The Evolving Landscape of Collaborative Writing in Literacy Education: A Systematic Bibliometric Review (2016-2025)
Bibliographic record
Abstract
This study aims to explore the global landscape of research on Collaborative Writing in Literacy Education through a systematic bibliometric analysis. Using 364 Scopus-indexed publications from 2016 to 2025, the study maps trends in publication growth, influential authors, productive countries, and thematic evolution. Data were collected through a TITLE-ABS-KEY search with the keywords “collaborative writing” and “literacy” and analyzed using Microsoft Excel, VOSviewer, and Biblioshiny. The results indicate a steady increase in scholarly output, with the United States, United Kingdom, and Canada as dominant contributors, and emerging research from China, Indonesia, and Australia. The most active authors, including Ann Hill Duin and Isabel Pedersen, advanced studies on digital and academic literacy integration. Thematic mapping identified digital literacy, academic writing, and teacher education as core areas, while AI-assisted writing and critical literacy represent emerging trends. The study’s novelty lies in providing a comprehensive bibliometric synthesis that integrates thematic, co-authorship, and keyword analyses to reveal intellectual linkages and future directions. Practically, it guides educators and policymakers in adopting collaborative and technology-enhanced pedagogies that foster critical, digital, and reflective literacy. This study contributes to a deeper understanding of how collaboration transforms literacy learning in a digitally connected world.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.068 | 0.219 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.162 | 0.159 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".