Applying VOSviewer in a bibliometric review on English language teacher education research: an analysis of narratives, networks and numbers
Bibliographic record
Abstract
The purpose of this article is to promote researcher agency to use bibliometric analysis for professional development. To achieve this aim, this paper provides an overview of academic research, by conducting a bibliometric review on English language teacher education between 1946 and 2022. With the aid of the VOSviewer software tool, this bibliometric review analysed 2594 Scopus-indexed documents related to English language teacher education. Rui Yuan from Hong Kong, China, was the most productive researcher. Karen E. Johnson (USA), Icy Lee (HK) and Thomas S.C. Farrell (Canada) were identified as the other three most influential researchers in English language teacher education. Also, the analysis showed three frequently discussed topics at different times: ‘teacher beliefs’ which appeared at Phase 3 (2010–2019) and Phase 4 (2020–2022), ‘pre-service teachers’ at Phase 2 (2000–2009), Phase 3 (2010–2019) and Phase 4 (2020-2022), and ‘reflective practice’ at Phase 1 (1946-1999), Phase 3 (2010–2019) and Phase 4 (2020–2022). The study uncovered several emerging topics, namely ‘sociocultural theory’, ‘teacher agency’, ‘online teaching’, and ‘higher education’. These findings contribute to a better understanding of English language teacher education. The information and data gained from a bibliometric review may help early-career researchers, postgraduate students, and experienced researchers exercise their agency in framing and strategising their research trajectories.
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 | 0.113 | 0.314 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.139 | 0.205 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".