Comprehensive Evaluation of Teaching Quality in Textile University Courses Based on Fuzzy TOPSIS and Factor Analysis
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Abstract
Rapid advances in intelligent textiles, wearable electronics, and electromagnetic-enabled sensing technologies have accelerated the interdisciplinary integration of textile engineering with antenna, propagation, and electronic system education, creating increasing demands for objective and adaptive teaching quality assessment. To address the limitations of conventional evaluation approaches, including indicator redundancy, static weighting, and insufficient representation of curriculum dynamics, this study proposes a comprehensive teaching quality evaluation framework that integrates factor analysis with fuzzy TOPSIS. Factor analysis is employed to reduce dimensionality and identify four latent dimensions— industry–education integration, curriculum update timeliness, resource capacity, and student innovation ability—while their variance contributions are directly incorporated into a data-driven weighting strategy for fuzzy TOPSIS ranking. Experimental validation on 28 textile university courses demonstrates that the proposed model achieves an expert recognition accuracy of 85.7%, a Spearman rank correlation coefficient of 0.74 with student innovation evaluations, and an average ranking consistency of 86.1% under robustness testing with incomplete data. By providing dynamic and interpretable assessment results, the framework supports evidence-based curriculum optimization and resource allocation. The proposed methodology is particularly applicable to interdisciplinary engineering programs that require continuous adaptation to emerging technologies, offering practical reference for quality assurance in textile education and related courses involving wearable electronics and electromagnetic engineering.
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