State-of-the-art in teachers’ online pedagogical competencies in higher education from 2011 to 2022
Bibliographic record
Abstract
University teachers play an important role in teaching and learning activities in the online environment. However, the emerging remote education due to the COVID-19 pandemic has revealed the shortcomings of online teaching competencies of teachers. The bibliometric study investigated the knowledge base of online teacher pedagogical competencies in higher education by analyzing 131 Web of Science-indexed documents from 2011 to 2022. The findings revealed the sudden decline of the number announced in the two years 2021 and 2022. Spain, Russia, the United States, and Canada are emerging countries in the network of international collaboration. The research community is based on small research groups and has emerged in recent years. Sources focus on subjects such as education, e-learning, computer science, developmental and educational psychology, among which education sciences is the most prominent one in the last years. With nine identified themes, these of interest are online teacher roles, remote emergency teaching, online professional development, and online teaching in COVID-19. Emerging keywords highlighted potential topics in the foreseeable future such as competency frameworks, global education, and teaching professionalism.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".