Teaching Critical Reflection to Graduate Students
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it