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GAN-Based Pencil Drawing Learning System for Art Education on Large-Scale Image Datasets with Learning Analytics

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Title: GAN-Based Pencil Drawing Learning System for Art Education on Large-Scale Image Datasets with Learning Analytics
Language: English
Authors: Jin, Yuxi (ORCID 0000-0002-9334-5205), Li, Ping (ORCID 0000-0002-1503-0240), Wang, Wenxiao (ORCID 0000-0002-0843-5434), Zhang, Suiyun (ORCID 0000-0002-3205-5896), Lin, Di (ORCID 0000-0002-9324-800X), Yin, Chengjiu (ORCID 0000-0003-1492-5250)
Source: Interactive Learning Environments. 2023 31(5):2544-2561.
Availability: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Peer Reviewed: Y
Page Count: 18
Publication Date: 2023
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Art Education, Learning Analytics, Learning Management Systems, Homework, Student Needs, Learning Processes, Teaching Methods, Visual Aids, Freehand Drawing, College Faculty, Undergraduate Students, Foreign Countries, Scoring, Student Attitudes, Instructional Effectiveness
Geographic Terms: Macau
DOI: 10.1080/10494820.2019.1636827
ISSN: 1049-4820
1744-5191
Abstract: We design a generative adversarial network (GAN)-based pencil drawing learning system for art education on large image datasets to help students study how to draw pencil drawings for images and scenes. The system generates a pencil drawing result for a natural image based on GAN. The GAN network is trained on pencil drawing big datasets containing image pairs of natural images and their corresponding pencil drawings. Using the pencil drawing learning system, students can paint pencil drawings whenever they want and for whatever they like by uploading an image of the content they want to draw and getting a pencil drawing example of the uploaded image from the system. With the returned pencil drawing, students will see the pencil drawing effect of natural scenes clearly and realize how to draw the pencil drawing for the natural scene. Besides, with students using the pencil drawing learning system, it will be convenient for teachers assigning homework to students. Teachers can know the learning demands of students by evaluating the hand-in homework and update the content correspondingly. We have conducted two user studies for evaluating the practicality of the system, and the result of the two user studies demonstrated the applicability and practicality of the system.
Abstractor: As Provided
Entry Date: 2023
Accession Number: EJ1394112
Database: ERIC
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  Data: GAN-Based Pencil Drawing Learning System for Art Education on Large-Scale Image Datasets with Learning Analytics
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  Data: <searchLink fieldCode="AR" term="%22Jin%2C+Yuxi%22">Jin, Yuxi</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0002-9334-5205">0000-0002-9334-5205</externalLink>)<br /><searchLink fieldCode="AR" term="%22Li%2C+Ping%22">Li, Ping</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0002-1503-0240">0000-0002-1503-0240</externalLink>)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Wenxiao%22">Wang, Wenxiao</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0002-0843-5434">0000-0002-0843-5434</externalLink>)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Suiyun%22">Zhang, Suiyun</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0002-3205-5896">0000-0002-3205-5896</externalLink>)<br /><searchLink fieldCode="AR" term="%22Lin%2C+Di%22">Lin, Di</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0002-9324-800X">0000-0002-9324-800X</externalLink>)<br /><searchLink fieldCode="AR" term="%22Yin%2C+Chengjiu%22">Yin, Chengjiu</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0003-1492-5250">0000-0003-1492-5250</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22Interactive+Learning+Environments%22"><i>Interactive Learning Environments</i></searchLink>. 2023 31(5):2544-2561.
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  Data: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
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  Data: 18
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  Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink>
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  Label: Descriptors
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  Data: <searchLink fieldCode="DE" term="%22Art+Education%22">Art Education</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Analytics%22">Learning Analytics</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Management+Systems%22">Learning Management Systems</searchLink><br /><searchLink fieldCode="DE" term="%22Homework%22">Homework</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Needs%22">Student Needs</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Processes%22">Learning Processes</searchLink><br /><searchLink fieldCode="DE" term="%22Teaching+Methods%22">Teaching Methods</searchLink><br /><searchLink fieldCode="DE" term="%22Visual+Aids%22">Visual Aids</searchLink><br /><searchLink fieldCode="DE" term="%22Freehand+Drawing%22">Freehand Drawing</searchLink><br /><searchLink fieldCode="DE" term="%22College+Faculty%22">College Faculty</searchLink><br /><searchLink fieldCode="DE" term="%22Undergraduate+Students%22">Undergraduate Students</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22Scoring%22">Scoring</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Attitudes%22">Student Attitudes</searchLink><br /><searchLink fieldCode="DE" term="%22Instructional+Effectiveness%22">Instructional Effectiveness</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Macau%22">Macau</searchLink>
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  Data: 10.1080/10494820.2019.1636827
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  Data: 1049-4820<br />1744-5191
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: We design a generative adversarial network (GAN)-based pencil drawing learning system for art education on large image datasets to help students study how to draw pencil drawings for images and scenes. The system generates a pencil drawing result for a natural image based on GAN. The GAN network is trained on pencil drawing big datasets containing image pairs of natural images and their corresponding pencil drawings. Using the pencil drawing learning system, students can paint pencil drawings whenever they want and for whatever they like by uploading an image of the content they want to draw and getting a pencil drawing example of the uploaded image from the system. With the returned pencil drawing, students will see the pencil drawing effect of natural scenes clearly and realize how to draw the pencil drawing for the natural scene. Besides, with students using the pencil drawing learning system, it will be convenient for teachers assigning homework to students. Teachers can know the learning demands of students by evaluating the hand-in homework and update the content correspondingly. We have conducted two user studies for evaluating the practicality of the system, and the result of the two user studies demonstrated the applicability and practicality of the system.
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  Data: 2023
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  Data: EJ1394112
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        Value: 10.1080/10494820.2019.1636827
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        PageCount: 18
        StartPage: 2544
    Subjects:
      – SubjectFull: Art Education
        Type: general
      – SubjectFull: Learning Analytics
        Type: general
      – SubjectFull: Learning Management Systems
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      – SubjectFull: Freehand Drawing
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      – SubjectFull: Macau
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