Bibliographic record
Abstract
Critical reflection is a highly valued and widely applied learning approach in higher education. There are many benefits associated with engaging in critical reflection, and it is often integrated into the design of graduate-level courses on university teaching, as a life-long learning strategy to help ensure that learners build their capacity as critical reflective teaching practitioners. Despite its broad application and learning benefits, students often find the process of engaging in critical reflection inherently challenging. This paper explores the challenge associated with incorporating critical reflection into a graduate course on University Teaching at the University of Guelph. Strategies for effectively incorporating critical reflection are presented, based largely on Arsonson’s (2011) framework for teaching critical reflection and the outcomes of a workshop offered at the 2013 STLHE Conference. The strategies discussed have multi-disciplinary relevance, and can be broadly applied to improve how critical reflection is incorporated into post-secondary courses.
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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 | 0.013 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".