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Computational Thinking and Deep Learning on Science Education Framework: A Systematic Review

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Title: Computational Thinking and Deep Learning on Science Education Framework: A Systematic Review
Language: English
Authors: Zainur Rasyid Ridlo (ORCID 0000-0002-5188-6273), Silvi Putri Ayu Ningsih (ORCID 0009-0004-7748-0388), Azza Liarista Anggraini (ORCID 0000-0001-9019-386X)
Source: Science Education International. 2025 36(4):480-489.
Availability: International Council of Associations for Science Education. Dokuz Eylul University Faculty of Education, Buca, Izmir 35150, Turkey. Tel: +90-532-4267927; Fax: +90-232-4204895; Web site: http://www.icaseonline.net/seiweb/
Peer Reviewed: Y
Page Count: 10
Publication Date: 2025
Intended Audience: Policymakers; Teachers
Document Type: Journal Articles
Information Analyses
Descriptors: Computation, Thinking Skills, Science Education, Learning Processes, Information Retrieval, Models, Correlation, Programming, Computer Software
ISSN: 1450-104X
2077-2327
Abstract: This research investigates the implementation and challenges of incorporating computational thinking (CT) into science education. This research aims to develop a comprehensive framework to improve computational thinking skills in science education through the implementation of a deep learning curriculum, also integrated with computer programming, which leads to enhancing analytical and problem-solving skills for students to tackle real-world problems. This research used a systematic literature review with the PRISMA technique. Data sources used in this research are indexed by the Crossref database and then analyzed using the VOSviewer program. By examining 133 articles included in total, the research identifies key factors that affect effective teaching in science classrooms to enhance computational thinking. Through a series of case studies and empirical analysis, the study highlights obstacles faced in this educational approach. Findings suggest that while deep learning and computer programming integrated into science classrooms can influence the improvement of computational thinking skills and students' understanding of scientific concepts and their application, challenges such as limited resources are addressed. The proposed framework offers practical strategies for policymakers and educators, especially science educators, in designing learning to overcome these challenges, aiming to prepare students for a technology-driven future better.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1498283
Database: ERIC
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  Data: Computational Thinking and Deep Learning on Science Education Framework: A Systematic Review
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  Data: <searchLink fieldCode="AR" term="%22Zainur+Rasyid+Ridlo%22">Zainur Rasyid Ridlo</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-5188-6273">0000-0002-5188-6273</externalLink>)<br /><searchLink fieldCode="AR" term="%22Silvi+Putri+Ayu+Ningsih%22">Silvi Putri Ayu Ningsih</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0004-7748-0388">0009-0004-7748-0388</externalLink>)<br /><searchLink fieldCode="AR" term="%22Azza+Liarista+Anggraini%22">Azza Liarista Anggraini</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-9019-386X">0000-0001-9019-386X</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22Science+Education+International%22"><i>Science Education International</i></searchLink>. 2025 36(4):480-489.
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  Data: International Council of Associations for Science Education. Dokuz Eylul University Faculty of Education, Buca, Izmir 35150, Turkey. Tel: +90-532-4267927; Fax: +90-232-4204895; Web site: http://www.icaseonline.net/seiweb/
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  Data: 10
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  Data: 2025
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  Data: Policymakers; Teachers
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  Data: Journal Articles<br />Information Analyses
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  Data: <searchLink fieldCode="DE" term="%22Computation%22">Computation</searchLink><br /><searchLink fieldCode="DE" term="%22Thinking+Skills%22">Thinking Skills</searchLink><br /><searchLink fieldCode="DE" term="%22Science+Education%22">Science Education</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Processes%22">Learning Processes</searchLink><br /><searchLink fieldCode="DE" term="%22Information+Retrieval%22">Information Retrieval</searchLink><br /><searchLink fieldCode="DE" term="%22Models%22">Models</searchLink><br /><searchLink fieldCode="DE" term="%22Correlation%22">Correlation</searchLink><br /><searchLink fieldCode="DE" term="%22Programming%22">Programming</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Software%22">Computer Software</searchLink>
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  Data: 1450-104X<br />2077-2327
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  Data: This research investigates the implementation and challenges of incorporating computational thinking (CT) into science education. This research aims to develop a comprehensive framework to improve computational thinking skills in science education through the implementation of a deep learning curriculum, also integrated with computer programming, which leads to enhancing analytical and problem-solving skills for students to tackle real-world problems. This research used a systematic literature review with the PRISMA technique. Data sources used in this research are indexed by the Crossref database and then analyzed using the VOSviewer program. By examining 133 articles included in total, the research identifies key factors that affect effective teaching in science classrooms to enhance computational thinking. Through a series of case studies and empirical analysis, the study highlights obstacles faced in this educational approach. Findings suggest that while deep learning and computer programming integrated into science classrooms can influence the improvement of computational thinking skills and students' understanding of scientific concepts and their application, challenges such as limited resources are addressed. The proposed framework offers practical strategies for policymakers and educators, especially science educators, in designing learning to overcome these challenges, aiming to prepare students for a technology-driven future better.
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      – Text: English
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      Pagination:
        PageCount: 10
        StartPage: 480
    Subjects:
      – SubjectFull: Computation
        Type: general
      – SubjectFull: Thinking Skills
        Type: general
      – SubjectFull: Science Education
        Type: general
      – SubjectFull: Learning Processes
        Type: general
      – SubjectFull: Information Retrieval
        Type: general
      – SubjectFull: Models
        Type: general
      – SubjectFull: Correlation
        Type: general
      – SubjectFull: Programming
        Type: general
      – SubjectFull: Computer Software
        Type: general
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      – TitleFull: Computational Thinking and Deep Learning on Science Education Framework: A Systematic Review
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            NameFull: Zainur Rasyid Ridlo
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            NameFull: Silvi Putri Ayu Ningsih
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            NameFull: Azza Liarista Anggraini
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              Type: published
              Y: 2025
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