Exploring the emerging COVID-19 research trends and current status in the field of education: a bibliometric analysis and knowledge mapping
Bibliographic record
Abstract
Background/purpose – The current study aims to analyze the thematic \nstructures and trends of scientific publications that examine the relationship \nbetween the COVID-19 pandemic and education, while presenting a \nroadmap for future research on this topic. \nMaterials/methods – The data were obtained from the Web of Science \nCore Collection (WoSCC) bibliographic database by identifying the \npublications that examine the relationship between the COVID-19 pandemic \nand education, then were analyzed using bibliometric methodology and \ncontent analysis. VOSviewer, GraphPad softwares, and visualization maps \nwere used to analyze the data and to present the findings. \nResults – The results of the study show that publications examining the \nrelationship between the COVID-19 pandemic and education focused on \n“online education” and “teacher education,” while the countries that \ncontributed the most to publications on this issue were USA, United \nKingdom, Canada, and Spain. It was determined that most publications \npreferred the “theoretical model” and the majority of the research data \nwere obtained through “scale/interview forms.” Furthermore, the findings \nof this study revealed that during the COVID-19 pandemic period, the \neditorial/refereeing processes of the articles submitted to academic journals \nwere carried out very quickly and the articles were published unusually \nquickly. \nConclusion – This study indicated that the majority of scientific studies on \nCOVID-19 are focused on the field of health, and that there is limited edition \nresearch on COVID-19-related education. To the best of the authors’ \nknowledge, the current study is the first research article in the international \nliterature to examine the thematic structures and trends of scientific \npublications on the relationship between solely education and COVID-19 \nthrough bibliometric and content analysis; and contributes to the knowledge \nbase on COVID-19-related education by mapping the existing knowledge.
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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: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.016 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.143 | 0.174 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".