What’s the “Use” of Student Ratings of Instruction for Administrators? One University’s Experience
Bibliographic record
Abstract
At most Canadian and American community colleges and universities, student ratings have been implemented as a means of evaluating course instruction. Although concerns regarding the validity of student ratings from instructors’ perspectives have been studied quite extensively, issues associated with the use of student ratings information by administrators have been largely ignored. In this study, we surveyed 52 administrators at a major Canadian university about the types of ratings they use, how useful they are, and their purpose. Our findings indicate that administrators are interested in knowing about instructor characteristics and teaching procedures. In addition, ratings are being used for instructor and department evaluation as well as scheduling courses. In general, administrators regard student ratings positively and think that they are useful. However, they have some reservations. Dans la majorité des collèges et des universités, les étudiants évaluent leurs professeurs. Les inquiétudes de la validité de ces évaluations selon les professeurs, ont été bien etudiées, mais l’utilization et les perspectives des administrateurs n’ont pas été etudiées. Dans cet étude 52 adminstrateurs dans une grande université Canadienne ont répondu aux questions selon les evaluations. Les résultats indiquent que les administrateurs s'intéressent aux caractéristiques des professeurs et de leurs apprentissages. De plus, les administrateurs utilisent les évaluations pour organiser les cours. En general, les administrateurs ont donné des réponses très positives avec peu de réservations.
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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 | 0.043 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".