Triangular Offnorm with Multi-Criteria Decision-Making Method for College Political and Ideological Instruction on Intelligent Learning Platforms

Traditional teaching has been transformed by the introduction of intelligent learning platforms into higher education, especially in political and ideological education. By determining important evaluative criteria and evaluating different instructional options in smart learning settings, this study seeks to investigate the efficacy and quality of such training. The study creates a balanced set of indicators in line with learner outcomes, technology engagement, and pedagogical goals using a systematic multicriteria decision-making (MCDM) framework. For political and ideological courses offered via intelligent platforms, the objective is to offer practical insights that can improve policy implementation and instructional design. We use the RATGOS methodology to rank alternatives. We use eight criteria and six alternatives in this paper. We use the Triangular OffNorm to deal with different decision values in the decision-making process. The ranks of alternatives are tested by sensitivity analysis. The results show the ranks of alternatives are stable in different cases.