Are Students Ratings Related to Teaching Behaviors?
Bibliographic record
Abstract
The widespread use of student ratings of instruction prompts the important question, "Do differences in student ratings correspond to genuine instructional differences among faculty members? (i.e., What specific things do faculty receiving high or low student ratings actually do in the college classroom?) " A recent, carefully controlled observational study reported by Harry G. Murray (1985) provides an interesting and important analysis of this question. The following issue highlights briefly this study and examines some of the practical pedagogical issues raised by its findings. The faculty who participated in this study were 48 full-time social science faculty (39 males; 9 females) employed at the University of Western Ontario. Ten were full professors, 18 were associate professors, and 20 were assistant professors. Six to eight carefully trained undergraduate observers unobtrusively visited three separate one-hour class periods taught by these instructors over a three month period of time. Summaries of the three hours of classroom observation were made using the "Teaching Behavior Inventory " which lists 100 specific observable behaviors. A factor analysis was performed on the 93 items which had inter-rated reliability coefficients greater than.50. A six factor solution, accounting for over 62 % of the variance was obtained; the factors were labeled (1) enthusiasm, (2) clarity, (3) interaction, (4) task orientation, (5) rapport, and (6) organization. Thirty items, with factor loadings greater than.60, were used in subsequent analyses.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".