Impact of massive open online courses in higher education using machine learning and decision based fuzzy frank power aggregation operators models

Massive open online courses (MOOCs) have transformed higher education by providing widespread access to quality educational content, and the integration of machine learning (ML) has significantly enhanced their effectiveness and adaptability. This article is designed to illustrate insufficient and vague types of information in expert's judgment using a multi-criteria group decision-making (MCGDM) problem. For this purpose, we modify the theoretical concepts of circular intuitionistic fuzzy set (Cir-IFS), which is an extended framework of fuzzy theory and intuitionistic fuzzy models. We derive robust power aggregation operators to find out the degree of weights of conflicting criteria. Moreover, a list of new mathematical approaches to power-weighted average and power-weighted geometric operators is also deduced. Some appropriate properties and special cases are discussed to reveal the efficiency and feasibility of the proposed aggregation operators. The MCGDM problem is established to determine the flexible ranking of alternatives under different conflicting criteria. Using decision algorithms and mathematical models, resolve a numerical example to find an appropriate platform that offers different MOOCs to improve higher education in the country. Additionally, a comparative study is modified to showcase the superiority and effectiveness of developed mathematical methodologies.