Making decisions based on multiple criteria in developing a league season teaching strategy for understanding playing functionalities.

In order to grasp the playing functions that utilize criteria for decision-making (DM) approaches, this paper offers a League season learning selection issue. The decision criteria to assess several game-based learning options are outlined, and a real-world choice dilemma is provided. Finding the most effective League season learning among playing functions is the goal. The needs for a League season of learning, such as Cognitive Methodologies, Gameplay Characteristics, Psychological Reactions, Accessibility, Users, as well as its environmental and societal impacts, are taken into consideration, leading to the identification of six primary criteria and 22 sub-criteria. Analytical Hierarchy Process (AHP) is used to calculate the criterion weights. The Preference Ranking Organization Method for Enrichment Evaluations and Vlse Kriterijumska Optimizacija I Kompromisno Resenje techniques are used to rank and choose between four possibilities. The outcomes of the Preference Ranking Organization Method for Enrichment Evaluations and Vlse Kriterijumska Optimizacija I Kompromisno Resenje procedures are also contrasted with those of the Complex Proportional Assessment, Multi-Atributive Ideal-Real Comparative Analysis and Multi-Attributive Border Approximation Area Comparison approaches. The results of the rankings of the alternatives produced by each approach all recommend the same choice as the best. Thus, it can be said that Preference Ranking Organization Method for Enrichment Evaluations, Vlse Kriterijumska Optimizacija I Kompromisno Resenje, Complex Proportional Assessment, Multi-Atributive Ideal-Real Comparative Analysis and Multi-Attributive Border Approximation Area Comparison approaches may be successfully employed for selection issues in game-based learning, as well as generally for other forms of multi-criteria decision problems with finite number of choices. [ABSTRACT FROM AUTHOR]