Trends of WoS educational research articles in the last half‐century
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Notice bibliographique
Résumé
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.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,015 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle