Student voices from the classroom: Concluding reflections on cultivating an environment where learning deeply matters
Bibliographic record
Abstract
The voices in this anthology thus far have largely omitted student stories about teaching and learning at McMaster. In response, this chapter is authored by current undergraduate and graduate students including recent alumni and teaching assistants who contributed to the student peer review team for the anthology, Where Learning Deeply Matters: Reflections on the Past, Present, and Future of Teaching at McMaster University. Our reflections in this concluding chapter—spanning issues of equity, diversity, and inclusion; student-instructor collaboration in online learning; accessibility; differential opportunities afforded to those in large enrolment programs; and integrating experiential learning into curriculum and program design—arise from personal student experiences but also amplify connections we felt with the themes discussed throughout the anthology. The urgency of our reflections, particularly as they come from underrepresented learners and teaching assistants, points towards the importance of respecting and listening to student voices. If taken into consideration, these perspectives, coupled with McMaster’s ongoing work to advance equity, diversity, and inclusion and to create an enriched culture to reflect its community, will undoubtedly guide the university towards cultivating an environment where “learning deeply matters.”
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".