Evidence map›Paper›PMID 41440818›Full record

ReviewJournal of Intelligence2025

From Evidence to Insight: An Umbrella Review of Computational Thinking Research Syntheses.

Jin Zhang, Yaxin Wu, Yimin Ning, Yafei Shi

Abstract readReview
In one paragraph

Review in Journal of Intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Jin ZhangFaculty of Education, Henan Normal University, Xinxiang 453007, China.
Yaxin WuFaculty of Education, Henan Normal University, Xinxiang 453007, China.ORCID 0009-0008-4828-3085
Yimin NingSchool of Mathematical Sciences, East China Normal University, Shanghai 200241, China.ORCID 0000-0002-6491-0906
Yafei ShiFaculty of Education, Henan Normal University, Xinxiang 453007, China.ORCID 0000-0002-6019-1369

Funding

National Natural Science Foundation of China 62477008
6 · The paper itself

Abstract

This study reviews 33 meta-analyses and systematic reviews on Computational Thinking (CT), focusing on research quality, intervention effectiveness, and content. Quality assessment of included studies was conducted using the AMSTAR 2 tool. The meta-analysis achieved an average score of 10.9 (a total of 16 points), while systematic reviews scored an average of 6.1 (a total of 11 points). The 15 meta-analyses showed diverse intervention strategies. Project-based learning, text-based programming, and game-based learning demonstrate more pronounced effects in terms of effect size and practical outcomes. Curricular integration, robotics programming, and unplugged strategies offered additional value in certain contexts. Gender and disciplinary background were stable moderators, while grade level and educational stage had more conditional effects. Intervention duration, sample size, instructional tools, and assessment methods were also significant moderators in several studies. The 18 systematic reviews used a five-layer framework based on ecological systems theory, covering educational context (microsystem), tools and strategies (mesosystem), social support (exosystem), macro-level characteristics (macrosystem), and CT development (chronosystem). Future research should focus on standardizing meta-analyses, unifying effect size indicators, and strengthening longitudinal studies with cognitive network analysis. Additionally, systematic reviews should improve evidence credibility by integrating textual synthesis and data-driven reasoning to reduce redundancy and homogeneity.

Indexed as

computational thinkingliterature reviewmeta-analysispedagogical issuessystematic reviewumbrella review

Identifiers

PMID41440818
PMCPMC12733880

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.