English as a Foreign Language (EFL)Writing Instruction: A Review of Bibliometric Analysis
Bibliographic record
Abstract
Writing instruction is a prominent topic in the field of English as a Foreign Language (EFL). Numerous studies have explored it from various perspectives. However, none has yet provided a bibliometric review of EFL writing instruction. To close the gap, a bibliometric review study was developed to gain insightful information on the current state and trends of EFL writing instruction as well as gaps that need addressing. The bibliometric approach in the current study used the tools--VOS viewer and SC Imago to analyze 2417 publications, which were derived from the Web of Science Database 2013-2024 and based on the criteria of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The extracted data were analyzed regarding the publications’ trajectory, countries, researchers, journals, and keywords. The main findings of EFL writing instruction’ current state was that, despite occasional fluctuations, annual publications in this field are increasing. Also, China and Iran emerged as the significant contributors and collaborators in the field, whereas Africa produces and collaborates the least. Furthermore, researchers from Asian countries produced the most publications and gained the most citations, as well as journals with the most publications and the highest citations came from two countries, namely the United States as well as the United Kingdom. The research trend shifted from the topics including "professional development," "beliefs," and "CLIL” to "virtual reality," "COVID-19," "blended learning," "writing performance," and "engagement". Future studies should perform in-depth bibliometric analyses from perspectives of bibliographic coupling as well as intellectual structure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.044 | 0.169 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.115 | 0.134 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.004 |
| 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".