Online Teaching Quality Evaluation of Business Statistics Course Utilizing Fermatean Fuzzy Analytical Hierarchy Process with Aggregation Operator
Due to the full-scale outbreak of COVID-19, many universities have adopted the way of online teaching to ensure the orderly development of teaching plans and teaching contents. However, whether online and offline teaching can develop homogeneously and how to ensure the teaching effect is a major cha...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Systems |
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| Online Access: | https://www.mdpi.com/2079-8954/10/3/63 |
| _version_ | 1827656333197836288 |
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| author | Shouzhen Zeng Yan Pan Huanhuan Jin |
| author_facet | Shouzhen Zeng Yan Pan Huanhuan Jin |
| author_sort | Shouzhen Zeng |
| collection | DOAJ |
| description | Due to the full-scale outbreak of COVID-19, many universities have adopted the way of online teaching to ensure the orderly development of teaching plans and teaching contents. However, whether online and offline teaching can develop homogeneously and how to ensure the teaching effect is a major challenge for colleges and universities. Therefore, it is urgent to construct a reasonable index system and evaluation approach for the quality of network teaching. Combined with the influencing factors and characteristics of online teaching, this study first puts forward a multi-index evaluation index system and then proposes a novel evaluation method for online teaching based on the analytical hierarchy process (AHP) and Dombi weighted partitioned Muirhead Mean (<i>PMM</i>) operator under Fermatean fuzzy (FF) environment. This presented method not only adapts to changeable evaluation information but also handles the elusive interrelationships among indexes, realizing the flexibility and comprehensiveness both in form and in the polyaddition process. The applicability and feasibility of this presented method are then discussed through the practical online teaching quality evaluation of a business statistics course case, and a group of tentative about the sensitivity analysis and comparative analysis further demonstrates the effectiveness and flexibility of the proposed method. |
| first_indexed | 2024-03-09T22:21:07Z |
| format | Article |
| id | doaj.art-5c501ff3242f426b99a8f0ee10d2bb1a |
| institution | Directory Open Access Journal |
| issn | 2079-8954 |
| language | English |
| last_indexed | 2024-03-09T22:21:07Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Systems |
| spelling | doaj.art-5c501ff3242f426b99a8f0ee10d2bb1a2023-11-23T19:14:03ZengMDPI AGSystems2079-89542022-05-011036310.3390/systems10030063Online Teaching Quality Evaluation of Business Statistics Course Utilizing Fermatean Fuzzy Analytical Hierarchy Process with Aggregation OperatorShouzhen Zeng0Yan Pan1Huanhuan Jin2School of Business, Ningbo University, Ningbo 315211, ChinaSchool of Business, Ningbo University, Ningbo 315211, ChinaHangzhou College of Commerce, Zhejiang Gongshang University, Hangzhou 310012, ChinaDue to the full-scale outbreak of COVID-19, many universities have adopted the way of online teaching to ensure the orderly development of teaching plans and teaching contents. However, whether online and offline teaching can develop homogeneously and how to ensure the teaching effect is a major challenge for colleges and universities. Therefore, it is urgent to construct a reasonable index system and evaluation approach for the quality of network teaching. Combined with the influencing factors and characteristics of online teaching, this study first puts forward a multi-index evaluation index system and then proposes a novel evaluation method for online teaching based on the analytical hierarchy process (AHP) and Dombi weighted partitioned Muirhead Mean (<i>PMM</i>) operator under Fermatean fuzzy (FF) environment. This presented method not only adapts to changeable evaluation information but also handles the elusive interrelationships among indexes, realizing the flexibility and comprehensiveness both in form and in the polyaddition process. The applicability and feasibility of this presented method are then discussed through the practical online teaching quality evaluation of a business statistics course case, and a group of tentative about the sensitivity analysis and comparative analysis further demonstrates the effectiveness and flexibility of the proposed method.https://www.mdpi.com/2079-8954/10/3/63Fermatean fuzzy setDombi operationpartitioned Muirhead meanonline teaching quality evaluationmulti-attribute decision makingbusiness statistics |
| spellingShingle | Shouzhen Zeng Yan Pan Huanhuan Jin Online Teaching Quality Evaluation of Business Statistics Course Utilizing Fermatean Fuzzy Analytical Hierarchy Process with Aggregation Operator Systems Fermatean fuzzy set Dombi operation partitioned Muirhead mean online teaching quality evaluation multi-attribute decision making business statistics |
| title | Online Teaching Quality Evaluation of Business Statistics Course Utilizing Fermatean Fuzzy Analytical Hierarchy Process with Aggregation Operator |
| title_full | Online Teaching Quality Evaluation of Business Statistics Course Utilizing Fermatean Fuzzy Analytical Hierarchy Process with Aggregation Operator |
| title_fullStr | Online Teaching Quality Evaluation of Business Statistics Course Utilizing Fermatean Fuzzy Analytical Hierarchy Process with Aggregation Operator |
| title_full_unstemmed | Online Teaching Quality Evaluation of Business Statistics Course Utilizing Fermatean Fuzzy Analytical Hierarchy Process with Aggregation Operator |
| title_short | Online Teaching Quality Evaluation of Business Statistics Course Utilizing Fermatean Fuzzy Analytical Hierarchy Process with Aggregation Operator |
| title_sort | online teaching quality evaluation of business statistics course utilizing fermatean fuzzy analytical hierarchy process with aggregation operator |
| topic | Fermatean fuzzy set Dombi operation partitioned Muirhead mean online teaching quality evaluation multi-attribute decision making business statistics |
| url | https://www.mdpi.com/2079-8954/10/3/63 |
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