Good Teachers, Scholarly Teachers and Teachers Engaged in Scholarship of Teaching and Learning: A Case Study from McMaster University, Hamilton, Canada
Bibliographic record
Abstract
This paper defines and operationalizes definitions of good teaching, scholarly teaching and the scholarship of teaching and learning in order to measure characteristics of these definitions amongst undergraduate instructors at McMaster University. A total of 2496 instructors, including all part-time instructors, were surveyed in 2007. A total of 339 surveys were returned. Indices of good teaching, scholarly teaching and scholarship of teaching and learning were developed. The data illustrated a strong correlation between good teaching and scholarly teaching and between scholarly teaching and scholarship of teaching and learning. The perceived value placed upon teaching varied across the different Faculties. New instructors and those engaged in scholarly teaching and scholarship of teaching and learning perceived teaching to be more valued than their peers. Le présent article définit et opérationnalise les définitions d’enseignement efficace[1], d’enseignement érudit[2] et de la publication sur l'enseignement supérieur[3] afin de mesurer les caractéristiques de ces définitions chez les enseignants de premier cycle de l’Université McMaster. Au total, 2 496 enseignants, y compris tous ceux qui travaillent à temps partiel, ont été sondés en 2007 et 339 questionnaires ont été retournés. Les chercheurs ont élaboré des indices d’un bon enseignement, d’un très bon enseignement et d’un excellent enseignement. Les données illustrent une forte corrélation entre un bon enseignement et un très bon enseignement, de même qu’entre un très bon enseignement et un excellent enseignement. La valeur perçue accordée à l’enseignement variait selon les différentes facultés. Les nouveaux enseignants pratiquant un très bon enseignement et un excellent enseignement trouvaient l’enseignement plus utile que leurs pairs. [1] good teaching, [2] scholarly teaching, [3] scholarship of teaching and learning
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.030 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".