Student Evaluations of Teaching: Understanding Limitations and Advocating for a Gold Standard for Measuring Teaching Effectiveness
Bibliographic record
Abstract
The arbitrator’s decision in Ryerson University v Ryerson Faculty Association [2018] CanLII 58446 (ON LA) rejected use of Student Evaluations of Teaching (SETs) for academic confirmation and promotion purposes. SETs provide largely quantitative data in response to pre-determined institutional, generic questions using a Likert scale applicable to all teaching modes. SETs may be efficient, but commonly low response rates mean the data is often statistically invalid. Studies of SETs suggest gender, age, race, and other biases are widespread, and they discourage teaching innovation because academics fear student backlash in SET scores. Consequently, SETs are of little value to academics for their professional development, confirmation or promotion, or as evidence for teaching grant or awards processes. The continuing impact of the COVID-19 pandemic on traditional models of teaching has forced many changes in teaching, learning and pedagogy, often with a temporary suspension of SETs to allow teachers to innovate without negative impact on professional development measures. This presents a unique opportunity for us to revisit how the effectiveness of teaching and learning is measured. Academic teaching staff still need evidence of teaching effectiveness, as do sessional staff looking for continued employment and/or a career in academia. This paper discusses the strengths and weaknesses of SETs; seeks to equip law academics to advocate for other measures of teaching effectiveness that better reflect their contribution to student learning; and to pave the way for law discipline and institutional level changes that support a gold standard in measuring teaching effectiveness beyond reliance on SETs, for the benefit of teachers in law and other disciplines.
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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 | 0.580 | 0.652 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.006 | 0.032 |
| Scholarly communication | 0.019 | 0.018 |
| Open science | 0.011 | 0.020 |
| Research integrity | 0.015 | 0.027 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".