A data-driven emotion model for English learners based on machine learning

Learning confusion is a common emotion among learners. With the aid of machine learning, this paper develops a data-driven emotion model that automatically recognizes learning confusion in facial expression images. The data on learning behaviors and learning confusion of multiple subjects were colle...

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Main Authors: Zheng, Zhao, Kew, Si Na
Format: Article
Language:English
Published: International Association of Online Engineering 2021
Subjects:
Online Access:http://eprints.utm.my/94034/1/KewSiNa2021_ADataDrivenEmotionModel.pdf
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author Zheng, Zhao
Kew, Si Na
author_facet Zheng, Zhao
Kew, Si Na
author_sort Zheng, Zhao
collection ePrints
description Learning confusion is a common emotion among learners. With the aid of machine learning, this paper develops a data-driven emotion model that automatically recognizes learning confusion in facial expression images. The data on learning behaviors and learning confusion of multiple subjects were collected through an online English evaluation experiment, and imported to the proposed model to derive the relationship between learning confusion and academic performance, which is measured by the correctness of the students’ answers to the test questions. The experimental results show that the students with learning confusion had relatively low correct rate of answering test questions. The research findings reveal the relationship between learning confusion and academic performance, laying the basis for predicting the academic performance of English learners through machine learning.
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spelling utm.eprints-940342022-02-28T13:31:21Z http://eprints.utm.my/94034/ A data-driven emotion model for English learners based on machine learning Zheng, Zhao Kew, Si Na PE English Learning confusion is a common emotion among learners. With the aid of machine learning, this paper develops a data-driven emotion model that automatically recognizes learning confusion in facial expression images. The data on learning behaviors and learning confusion of multiple subjects were collected through an online English evaluation experiment, and imported to the proposed model to derive the relationship between learning confusion and academic performance, which is measured by the correctness of the students’ answers to the test questions. The experimental results show that the students with learning confusion had relatively low correct rate of answering test questions. The research findings reveal the relationship between learning confusion and academic performance, laying the basis for predicting the academic performance of English learners through machine learning. International Association of Online Engineering 2021-06 Article PeerReviewed application/pdf en http://eprints.utm.my/94034/1/KewSiNa2021_ADataDrivenEmotionModel.pdf Zheng, Zhao and Kew, Si Na (2021) A data-driven emotion model for English learners based on machine learning. International Journal of Emerging Technologies in Learning, 16 (8). pp. 34-46. ISSN 1868-8799 http://dx.doi.org/10.3991/ijet.v16i08.22127 DOI:10.3991/ijet.v16i08.22127
spellingShingle PE English
Zheng, Zhao
Kew, Si Na
A data-driven emotion model for English learners based on machine learning
title A data-driven emotion model for English learners based on machine learning
title_full A data-driven emotion model for English learners based on machine learning
title_fullStr A data-driven emotion model for English learners based on machine learning
title_full_unstemmed A data-driven emotion model for English learners based on machine learning
title_short A data-driven emotion model for English learners based on machine learning
title_sort data driven emotion model for english learners based on machine learning
topic PE English
url http://eprints.utm.my/94034/1/KewSiNa2021_ADataDrivenEmotionModel.pdf
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AT kewsina adatadrivenemotionmodelforenglishlearnersbasedonmachinelearning
AT zhengzhao datadrivenemotionmodelforenglishlearnersbasedonmachinelearning
AT kewsina datadrivenemotionmodelforenglishlearnersbasedonmachinelearning