Cargando…

Optimizing decision trees for English Teaching Quality Evaluation (ETQE) using Artificial Bee Colony (ABC) optimization

Changes in educational systems and English teaching strategies have increased the need for automatic methods for English Teaching Quality Evaluation (ETQE). A practical model for ETQE applies in different fields, determines the most relevant factors in teaching quality (TQ), and has optimal performa...

Descripción completa

Detalles Bibliográficos
Autor principal: Cui, Yingying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10469990/
https://www.ncbi.nlm.nih.gov/pubmed/37664698
http://dx.doi.org/10.1016/j.heliyon.2023.e19274
_version_ 1785099571128434688
author Cui, Yingying
author_facet Cui, Yingying
author_sort Cui, Yingying
collection PubMed
description Changes in educational systems and English teaching strategies have increased the need for automatic methods for English Teaching Quality Evaluation (ETQE). A practical model for ETQE applies in different fields, determines the most relevant factors in teaching quality (TQ), and has optimal performance in different conditions. This paper presents a new method based on Artificial Intelligence (AI) and meta-heuristic algorithms to solve the ETQE problem. The proposed method performs the prediction process in two phases: “determination of related indicators” and “quality prediction”. During the first phase, after introducing a set of 24 candidate indicators, an optimal subset of them having maximum correlation with ETQE and minimum redundancy are selected using Artificial Bee Colony (ABC) algorithm. In the second phase of the proposed method, a Classification and Regression Tree (CART) model optimized by ABC are applied to predict ETQ based on the indicators determined in the first phase. In this learning model, split points of decision nodes are determined by ABC in a way that the prediction accuracy would be maximized. The performance of the proposed method has been evaluated in two different teaching environments. The performance of the proposed method has been evaluated in two different teaching environments. The studied teaching environments are face-to-face (FF) and online classes that were held for middle school and university students, respectively. Based on the obtained results, the proposed method can predict the ETQ with an accuracy of more than 98.99% in both tested scenarios, which results in an increase of at least 1.11% compared to the previous methods. The efficiency of the proposed model in both studied scenarios prove the generality of this method to be used in real-world applications.
format Online
Article
Text
id pubmed-10469990
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Elsevier
record_format MEDLINE/PubMed
spelling pubmed-104699902023-09-01 Optimizing decision trees for English Teaching Quality Evaluation (ETQE) using Artificial Bee Colony (ABC) optimization Cui, Yingying Heliyon Research Article Changes in educational systems and English teaching strategies have increased the need for automatic methods for English Teaching Quality Evaluation (ETQE). A practical model for ETQE applies in different fields, determines the most relevant factors in teaching quality (TQ), and has optimal performance in different conditions. This paper presents a new method based on Artificial Intelligence (AI) and meta-heuristic algorithms to solve the ETQE problem. The proposed method performs the prediction process in two phases: “determination of related indicators” and “quality prediction”. During the first phase, after introducing a set of 24 candidate indicators, an optimal subset of them having maximum correlation with ETQE and minimum redundancy are selected using Artificial Bee Colony (ABC) algorithm. In the second phase of the proposed method, a Classification and Regression Tree (CART) model optimized by ABC are applied to predict ETQ based on the indicators determined in the first phase. In this learning model, split points of decision nodes are determined by ABC in a way that the prediction accuracy would be maximized. The performance of the proposed method has been evaluated in two different teaching environments. The performance of the proposed method has been evaluated in two different teaching environments. The studied teaching environments are face-to-face (FF) and online classes that were held for middle school and university students, respectively. Based on the obtained results, the proposed method can predict the ETQ with an accuracy of more than 98.99% in both tested scenarios, which results in an increase of at least 1.11% compared to the previous methods. The efficiency of the proposed model in both studied scenarios prove the generality of this method to be used in real-world applications. Elsevier 2023-08-18 /pmc/articles/PMC10469990/ /pubmed/37664698 http://dx.doi.org/10.1016/j.heliyon.2023.e19274 Text en © 2023 The Author. Published by Elsevier Ltd. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research Article
Cui, Yingying
Optimizing decision trees for English Teaching Quality Evaluation (ETQE) using Artificial Bee Colony (ABC) optimization
title Optimizing decision trees for English Teaching Quality Evaluation (ETQE) using Artificial Bee Colony (ABC) optimization
title_full Optimizing decision trees for English Teaching Quality Evaluation (ETQE) using Artificial Bee Colony (ABC) optimization
title_fullStr Optimizing decision trees for English Teaching Quality Evaluation (ETQE) using Artificial Bee Colony (ABC) optimization
title_full_unstemmed Optimizing decision trees for English Teaching Quality Evaluation (ETQE) using Artificial Bee Colony (ABC) optimization
title_short Optimizing decision trees for English Teaching Quality Evaluation (ETQE) using Artificial Bee Colony (ABC) optimization
title_sort optimizing decision trees for english teaching quality evaluation (etqe) using artificial bee colony (abc) optimization
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10469990/
https://www.ncbi.nlm.nih.gov/pubmed/37664698
http://dx.doi.org/10.1016/j.heliyon.2023.e19274
work_keys_str_mv AT cuiyingying optimizingdecisiontreesforenglishteachingqualityevaluationetqeusingartificialbeecolonyabcoptimization