Assessing Risk in Teaching: A Fuzzy TOPSIS Approach for Higher Education

Abstract

This study addresses the critical challenge of evaluating and mitigating risks associated with various teaching modalities in higher education, particularly as institutions increasingly adopt Online and Hybrid Learning environments. Utilizing the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (Fuzzy TOPSIS), a robust multi-criteria decision-making (MCDM) method, the study systematically assesses and ranks teaching modalities based on five key risk dimensions: technological, operational, pedagogical, compliance, and reputational. Expert evaluations were converted into fuzzy numbers, and distances from the Fuzzy Positive Ideal Solution (FPIS) and Fuzzy Negative Ideal Solution (FNIS) were computed to determine each modality’s relative performance. The findings reveal that Online Learning is the most effective modality for risk mitigation, particularly excelling in technological, operational, and reputational domains. Hybrid Learning demonstrated balanced performance, ranking second overall, while Face-to-Face instruction was most effective in managing pedagogical and compliance-related risks. These results offer data-driven insights that can guide higher education institutions in optimizing teaching evaluation strategies, improving institutional resilience, and enhancing quality assurance processes. By adopting a structured, evidence-based approach, institutions can better align their instructional delivery with risk management priorities. The study highlights the benefits of integrating Fuzzy TOPSIS in educational decision-making, providing a scalable and transparent framework for continuous improvement