The educational development of university teachers: mapping the landscape
Bibliographic record
Abstract
This article presents the results of a scoping review designed to explore the current state of knowledge about the educational development of university teachers. More specifically, the study examined the definitions attributed to educational development, its aims, the factors that foster it and the variables studied in this field. A thematic analysis was conducted on 98 scholarly documents published between 2000 and 2022. The results indicate that the field of educational development is mainly characterized by ideological and political rather than scientific dimensions. Consequently, the focus is on desired changes in educational development, reflecting a high degree of desirability. Furthermore, the results highlight the individualistic nature of the starting point of professional learning process, suggesting that institutional conditions and resources should be adapted to accommodate the diversity of learning trajectories. This study contributes to a comprehensive understanding of the complex landscape surrounding the educational development of university teachers, highlighting the need for nuanced approaches to promote teaching quality and professional development in the context of higher education.
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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 | 0.020 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.022 | 0.037 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".