MODELING LEARNING THROUGH EXPERIENCE: USING STUDENT FEEDBACK TEAMS TO CONTINUOUSLY IMPROVE TEACHING
Bibliographic record
Abstract
Effective feedback is timely, constructive, concrete, descriptive, meaningful, and credible. Yet, universities rely on end-of-course quantitative student evaluations of teaching (SETs) in broad categories to measure teaching effectiveness, in part for evaluation purposes but also as a means of improving instruction. Research points to the well disputed validity and impact of SETs, and a few alternatives are available. Mid-semester questionnaires and “minute papers” offer instructors more opportunity to make “course corrections” midway through a course. Although these formative evaluations are an improvement over SETs in terms of their timeliness and instructors’ ability to act on them, they have their own set of limitations. This paper describes a simple process that allows instructors to continuously improve their courses: holding brief, informal student feedback team meetings after every class. Through this mutual sharing of perspectives, dialogue, and reciprocal flow of influence, instructors model openness to feedback and learning from experience to their students. As such, this approach is particularly valuable for courses employing experiential learning methods.
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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 | 0.035 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".