An Application of Mezirow’s Critical Reflection Theory to Electronic Portfolios
Bibliographic record
Abstract
In this study, the authors developed a framework for the analysis of teacher reflection in standards-based e-portfolios. Using NVivo, the authors analyzed the written arguments of 127 students, as they provided rationales for why their chosen artifacts represented specific teaching standards. In total, 656 rationales yielded 1,427 statements when categorized using Mezirow’s types of reflection. The findings indicate that subjective reframing, in general, and narrative critical self-reflection on assumptions and epistemic critical self-reflection on assumptions, in particular, are well represented in the University of Northern British Columbia (UNBC) teacher education program as evidenced by approximately 50% of the overall statements represented by these two types of critical self-reflection. As well, Mezirow’s taxonomy appears to be a sound theoretical framework to represent reflection in teacher education.
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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 | 0.028 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.004 | 0.031 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".