Challenges and Issues in the Evaluation of Teaching Quality: How Does it Affect Teachers' Professional Practice? A UK Perspective
Bibliographic record
Abstract
Evaluation of the quality of higher education is undertaken for the purposes of ensuring accountability, accreditation, and improvement, all of which are highly relevant to veterinary teaching institutions in the current economic climate. If evaluation is to drive change, it needs to be able to influence teaching practice. This article reviews the literature relating to evaluation of teaching quality in higher education with a particular focus on teachers' professional practice. Student evaluation and peer observation of teaching are discussed as examples of widely used evaluation processes. These approaches clearly have the potential to influence teachers' practice. Institutions should strive to ensure the development of a supportive culture that prioritizes teaching quality while being aware of any potential consequences related to cost, faculty time, or negative emotional responses that might result from the use of different evaluation methods.
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 | 0.105 | 0.213 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.024 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".