Facilitating a “Last Class Workshop” – A tool for course evaluation and evolution
Bibliographic record
Abstract
Recognizing that the last session of class at the end of term is often not very materially productive, we searched for a way to make this last class meaningful and functional. In this presentation, we describe our implementation of and research surrounding a workshop oriented towards obtaining real-time course evaluations, and driving course evolution (Bleicher, 2011).\nDuring this session we will describe models of the “Last Class Workshop” for in-person learning as well as both synchronous and asynchronous online learning environments, alongside data speaking to its success in these environments (Styles & Polvi 2022). We will describe the preparative work required of students and instructors. The success of the “Last Class Workshop” depends on the openness of the facilitator to accepting feedback of all types, and on the active engagement and deliberate self-reflection of students (Bovill et al., 2011, Pintrich, 2004), and much of the preparation before the session is oriented towards appropriately framing it for success in these areas. We’ll invite the audience to participate in a mock mini-workshop to illustrate the dynamics and utility of this tool.\nFundamentally, the “Last Class Workshop” is built on the idea that the students themselves are the best source of constructive critique, innovative adaptations, and meaningful updates in a course. It is not difficult to implement, has a noticeable impact on participants, and can provide transformative feedback.\nThis research was approved by the University of Toronto Research Ethics Board Protocol #42582 and #40718.
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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 | 0.045 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.033 | 0.014 |
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".