An examination of the studies on foreign language teaching in pre-school education: a bibliometric mapping analysis
Bibliographic record
Abstract
This study aims to provide a bibliometric mapping analysis of the studies on foreign language teaching in early childhood education in Web of Science. A total of 638 studies were reached however 596 studies were selected for the analysis. For bibliometric analysis, VOSViewer programme was used in order to reveal the most used keywords, words in the abstracts, citation analyses and co-citation analyses in the studies. In addition, whether any technology was adopted in the studies was examined within 76 studies. The results showed that the most used keywords were English language learning, bilingualism, English as a second language, and English learners. The most used words in the abstracts were teacher, acquisition and effect. Bialystok and Cummins, are the most cited authors in this field. The most cited journals are Journal of Educational Psychology and Applied Psycholinguistics. The studies have been mostly published in Spain, Sweden, and Israel. In addition, it is observed that the studies started in 2012 mainly in the United States, Canada, England and Germany. When technology use in the studies was examined, ICT in education, multimedia, digital technologies, instructional technology, technology integration were used in the studies. This study provides a guide for new studies, to identify the trends in the field and to compare the existing research on the topic. Consequently, it is suggested that future researches need focusing more on the pedagogical aspects and testing whether teaching environments supported by new technologies contribute to foreign language education.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.011 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.124 | 0.154 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".