An interactive data-driven multiple-attribute decision-making technique via interval-valued intuitionistic fuzzy sets for teaching quality evaluation in higher education
Affiliations: Department of Anti-Drug and Public Security, Criminal Investigation Police University of China, Shenyang, Liaoning, China | E-mail: [email protected]
Correspondence:
[*]
Corresponding author: Department of Anti-Drug and Public Security, Criminal Investigation Police University of China, Shenyang, Liaoning, China. E-mail: [email protected].
Abstract: Improving the quality of higher education teaching is a systematic project. The improvement and formulation of relevant laws, regulations, and measures at the macro level are the minimum and specific requirements for the overall private universities, and are the basic guarantee for controlling the healthy and orderly development of universities. At the micro level, school management needs to focus on two aspects: leadership level construction and teacher level construction. Only by scientifically controlling the above issues and comprehensively considering them can the persistent problem of low teaching quality be fundamentally and gradually solved. In short, the construction of the quality assurance and evaluation system for higher education teaching in China is still in the initial stage of development. Therefore, universities should start from the guarantee and evaluation system to promote the construction of teaching process monitoring and evaluation systems, and improve the level of education and teaching quality on the basis of highlighting higher education teaching reform and research. The teaching quality evaluation of higher education in the era of artificial intelligence is a MADM. In this study, in light with interval-valued intuitionistic fuzzy Hamacher interactive hybrid weighted geometric (IVIFHIHWG) technique and induced OWG (I-OWG) technique, the induced IVIFHIHWG (I-IVIFHIHWG) technique is administrated. Then, the I-IVIFHIHWG technique is exploited to manage the MADM under IVIFSs. Finally, the numerical example for teaching quality evaluation of higher education in the era of artificial intelligence is exploited to verify the I-IVIFHIHWG technique. Thus, the main research contributions are administrated: (1) the I-IVIFHIHWG technique is administrated in line with the IVIFHIHWG and I-OWG technique; (2) the I-IVIFHIHWG technique is exploited to manage the MADM under IVIFSs; (3) the numerical example for teaching quality evaluation of higher education in the era of artificial intelligence and some comparative studies were exploited to verify the I-IVIFHIHWG technique.
