Digging for ponies: reflection, feedback, accountability, and social support for STEM educators
Bibliographic record
Abstract
“There must be a pony somewhere!”\nThe value of critical reflection of teaching in higher education is increasingly being recognized for its use in ongoing professional improvement. Indeed, a purposeful reflective practice was used as a criterion to distinguish “expert” from “novice” instructors in a recent study aiming to quantify differences between novices and experts (Auerbach et al. 2018). However, reflection and feedback can be challenging on a number of levels, such as concerns have been raised about some traditional feedback approaches (e.g. student evaluations of teaching).\nWith that in mind, one year ago, a group of Biology educators from different universities in Ontario embarked upon a critical reflection practise project. Together, we sought to develop a better understanding of how reflection and feedback could affect our teaching, while supporting one another and keeping each other accountable. It’s been an eventful year (in different ways for each of us)!\nThis session will introduce the literature on critical reflection and feedback in STEM teaching, share our own experiences and lessons learned, and provide tips and materials for others who would like to undertake their own reflection project. Participants will leave this session with resources and practical steps for continuing their professional reflection individually or in reflection groups.
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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 | 0.025 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".