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The Use of Deep Learning in Open Learning: A Systematic Review (2019 to 2023)

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Title: The Use of Deep Learning in Open Learning: A Systematic Review (2019 to 2023)
Language: English
Authors: Odiel Estrada-Molina, Juanjo Mena, Alexander López-Padrón
Source: International Review of Research in Open and Distributed Learning. 2024 25(3):371-393.
Availability: Athabasca University Press. 1200, 10011-109 Street, Edmonton, AB T5J 3S8, Canada. Tel: 780-497-3412; Fax: 780-421-3298; e-mail: irrodl@athabascau.ca; Web site: http://www.irrodl.org
Peer Reviewed: Y
Page Count: 24
Publication Date: 2024
Document Type: Journal Articles
Information Analyses
Descriptors: Artificial Intelligence, Intelligent Tutoring Systems, Open Education, Educational Trends, Technology Uses in Education, Journal Articles, Potential Dropouts, Predictor Variables, Automation, Grading, MOOCs, Course Selection (Students), Algorithms, Prediction, Decision Support Systems, Barriers, Individualized Instruction, Bias, Course Content
ISSN: 1492-3831
Abstract: No records of systematic reviews focused on deep learning in open learning have been found, although there has been some focus on other areas of machine learning. Through a systematic review, this study aimed to determine the trends, applied computational techniques, and areas of educational use of deep learning in open learning. The PRISMA protocol was used, and the Web of Science Core Collection (2019-2023) was searched. VOSviewer was used for networking and clustering, and in-depth analysis was employed to answer the research questions. Among the main results, it is worth noting that the scientific literature has focused on the following areas: (a) predicting student dropout, (b) automatic grading of short answers, and (c) recommending MOOC courses. It was concluded that pedagogical challenges have included the effective personalization of content for different learning styles and the need to address possible inherent biases in the datasets (e.g., socio-demographics, traces, competencies, learning objectives) used for training. Regarding deep learning, we observed an increase in the use of pre-trained models, the development of more efficient architectures, and the growing use of interpretability techniques. Technological challenges related to the use of large datasets, intensive computation, interpretability, knowledge transfer, ethics and bias, security, and cost of implementation were also evident.
Abstractor: As Provided
Entry Date: 2024
Accession Number: EJ1441369
Database: ERIC
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  Data: The Use of Deep Learning in Open Learning: A Systematic Review (2019 to 2023)
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  Data: <searchLink fieldCode="SO" term="%22International+Review+of+Research+in+Open+and+Distributed+Learning%22"><i>International Review of Research in Open and Distributed Learning</i></searchLink>. 2024 25(3):371-393.
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  Data: Athabasca University Press. 1200, 10011-109 Street, Edmonton, AB T5J 3S8, Canada. Tel: 780-497-3412; Fax: 780-421-3298; e-mail: irrodl@athabascau.ca; Web site: http://www.irrodl.org
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  Data: <searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Intelligent+Tutoring+Systems%22">Intelligent Tutoring Systems</searchLink><br /><searchLink fieldCode="DE" term="%22Open+Education%22">Open Education</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Trends%22">Educational Trends</searchLink><br /><searchLink fieldCode="DE" term="%22Technology+Uses+in+Education%22">Technology Uses in Education</searchLink><br /><searchLink fieldCode="DE" term="%22Journal+Articles%22">Journal Articles</searchLink><br /><searchLink fieldCode="DE" term="%22Potential+Dropouts%22">Potential Dropouts</searchLink><br /><searchLink fieldCode="DE" term="%22Predictor+Variables%22">Predictor Variables</searchLink><br /><searchLink fieldCode="DE" term="%22Automation%22">Automation</searchLink><br /><searchLink fieldCode="DE" term="%22Grading%22">Grading</searchLink><br /><searchLink fieldCode="DE" term="%22MOOCs%22">MOOCs</searchLink><br /><searchLink fieldCode="DE" term="%22Course+Selection+%28Students%29%22">Course Selection (Students)</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction%22">Prediction</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+Support+Systems%22">Decision Support Systems</searchLink><br /><searchLink fieldCode="DE" term="%22Barriers%22">Barriers</searchLink><br /><searchLink fieldCode="DE" term="%22Individualized+Instruction%22">Individualized Instruction</searchLink><br /><searchLink fieldCode="DE" term="%22Bias%22">Bias</searchLink><br /><searchLink fieldCode="DE" term="%22Course+Content%22">Course Content</searchLink>
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  Data: No records of systematic reviews focused on deep learning in open learning have been found, although there has been some focus on other areas of machine learning. Through a systematic review, this study aimed to determine the trends, applied computational techniques, and areas of educational use of deep learning in open learning. The PRISMA protocol was used, and the Web of Science Core Collection (2019-2023) was searched. VOSviewer was used for networking and clustering, and in-depth analysis was employed to answer the research questions. Among the main results, it is worth noting that the scientific literature has focused on the following areas: (a) predicting student dropout, (b) automatic grading of short answers, and (c) recommending MOOC courses. It was concluded that pedagogical challenges have included the effective personalization of content for different learning styles and the need to address possible inherent biases in the datasets (e.g., socio-demographics, traces, competencies, learning objectives) used for training. Regarding deep learning, we observed an increase in the use of pre-trained models, the development of more efficient architectures, and the growing use of interpretability techniques. Technological challenges related to the use of large datasets, intensive computation, interpretability, knowledge transfer, ethics and bias, security, and cost of implementation were also evident.
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    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 24
        StartPage: 371
    Subjects:
      – SubjectFull: Artificial Intelligence
        Type: general
      – SubjectFull: Intelligent Tutoring Systems
        Type: general
      – SubjectFull: Open Education
        Type: general
      – SubjectFull: Educational Trends
        Type: general
      – SubjectFull: Technology Uses in Education
        Type: general
      – SubjectFull: Journal Articles
        Type: general
      – SubjectFull: Potential Dropouts
        Type: general
      – SubjectFull: Predictor Variables
        Type: general
      – SubjectFull: Automation
        Type: general
      – SubjectFull: Grading
        Type: general
      – SubjectFull: MOOCs
        Type: general
      – SubjectFull: Course Selection (Students)
        Type: general
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Prediction
        Type: general
      – SubjectFull: Decision Support Systems
        Type: general
      – SubjectFull: Barriers
        Type: general
      – SubjectFull: Individualized Instruction
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      – SubjectFull: Bias
        Type: general
      – SubjectFull: Course Content
        Type: general
    Titles:
      – TitleFull: The Use of Deep Learning in Open Learning: A Systematic Review (2019 to 2023)
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            NameFull: Odiel Estrada-Molina
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            NameFull: Juanjo Mena
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            NameFull: Alexander López-Padrón
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              Type: published
              Y: 2024
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              Value: 1492-3831
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            – TitleFull: International Review of Research in Open and Distributed Learning
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