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Enhanced teaching team evaluation system for vocational colleges

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Titel: Enhanced teaching team evaluation system for vocational colleges
Autoren: Qiming Tian, Wanle Chi, Dafeng Gong, Yuwen Shi, Lili Shi, Huiling Chen, Sudan Yu
Quelle: Scientific Reports, Vol 15, Iss 1, Pp 1-23 (2025)
Verlagsinformationen: Nature Portfolio, 2025.
Publikationsjahr: 2025
Bestand: LCC:Medicine
LCC:Science
Schlagwörter: Artemisinin optimization, Global optimization, Comprehensive learning, Dispersed foraging strategy, Teaching quality evaluation, Medicine, Science
Beschreibung: Abstract Constructing an evaluation system for teaching teams is very important to promote the reform of teaching evaluation in higher vocational colleges. However, there is no existing artificial intelligence−based evaluation system for teaching teams that can accurately predict the teaching evaluations in higher vocational colleges. The artemisinin optimization (AO) algorithm draws its core inspiration from the treatment process of malaria with artemisinin. It simulates the human body as a “closed space”, with the malaria parasites considered as hidden "solutions”. The algorithm mimics the ongoing process of artemisinin medicine eradicating the malaria parasites within the body. But AO tend to fall into local optima without finding the global optimum. Therefore, we propose an intelligent AO version to predict teaching team. AO has been enhanced by adding the comprehensive learning mechanism and the dispersed foraging mechanism, that is CLDMAO. The CLDMAO is benchmarked against various cutting-edge algorithms using the CEC 2017 test functions to validate its effectiveness and stability. The results indicate that CLDMAO ranks first in the majority of the test functions. What’s more, the binary CLDMAO-KNN model performs excellently for the evaluation system for teaching teams in terms of error rate, fitness, and the number of features. Through this model, factors for establishing an evaluation system can be identified within the dataset, enabling the construction of a complete evaluation system and further analysis of the potential correlations between various factors.
Publikationsart: article
Dateibeschreibung: electronic resource
Sprache: English
ISSN: 2045-2322
Relation: https://doaj.org/toc/2045-2322
DOI: 10.1038/s41598-025-22932-0
Zugangs-URL: https://doaj.org/article/5a77058640d24cc6af4c7de647e268ff
Dokumentencode: edsdoj.5a77058640d24cc6af4c7de647e268ff
Datenbank: Directory of Open Access Journals
Beschreibung
Abstract:Abstract Constructing an evaluation system for teaching teams is very important to promote the reform of teaching evaluation in higher vocational colleges. However, there is no existing artificial intelligence−based evaluation system for teaching teams that can accurately predict the teaching evaluations in higher vocational colleges. The artemisinin optimization (AO) algorithm draws its core inspiration from the treatment process of malaria with artemisinin. It simulates the human body as a “closed space”, with the malaria parasites considered as hidden "solutions”. The algorithm mimics the ongoing process of artemisinin medicine eradicating the malaria parasites within the body. But AO tend to fall into local optima without finding the global optimum. Therefore, we propose an intelligent AO version to predict teaching team. AO has been enhanced by adding the comprehensive learning mechanism and the dispersed foraging mechanism, that is CLDMAO. The CLDMAO is benchmarked against various cutting-edge algorithms using the CEC 2017 test functions to validate its effectiveness and stability. The results indicate that CLDMAO ranks first in the majority of the test functions. What’s more, the binary CLDMAO-KNN model performs excellently for the evaluation system for teaching teams in terms of error rate, fitness, and the number of features. Through this model, factors for establishing an evaluation system can be identified within the dataset, enabling the construction of a complete evaluation system and further analysis of the potential correlations between various factors.
ISSN:20452322
DOI:10.1038/s41598-025-22932-0