Defining University Teaching Excellence in a Globalized Profession
Bibliographic record
Abstract
In universities around the world, metrics have been developed to assess research extremely well in the career development of faculty; however, teaching effectiveness has been left to the subjective and usually unreliable evidence of student evaluations, unique or irregular classroom visits, and committee reviews of course syllabi and assignments. There are seldom standards of achievement provided, and each file is usually assessed without reference to others. There is need, then, for a broad set of expectations in the academy for what we call excellence and competence in teaching. This chapter discusses how the authors’ experiences in faculty development in three institutions—in Canada, China, and Oman—reflects both the need for such metrics and the difficulties in establishing procedures in very different contexts. From this, he hopes a debate about how to establish international guidelines for teaching excellence that parallel the rigour given to the assessment of research can be initiated to guide decisions concerning appointment, promotion, and tenure in the modern, internationalized university.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".