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Part 1: Document Description
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Citation |
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Title: |
Open Dataset for Meta-Analysis of AI-Assisted Programming Learning and Students’ Computational Thinking |
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Identification Number: |
doi:10.7910/DVN/8NAYAD |
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Distributor: |
Harvard Dataverse |
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Date of Distribution: |
2026-04-27 |
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Version: |
1 |
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Bibliographic Citation: |
YHP, 2026, "Open Dataset for Meta-Analysis of AI-Assisted Programming Learning and Students’ Computational Thinking", https://doi.org/10.7910/DVN/8NAYAD, Harvard Dataverse, V1 |
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Citation |
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Title: |
Open Dataset for Meta-Analysis of AI-Assisted Programming Learning and Students’ Computational Thinking |
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Identification Number: |
doi:10.7910/DVN/8NAYAD |
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Authoring Entity: |
YHP |
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Software used in Production: |
Comprehensive Meta-Analysis Software (CMA) |
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Distributor: |
Harvard Dataverse |
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Access Authority: |
YHP |
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Depositor: |
YANGHAIPENG, Haipeng |
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Date of Deposit: |
2026-04-24 |
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Holdings Information: |
https://doi.org/10.7910/DVN/8NAYAD |
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Study Scope |
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Keywords: |
Social Sciences, Educational Technology |
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Abstract: |
This repository contains the data and supporting materials for the meta-analysis titled The Effect of Artificial Intelligence-Assisted Programming Learning on Students’ Computational Thinking. The main research hypothesis was that artificial intelligence-assisted programming learning has a positive effect on students’ computational thinking (CT), and that the magnitude of this effect may vary across study and instructional characteristics. The dataset includes study-level information extracted from 19 empirical studies and 19 independent effect sizes. The coded variables include title, publication year, authors, educational level, teaching strategy, programming environment, sample size, intervention duration, CT measurement tool, instructional function of AI, type of AI, methodological quality scores, and the effect-size data used for synthesis. According to the study protocol, two researchers coded all included studies independently, and inter-coder reliability was high (Cohen’s kappa = 0.902). Methodological quality was assessed using the Kmet et al. checklist, and all included studies were rated as high quality, with scores ranging from 0.818 to 0.917; inter-rater reliability for quality assessment was 0.925. The repository also contains supporting documentation to improve transparency and reusability, including the coding sheet for included studies, the list of full-text articles excluded after eligibility assessment, the PRISMA 2020 checklist, and the PRISMA flow diagram. These files document how studies were identified, screened, assessed for eligibility, coded, and included in the final synthesis. The meta-analysis was conducted in Comprehensive Meta-Analysis 3.0 using a random-effects model because substantial variation across studies was expected in terms of participants, interventions, and educational contexts. Hedges’ g was used as the effect-size index. Publication bias was assessed through funnel plot inspection, Begg’s test, Egger’s test, fail-safe N, and trim-and-fill analysis. The results suggested that publication bias was unlikely to materially affect the findings. The pooled overall effect was large and positive (Hedges’ g = 0.949, 95% CI [0.650, 1.247], p < .001), indicating that AI-assisted programming learning significantly improved students’ CT. However, heterogeneity was substantial (Q = 129.398, I² = 86.1%), so moderator analyses were conducted. Significant subgroup differences were found for CT measurement tool and instructional function of AI, whereas educational level, teaching strategy, programming environment, sample size, intervention duration, and AI type did not show significant between-group differences. A leave-one-out sensitivity analysis further showed that the overall result was robust. This dataset can be used to verify the reported meta-analytic results, inspect coding decisions, reproduce subgroup analyses, and understand the study selection process. |
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Notes: |
This dataset was developed to support a meta-analysis of the effect of artificial intelligence-assisted programming learning on students’ computational thinking (CT). The data were gathered through a structured systematic review process guided by PRISMA principles. Studies were identified, screened, assessed for eligibility, and included according to predefined inclusion and exclusion criteria. To improve transparency and reproducibility, this repository also includes the PRISMA flow diagram, the PRISMA 2020 checklist, the list of studies excluded after full-text review, and the coding sheet for included studies. |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Notes: |
<a href="http://creativecommons.org/publicdomain/zero/1.0">CC0 1.0</a> |
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Other Study Description Materials |
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Label: |
AI_Programming_CT_MetaAnalysis_CMA_Data.cma |
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Notes: |
application/octet-stream |
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Label: |
Coding_of_Included_Studies.docx |
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Notes: |
application/vnd.openxmlformats-officedocument.wordprocessingml.document |
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Label: |
Folder 1_PRISMA.zip |
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Notes: |
application/zip |
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Folder 2_Screening records.zip |
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Notes: |
application/zip |
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Label: |
Full search strategies for all databases and sources consulted.xlsx |
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Notes: |
application/vnd.openxmlformats-officedocument.spreadsheetml.sheet |
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Label: |
Included document codes.xlsx |
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Notes: |
application/vnd.openxmlformats-officedocument.spreadsheetml.sheet |
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Label: |
List of literature excluded after full-text screening.docx |
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Notes: |
application/vnd.openxmlformats-officedocument.wordprocessingml.document |
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Label: |
Quality_Assessment_of_Included_Studies.docx |
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Notes: |
application/vnd.openxmlformats-officedocument.wordprocessingml.document |