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An emotion analysis in learning environment based on theme-specified drawing by convolutional neural network

Emotion in the learning process can directly influence the learner's attention, memory, and cognitive activities. Several literatures indicate that hand-drawn painting could reflect the learner's emotional status. But, such an evaluation of emotional status, manually conducted by the psych...

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Autores principales: He, Tiancheng, Li, Chao, Wang, Jiayang, Wang, Minjun, Wang, Zhenghao, Jiao, Changyong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9669759/
https://www.ncbi.nlm.nih.gov/pubmed/36408050
http://dx.doi.org/10.3389/fpubh.2022.958870
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author He, Tiancheng
Li, Chao
Wang, Jiayang
Wang, Minjun
Wang, Zhenghao
Jiao, Changyong
author_facet He, Tiancheng
Li, Chao
Wang, Jiayang
Wang, Minjun
Wang, Zhenghao
Jiao, Changyong
author_sort He, Tiancheng
collection PubMed
description Emotion in the learning process can directly influence the learner's attention, memory, and cognitive activities. Several literatures indicate that hand-drawn painting could reflect the learner's emotional status. But, such an evaluation of emotional status, manually conducted by the psychologist, is usually subjective and inefficient for clinical practice. To address the issues of subjectivity and inefficiency in the painting based emotional analysis, we conducted an exploration of a painting based emotional analysis in learning environment by using convolutional neural network model. A painting image of 100 × 100 pixels was used as input for the model. The instant emotional statue of the learner was collected by filling out a questionnaire and was reviewed by a psychologist and then used as the label for training the convolutional neural network model. With the completion of convolutional, full-connected, and classification operations, the features of the painting image were learned from the underlying pixel matrix to the high-level semantic feature mapping. Then the emotional classification of the painting image could be made to reflect the learner's emotional status. Finally, the classification result by the model was compared with the result manually conducted by a psychologist to validate the model accuracy. We conducted an experiment in a university at Hangzhou, and 2,103 learners joined in the experiment. The learner was required to first fill out a questionnaire reporting emotional status in the learning process, and then to complete a theme-specified painting. Two thousand valid paintings were received and divided into training dataset (1,600) and test dataset (400). The experimental result indicated that the model achieved the accuracy of 72.1%, which confirmed the effectiveness of the model for emotional analysis.
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spelling pubmed-96697592022-11-18 An emotion analysis in learning environment based on theme-specified drawing by convolutional neural network He, Tiancheng Li, Chao Wang, Jiayang Wang, Minjun Wang, Zhenghao Jiao, Changyong Front Public Health Public Health Emotion in the learning process can directly influence the learner's attention, memory, and cognitive activities. Several literatures indicate that hand-drawn painting could reflect the learner's emotional status. But, such an evaluation of emotional status, manually conducted by the psychologist, is usually subjective and inefficient for clinical practice. To address the issues of subjectivity and inefficiency in the painting based emotional analysis, we conducted an exploration of a painting based emotional analysis in learning environment by using convolutional neural network model. A painting image of 100 × 100 pixels was used as input for the model. The instant emotional statue of the learner was collected by filling out a questionnaire and was reviewed by a psychologist and then used as the label for training the convolutional neural network model. With the completion of convolutional, full-connected, and classification operations, the features of the painting image were learned from the underlying pixel matrix to the high-level semantic feature mapping. Then the emotional classification of the painting image could be made to reflect the learner's emotional status. Finally, the classification result by the model was compared with the result manually conducted by a psychologist to validate the model accuracy. We conducted an experiment in a university at Hangzhou, and 2,103 learners joined in the experiment. The learner was required to first fill out a questionnaire reporting emotional status in the learning process, and then to complete a theme-specified painting. Two thousand valid paintings were received and divided into training dataset (1,600) and test dataset (400). The experimental result indicated that the model achieved the accuracy of 72.1%, which confirmed the effectiveness of the model for emotional analysis. Frontiers Media S.A. 2022-11-03 /pmc/articles/PMC9669759/ /pubmed/36408050 http://dx.doi.org/10.3389/fpubh.2022.958870 Text en Copyright © 2022 He, Li, Wang, Wang, Wang and Jiao. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Public Health
He, Tiancheng
Li, Chao
Wang, Jiayang
Wang, Minjun
Wang, Zhenghao
Jiao, Changyong
An emotion analysis in learning environment based on theme-specified drawing by convolutional neural network
title An emotion analysis in learning environment based on theme-specified drawing by convolutional neural network
title_full An emotion analysis in learning environment based on theme-specified drawing by convolutional neural network
title_fullStr An emotion analysis in learning environment based on theme-specified drawing by convolutional neural network
title_full_unstemmed An emotion analysis in learning environment based on theme-specified drawing by convolutional neural network
title_short An emotion analysis in learning environment based on theme-specified drawing by convolutional neural network
title_sort emotion analysis in learning environment based on theme-specified drawing by convolutional neural network
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9669759/
https://www.ncbi.nlm.nih.gov/pubmed/36408050
http://dx.doi.org/10.3389/fpubh.2022.958870
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