are numerous and wide variety of online learning platforms (OLP) available for students and educators. In order to make the online learning process run smoothly, educators need to be smart in choosing a good medium to use. Many factors should be considered concurrently during decision-making processes. As a result, Multi Criteria Decision-Making (MCDM) method, which seeks to pick the best option from a set of choices, can help decision makers in making suitable and unambiguous judgments in difficult situations. To evaluate such difficulties, this paper combines neutrosophic set with the Best-Worst Method (BWM) approach. The neutrosophic set is important in solving indeterminacy problems and it is more suitable to solve real-world decision-making problems. Besides, in the MCDM problem, similarity measures may be used to describe how different alternatives vary and are similar to one another. However, it appears that the existing similarity measures seem to have a few major weaknesses and inherent problems, such as producing unreasonable results in some cases. Therefore, to cater such problems, this paper introduced a novel single-valued neutrosophic best-worst method, SVN-BWM approach with improved similarity measures of the single-valued neutrosophic set (SVNS) model into a case study of selecting the best online learning platforms based on educators' preferences. The data are collected from six decision makers (DM) among Mathematics lecturers of Universiti Teknologi MARA (UiTM) Shah Alam. From the findings, it is found that Google Meet is the most preferred online learning platform. [ABSTRACT FROM AUTHOR]