Bibliometric Analysis of Articles on Distance Education During the Last Two Decades
Bibliographic record
Abstract
In this study, research on distance education in the last twenty years has been analyzed according to years, keywords, countries, and institutions. The sample of the study consisted of 20,634 studies published on distance education in the last twenty years on Web of Science. Data were downloaded from the database by year. The data obtained were analyzed with WOSViewer. For some analyses, data were analyzed with Power BI version 2.11 software to reach meaningful results. The results were analyzed and compared separately for four quarters or periods of time: 2003-2007, 2008-2012, 2013-2017, and 2018-2023. As a result, most publications were made in 2021 and in the fourth quarter. "Distance learning, higher education, distance education, education, online education, blended learning, online teaching, pandemic, and covid-19" are the most used keywords according to the general distribution of research by keywords. The use of keywords differed by quartiles. The USA, China, England, Australia, and Spain were the countries with the highest number of distance education publications according to general distribution. The USA, England, Australia, Spain, and Saudi Arabia have conducted studies every quarter. The University of California System, N Research Partnership, University of London were the organizations with the highest number of publications during the time periods studied. It was found that only the Open University in the UK published distance education articles in three quarters.
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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 | 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 | 0.007 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.184 | 0.230 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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.
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".