Conflict of interest explains the size of student evaluation of teaching and learning correlations in multisection studies: a meta-analysis
Bibliographic record
Abstract
We examined the associations between the size of student evaluation of teaching and learning (SET/learning) correlations and presence of several conflicts of interest (COIs) including corporate, administrative, evaluation unit, SET author, and funder interests. Our meta-analyses of SET/learning correlations reported by multisection studies show that researchers with a vested interest in finding large positive SET/learning correlations found, on average, large positive SET/learning correlations. In contrast, researchers with no identifiable COIs found that SET/learning correlations were zero or nearly zero. The largest SET/learning correlations were reported by authors with ties to SET selling corporations. Smaller but still substantial SET/learning correlations were reported by researchers with administrative assignments and by researchers in evaluation units/departments responsible for the administration of SET. Moreover, authors with the most significant COIs were publishing their studies primarily prior to 1981 whereas authors with no or less significant COIs were publishing their studies in 1981 or afterwards. Studies published prior to 1981 reported small but significant ( r = .31) SET/learning correlations whereas studies published in 1981 and after reported near zero, non-significant SET/learning correlations ( r = .06). The presence of COIs was associated with earlier publication date but also with smaller samples. Finally, whereas corporate, administrative, and evaluation unit authors nearly ceased publishing multisection studies on SET/learning correlations, authors from business and economics departments are now responsible for the substantial portion of newer, larger, and higher quality studies published in 1981 and after.
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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 | 0.077 | 0.149 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.036 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".