The Utility of Student Ratings of Instruction for Students, Faculty, and Administrators: A "Consequential Validity" Study
Bibliographic record
Abstract
Students, faculty and administrators at a major Canadian university were surveyed to investigate the utility or "consequential validity" of student ratings of instructors. Of the 1,229 (approximately equal number of males and females) students and alumni, about half (52%) indicated that they had never used the ratings, but of those who did use it, many (47%) reported using it several times to select courses and/or instructors. The majority (84%) of faculty members (n = 357) gave favorable responses about the usefulness of student ratings for improving quality of teaching. Paradoxically, even though faculty members were positive about the student ratings, they did not generally use them to make changes in their teaching. The majority (87%) of administrators (n = 52) stated that they use the student ratings for various purposes including decisions about faculty merit and tenure. Students, faculty and administrators considered the overall course instruction to be the most useful type of information derived from the student ratings. The results of the present study indicate that while the utility of data from student ratings of instructors is quite variable, there is evidence of "consequential validity" particularly from administrators.
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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 | 0.050 | 0.273 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".