Exploring Immediate and Sustained Changes in Teaching Practices Following Midterm Student Feedback
Bibliographic record
Abstract
Midterm student feedback is increasingly considered to have greater potential for improvement in post-secondary teaching than end-of-term course evaluations. While many benefits have been established, the process for gathering midterm feedback has been studied exclusively with the aim of characterizing short-term effects. At McMaster University, in Hamilton, Ontario, Canada, midterm student feedback is called a “course refinement.” As the final part of a multiphase study investigating instructors’ perceptions of the course refinement process and its impact, this paper examines whether changes made by instructors following the course refinement process are sustained beyond the term. The study involved two phases of data collection: initially, a semi-structured in-person interview or survey completed one to three months following the conclusion of an instructor’s refined course, followed by an additional interview one year after the instructor’s course refinement. Changes to instructors’ teaching practices were evident in both phases. Furthermore, a thorough examination of sustained change revealed two predominant themes: the relationship between sustained change and instructors’ beliefs about teaching, and the impact of sustained change on various levels of higher education. The latter theme is explored via an ecological systems framework, which revealed much broader implications than we ever imagined. Course refinements do, indeed, lead to lasting changes that go beyond the boundaries of a course and have effects departmentally, institutionally, and inter-institutionally—and conceivably even influence post-secondary society and culture more widely.
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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 | 0.018 | 0.068 |
| 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 | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".