Lessons Learned from Research on Student Evaluation of Teaching in Higher Education
Bibliographic record
Abstract
Abstract In higher education, anonymous student evaluation of teaching (SET) ratings are used to measure faculty’s teaching effectiveness and to make high-stakes decisions about hiring, firing, promotion, merit pay, and teaching awards. SET have many desirable properties: SET are quick and cheap to collect, SET means and standard deviations give aura of precision and scientific validity, and SET provide tangible seemingly objective numbers for both high-stake decisions and public accountability purposes. Unfortunately, SET as a measure of teaching effectiveness are fatally flawed. First, experts cannot agree what effective teaching is. They only agree that effective teaching ought to result in learning. Second, SET do not measure faculty’s teaching effectiveness as students do not learn more from more highly rated professors. Third, SET depend on many teaching effectiveness irrelevant factors (TEIFs) not attributable to the professor (e.g., students’ intelligence, students’ prior knowledge, class size, subject). Fourth, SET are influenced by student preference factors (SPFs) whose consideration violates human rights legislation (e.g., ethnicity, accent). Fifth, SET are easily manipulated by chocolates, course easiness, and other incentives. However, student ratings of professors can be used for very limited purposes such as formative feedback and raising alarm about ineffective teaching practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".