Advancing young students’ computational thinking: An investigation of structured curriculum in early years primary schooling
Open accessAbstract
In recent years, the development of computational thinking (CT) has become integral to many school curricula worldwide. This has been associated with calls for computational thinking to be considered a ‘21St Century’ competency, valuable to all students as a transferable process for solving problems and building understanding of human behaviour and systems. However, while computational thinking is a focus of most secondary school computer science curricula, proponents such as Jeanette Wing argue its relevance for younger students, indicating more work must be done investigating its development in early years' education. This study used a structured, problem-based curriculum supported by guided inquiry pedagogy, to explore 6 year old students' learning of basic computational thinking concepts and practices while coding programmable floor robots (Blue-bots and an iPad app). Results indicated improvement across the seven lessons in students' sequencing/algorithm authoring, error correction, and pattern recognition. Furthermore, they revealed evidence of higher order thinking such as identifying patterns in code, and how these can be transferred to help solve problems of different designs. While currently play-based approaches are used to introduce computational thinking concepts and practices in early years' education, results from this study suggest that more structured, problem-based methods should be seriously considered. Results challenge commonly understood developmental th…
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There is a noticeable trend toward integrating computational thinking into even the first levels of K-12 education.
“Thus, there's a noticeable trend toward integrating computational thinking (CT) into even the first levels of K-12 education [26][27][28].”
From Fostering computational thinking in young students through student generated challenges in tangible mobile augmented reality games (2025) · cites this paper as [27]
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- Authors
- Falloon, G.
- Affiliations
- Federation University (AU)
- Corresponding author
- Falloon, Garry
- ORCID
- Falloon, Garry
- Venue
- Computers & Education
- Publisher
- Elsevier BV
- Published
- 2024-07
- Volume
- 216
- Pages
- 105045
- ISSN
- 0360-1315 (ISSN-L)
- OpenAlex
- W4394580410
- Cited by
- 63
- References
- 49
- Citations by year
- 2026: 29 · 2025: 27 · 2024: 7
- FWCI
- 17.41
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Computers & Education (publishedVersion, cc-by)
- FedUni ResearchOnline (Federation University Australia) (submittedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Physical Sciences › Computer Science › Artificial Intelligence › Educational Robotics and Engineering
- Social Sciences › Education › Child Development and Digital Technology
- Keywords
- computational thinking, problem-based curriculum, algorithmic thinking, error correction, pattern recognition, higher-order thinking skills, digital literacy, teacher professional knowledge
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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