A systematic review of integrating computational thinking in early childhood education
Open accessAbstract
Computational thinking education has become an increasingly popular topic among practitioners and researchers. However, rare is known how to effectively teach and learn computational thinking in early childhood education. To address this knowledge gap, this systematic review examined 26 studies on the teaching and learning of computational thinking in early childhood education from 2010 to 2022. The content knowledge, tools, pedagogical design, assessment methods, and learning outcomes were analyzed. Results indicated that, with age-appropriate instructional design, children could develop early concepts and skills of computational thinking, as well as other related skills such as communication, collaboration, and problem solving. Across the studies, we found that most studies used quantitative research methods, with direct assessment and observation being the most. Several challenges were identified: (1) achieving a deeper learning of computational thinking; (2) a lack of valid and reliable computational thinking assessments for children with a wider age range; (3) selecting appropriate learning tools; and (4) designing age-appropriate activities for young learners. Although with these challenges, computational thinking education could bring new learning opportunities and enhance children's computational thinking skills, as well as other non-cognitive skills such as critical thinking, body-material interaction, and hand-eye coordination. This systematic review informs future endeavors in theorizing a digital learning framework that can integrate computational thinking into early childhood education.
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Young learners often lack the requisite literacy, arithmetic capabilities, fine-motor skills, and hand-eye coordination to effectively engage with CT platforms.
“Young learners often lack the requisite literacy, arithmetic capabilities, fine-motor skills, and hand-eye coordination to effectively engage with these CT platforms [64].”
From ChatScratch: An AI-Augmented System Toward Autonomous Visual Programming Learning for Children Aged 6-12 (2024) · cites this paper as [64]
Project-based learning emerges as the most promising approach.
“Concurrently, another line of research, has bypassed tool-specific discussions to focus instead on the topic of learning strategies, where project-based learning emerges as the most promising approaches [24, 64].”
From ChatScratch: An AI-Augmented System Toward Autonomous Visual Programming Learning for Children Aged 6-12 (2024) · cites this paper as [64]
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- Authors
- Su, J., & Yang, W.
- Affiliations
- University of Hong Kong (HK)
- Education University of Hong Kong (HK)
- Corresponding author
- Su, Jiahong
- Venue
- Computers and Education Open
- Publisher
- Elsevier BV
- Published
- 2023-12
- Volume
- 4
- Pages
- 100122
- ISSN
- 2666-5573 (ISSN-L)
- OpenAlex
- W4313596455
- Cited by
- 174
- References
- 51
- Citations by year
- 2026: 41 · 2025: 63 · 2024: 53 · 2023: 17
- FWCI
- 7.51
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Computers and Education Open (publishedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Social Sciences › Education › Child Development and Digital Technology
- Physical Sciences › Computer Science › Artificial Intelligence › Educational Robotics and Engineering
- Keywords
- computational thinking, early childhood education, assessment of computational thinking, learning tools, non-cognitive skills, problem solving, young children
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