Exploring the personal and professional factors associated with student evaluations of tenure-track faculty
Bibliographic record
Abstract
Tenure-track faculty members in the United States are evaluated on their performance in both research and teaching. In spite of accusations of bias and invalidity, student evaluations of teaching have dominated teaching evaluation at U.S. universities. However, studies on the topic have tended to be limited to particular institutional and disciplinary contexts. Moreover, in spite of the idealistic assumption that research and teaching are mutually beneficial, few studies have examined the link between research performance and student evaluations of teaching. In this study, we conduct a large scale exploratory analysis of the factors associated with student evaluations of teachers, controlling for heterogeneous institutional and disciplinary contexts. We source public student evaluations of teaching from RateMyProfessor.com and information regarding career and contemporary research performance indicators from the company Academic Analytics. The factors most associated with higher student ratings were the attractiveness of the faculty and the student's interest in the class; the factors most associated with lower student ratings were course difficulty and whether student comments mentioned an accent or a teaching assistant. Moreover, faculty tended to be rated more highly when they were young, male, White, in the Humanities, and held a rank of full professor. We observed little to no evidence of any relationship, positive or negative, between student evaluations of teaching and research performance. These results shed light on what factors relate to student evaluations of teaching across diverse contexts and contribute to the continuing discussion teaching evaluation and faculty assessment.
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 | 0.005 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".