A University Teachers’ Teaching Performance Evaluation Method Based on Type-II Fuzzy Sets

This paper proposes a university teachers’ teaching performance evaluation method based on type-II fuzzy sets (T2 FSs), which solves the problems of fuzziness, complexity and uncertainty in teaching performance evaluation. Firstly, the evaluation indicator system is constructed from the aspects of t...

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Bibliographic Details
Published in:Mathematics (Basel) 2021-09, Vol.9 (17), p.2126
Main Authors: Sun, Xixia, Cai, Chao, Pan, Su, Bao, Nan, Liu, Ning
Format: Article
Language:English
Subjects:
Colleges & universities
Complexity
Computation
computing with words
Data envelopment analysis
Decision making
Fuzzy sets
linguistic weighted average
Linguistics
Mathematics
Performance evaluation
Principal components analysis
Students
Study skills
Teachers
Teaching
Teaching methods
teaching performance evaluation
type-II fuzzy sets
Uncertainty
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Summary:This paper proposes a university teachers’ teaching performance evaluation method based on type-II fuzzy sets (T2 FSs), which solves the problems of fuzziness, complexity and uncertainty in teaching performance evaluation. Firstly, the evaluation indicator system is constructed from the aspects of teaching attitude, teaching contents, teaching professionalism, teaching methods and teaching effects. Then, T2 FSs theory and the perceptual computing method are introduced to model subjective judgments and capture uncertainties, effectively handling higher levels of uncertainty in the evaluation process. Furthermore, the linguistic weighted average operator is applied as the computing with words engine to aggregate scores and weights of indicators, which effectively integrates the uncertain information in the input data into the final evaluation conclusion and guarantees the accuracy of the evaluation results. Finally, the effectiveness of the method of this study is evaluated by simulation experiments. The computational results demonstrate that it can capture more uncertain and complex information, and is more accurate and reliable than the type-I fuzzy sets method.
ISSN:2227-7390
2227-7390
DOI:10.3390/math9172126