MAGDM for Evaluating the Role of Computer Science in Education with PLq-ROF Heronian Mean Operators and Maximizing Deviation-MOORA Methodology - Shafiq, Aqsa; Rasheed, Muhammad Waheed; Habeeb, Marwah Shaker; Tasneem, Rabia; Shabbir, Nimra; Alameri, Abdu (Springer Nature, 2025)
Computer science (CS) has changed educational processes in a wide range of academic fields and is now considered to be an essential part of modern education. This manuscript presents a novel methodology in which CS can be used to solve judgment concerns in education. The conventional education system frequently finds it difficult to deal with the ambiguities and linguistic expressions that are an integral component of human judgment and choice-making processes. By combining probabilistic uncertainty with linguistic concepts, a probabilistic linguistic q-rung orthopair fuzzy set (PLq-ROFS) presents an efficient mathematical methodology for describing inaccurate and unclear facts to handle these concerns. For aggregating the assessment data, we first propose the PLq-ROF Heronian mean operators. The maximizing deviation methodology is a helpful tool when dealing with situations where the knowledge about the attribute weights is either partially or fully unknown. To find attribute weights, this study presents the PLq-ROF-maximizing deviation methodology. We also propose the multi-objective optimization by ratio analysis (MOORA) methodology with PLq-ROFNs to evaluate the options and identify an optimal choice. Furthermore, a case study related to the judgment process on CS applications in education is provided from the PLq-ROFS perspective to illustrate the effectiveness and use of the developed technique. The numerical results show that {\mathbb {A}}_{4} (namely: skill development) is the best CS application in education.