Bibliographic record
Abstract
Trying to improve undergraduate teaching based on feedback collected by traditional student course evaluations can be a frustrating experience. Unclear, contradictory and ill-informed student comments leave instructors confused and discouraged. We designed and then implemented an evaluation mechanism where an independent CS faculty peer visits a lecture and holds an evaluation discussion with the students. These facilitated discussions begin by looking at overall strengths and weaknesses for the course but quickly focus on the key student concerns and suggestions for improvement. After conducting thirty four facilitated discussions, we find them appreciated by students who feel heard and valued. A survey of participating faculty indicates that the written discussion report is more useful to them than standard student survey results. Faculty report that they have made changes based on the recommendations and limited quantitative data suggests that teaching has improved and its value in the departmental culture has increased. In this paper we describe the evaluation process, discuss our experiences and offer some concrete suggestions for those who might want to try this approach in their own department.
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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 | 0.026 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".