Redesigning the Course and Teacher Ratings: Methods, Outcomes, and Lessons Learned
Bibliographic record
Abstract
Abstract Less than one year into the COVID-19 pandemic, the provost and faculty union leadership at a midsized private university agreed that the time was right for a reevaluation of the student evaluation of teaching (SET) process and policy. A university-wide Blue Ribbon Committee was formed to evaluate simultaneously the SET and the peer observation processes and policies and to make recommendations as appropriate. The committee researched and compared "name-of" University (U) standards and processes to peer and aspirant institutions, as well as best practices to prevent bias in responses from students. The initial meeting of the Committee occurred on December 17, 2020 with members representing each of the schools at the University as well as the offices of the Provost and Institutional Research. Our diverse committee agreed on three guiding principles (a) Update the CTR to make it a more useful instrument for faculty development; (b) Include items that capture student perceptions of class climate; (c) Broaden the scope of teaching behaviors assessed to reflect the broad range of course structures and effective teaching styles of our faculty. To date, the committee has assessed, revised, proposed, and piloted questions for the CTRs. The committee has also tested numerous roll-out plans to optimize student response. The outcomes, results, and lessons learned from these efforts will be shared in this paper.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".