Bibliographic record
Abstract
Abstract The aim of this study was to reveal the development trends of the last 50 years of Web of Science (WoS) educational research. For this aim, 93,699 articles published in 116 journals were analysed with bibliometric techniques. The results of the analysis showed that the number of WoS educational research articles accelerated in 1980, 2008 and 2019. The first conclusion of the research was that funding support is an important factor in the number of WoS educational research articles. The core topics of educational research by countries, year intervals, Q categories of journals and all sub‐disciplines of education were revealed in this study, and the second conclusion of this research was that the core topics of the last half‐century of WoS educational research articles were higher education, teacher education, professional development and assessment. In addition, the study revealed that components of the core topics in educational research have differed according to countries, year intervals, Q categories of journals and all sub‐disciplines of education. The results of this study showed that the focus of the articles published in Q1 journals was technology‐assisted learning environments. The most productive countries were the USA, UK, Australia and Canada. In addition, China and Taiwan were among the top 10 with increasing article number in recent years. According to another conclusion of this study, the contribution rates of the top 10 countries to educational research tended to decrease with the contributions of other countries’ researchers to the field. It was found that the most productive authors were C.C. Tsai, G.J. Hwang and W.M. Roth. On the other hand, the most productive organisations, the journals that publish the most articles, the organisations that provide the most funds and the common features of the most cited articles were determined in the present study. The diversity in educational researches can be achieved by knowing the topics that researchers have been interested in the past periods. Therefore, the results of the present study are considered to be indicative for different studies in future.
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 | 0.012 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.064 | 0.071 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".