Revolutionizing Education: An Optimal MAGDM-Based e-Learning Approach for Curriculum Beyond the Classroom

The rise of web applications in the form of e-learning websites has presented both opportunities and challenges for academic organizations and individuals involved in education. Many organizations have developed websites to provide education and enhance skills. However, the rapid growth of e-learning brings the challenge of evaluating and selecting the most suitable e-learning websites. One approach to address this challenge is through Multi-Attribute Group Decision-Making (MAGDM) problems. To select the best e-learning website, this study proposes an integrated model in a 2-tuple linguistic q-rung orthopair fuzzy (2TLq-R) set. The proposed model utilizes the 2TLq-R set, a robust mathematical framework capable of handling uncertainties and linguistic assessments in decision-making problems. To improve the model’s effectiveness, new operators for 2TLq-R numbers are introduced, utilizing the Hamacher t-norm (TN) and t-conorm (TCN) operations. These operators enable efficient aggregation and comparison of linguistic evaluations, streamlining the selection process for the e-learning website. With the proposed approach, decision-makers can assess e-learning websites based on multiple attributes, including content quality, interactivity, user experience, and cost-effectiveness. This comprehensive evaluation allows decision-makers to make informed choices when selecting the most suitable e-learning platform. This paper investigates the properties and analyzes specific cases of 2TLq-R operators to understand their characteristics. Moreover, the study combines the Multi-Attributive Border Approximation Area Comparison (MABAC) method with the 2TLq-R operators to incorporate the decision-makers’ psychological behavior. This integration leads to the development of a novel approach called 2TLq-R-MABAC, which effectively addresses real-world MAGDM problems. Additionally, the paper includes an illustrative example and a comparative evaluation to demonstrate the practicality and applicability of the proposed method for selecting the optimal e-learning website.