The Importance of Ending Well: A Virtual Last Class Workshop for Course Evaluation and Evolution
Bibliographic record
Abstract
The last class session of the academic term represents an excellent opportunity to solicit meaningful feedback from students who have just completed the course. To capitalize on the students’ first-hand knowledge of their own experiences with our course and maximize the impact of the last class for our Canadian graduate-level genetics course, we have used and optimized a workshop first described by Bleicher (2011) as a means of obtaining real-time, in-person course evaluations, and driving course evolution. Presented as an empowering opportunity for student activism, students are asked to contribute collaboratively to improving future iterations of the course. This approach stimulates thoughtful discussions, generates honest and useful feedback, and requires only nominal preparative work on the part of the instructor, whose primary role during the workshop is as a facilitator. In light of the COVID-19 pandemic, we’ve assessed student perceptions of two virtual models for the Last Class Workshop—one using Google Docs, a free web-based word processor, and another using Miro, a collaborative whiteboard platform—to identify whether or not the Last Class Workshop can be effectively translated for a synchronous online learning environment. Student responses to the virtual workshops have been highly positive, and participants overwhelmingly preferred the Miro adaptation. We suggest that this is an effective way to access the expert knowledge of our students to develop innovative adaptations, updates, and evolutionary change at the end of a course, and conclude with a proposal for maintaining this virtual tool after in-person learning resumes.
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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 | 0.030 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".