Challenges and applications of multi-model learning analytics in decision support for physical education teaching in the context of intuitionistic fuzzy Z-numbers
Xue Feng Deng, Xue Bai, Zepei Li · Journal of Computational Methods in Sciences and Engineering · 2025
Challenges and applications of multi-model learning analytics in “Decision Support for Physical Education Teaching” refers to a domain where learning analytics techniques are applied to enhance decision-making processes in physical education instruction. This case study explores the inauguration of a new educational initiative in a developing nation, illuminating the complex web of parties engaged in the process. The usage of Intuitionistic Fuzzy Z ˘ -Number Sets (IF Z ˘ NS) becomes crucial while handling advanced problems in Multi-Attribute Decision Making (MADM). These sets provide a significant improvement over existing fuzzy set designs in handling higher degrees of uncertainty. The method we provide in this work called MARCOS, is intended to discourse MADM complications in IF Z ˘ NS, predominantly in context while attribute weights are unknown. We derive attribute weights through the entropy measure. The study begins by investigating IF Z ˘ NS, analyzing their scoring and accuracy, and clarifying the essential perception that underpins their operations. Next, we recommend a decision-making method to handle MADM cases by using IF Z ˘ NS information. This work improves this field’s practical execution as well as its theoretical underpinnings. We use the Combined Compromise Solution (CoCoSo) approach to a comparison study to verify and prove the legitimacy of our results. This meticulous strategy agreements a systematic estimation of the advocated methodology’s efficiency and complements the existing argument on efficient decision-making in situations that are complicated and erratic.