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English Education Tutoring Teaching System Based on MOOC

In the process of continuous reform and development of education and teaching, some traditional teaching modes are gradually eliminated, whereas new teaching modes are gradually recognized by teachers and students. These modes are widely used in education and teaching due to their overwhelming chara...

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Detalles Bibliográficos
Autor principal: Mi, Wang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9167017/
https://www.ncbi.nlm.nih.gov/pubmed/35669648
http://dx.doi.org/10.1155/2022/1563352
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author Mi, Wang
author_facet Mi, Wang
author_sort Mi, Wang
collection PubMed
description In the process of continuous reform and development of education and teaching, some traditional teaching modes are gradually eliminated, whereas new teaching modes are gradually recognized by teachers and students. These modes are widely used in education and teaching due to their overwhelming characteristics. Among these emerging teaching modes, massive open online course (MOOC) is a relatively advanced teaching mode with a better application effect. Quickly and accurately detecting the cheating behavior of MOOC learners is of great significance for maintaining the development of the MOOC platform and English education counseling. This paper studies a deep-learning-based hybrid model for MOOC cheating detection. The model greatly improves the detection performance of a single model by integrating CNN, a bidirectional gated recurrent unit, and an attention mechanism. The proposed model selects the English learning behavior data of a MOOC platform to verify the performance of the algorithm. Simulation results show that the proposed scheme can greatly help MOOC-based English education tutoring.
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spelling pubmed-91670172022-06-05 English Education Tutoring Teaching System Based on MOOC Mi, Wang Comput Intell Neurosci Research Article In the process of continuous reform and development of education and teaching, some traditional teaching modes are gradually eliminated, whereas new teaching modes are gradually recognized by teachers and students. These modes are widely used in education and teaching due to their overwhelming characteristics. Among these emerging teaching modes, massive open online course (MOOC) is a relatively advanced teaching mode with a better application effect. Quickly and accurately detecting the cheating behavior of MOOC learners is of great significance for maintaining the development of the MOOC platform and English education counseling. This paper studies a deep-learning-based hybrid model for MOOC cheating detection. The model greatly improves the detection performance of a single model by integrating CNN, a bidirectional gated recurrent unit, and an attention mechanism. The proposed model selects the English learning behavior data of a MOOC platform to verify the performance of the algorithm. Simulation results show that the proposed scheme can greatly help MOOC-based English education tutoring. Hindawi 2022-05-27 /pmc/articles/PMC9167017/ /pubmed/35669648 http://dx.doi.org/10.1155/2022/1563352 Text en Copyright © 2022 Wang Mi. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Mi, Wang
English Education Tutoring Teaching System Based on MOOC
title English Education Tutoring Teaching System Based on MOOC
title_full English Education Tutoring Teaching System Based on MOOC
title_fullStr English Education Tutoring Teaching System Based on MOOC
title_full_unstemmed English Education Tutoring Teaching System Based on MOOC
title_short English Education Tutoring Teaching System Based on MOOC
title_sort english education tutoring teaching system based on mooc
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9167017/
https://www.ncbi.nlm.nih.gov/pubmed/35669648
http://dx.doi.org/10.1155/2022/1563352
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