Challenges and applications of multi-model learning analytics in decision support for physical education teaching in the context of intuitionistic fuzzy Z-numbers

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 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mover accent="true"> <mml:mi>Z</mml:mi> <mml:mo>˘</mml:mo> </mml:mover> </mml:mrow> </mml:math> -Number Sets (IF <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mover accent="true"> <mml:mi>Z</mml:mi> <mml:mo>˘</mml:mo> </mml:mover> </mml:mrow> </mml:math> 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 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mover accent="true"> <mml:mi>Z</mml:mi> <mml:mo>˘</mml:mo> </mml:mover> </mml:mrow> </mml:math> NS, predominantly in context while attribute weights are unknown. We derive attribute weights through the entropy measure. The study begins by investigating IF <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mover accent="true"> <mml:mi>Z</mml:mi> <mml:mo>˘</mml:mo> </mml:mover> </mml:mrow> </mml:math> 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 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mover accent="true"> <mml:mi>Z</mml:mi> <mml:mo>˘</mml:mo> </mml:mover> </mml:mrow> </mml:math> 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.