USING A CRITICAL REFLECTION FRAMEWORK AND COLLABORATIVE INQUIRY TO IMPROVE TEACHING PRACTICE: AN ACTION RESEARCH PROJECT
Bibliographic record
Abstract
This action research reports on a three-year collaborative learning process among three teachers. We used current literature and a critical reflection framework to understand why our teaching approaches were not resulting in increased student learning. This allowed us to examine our previously unrecognized and uninterrupted—and often, problematic—beliefs and values. Our findings revealed key barriers related to unexamined judgments, beliefs, assumptions, and expectations that affected our ability to facilitate effective learning. This encouraged conceptual change and led to a transformation in our teaching practice: We became more socioculturally responsive teachers. Our findings led us to conclude that professional learning needs to move beyond the acquisition of skills and strategies, and include the critical reflection necessary to deconstruct problematic beliefs and thought-patterns that can impede student learning.
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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 | 0.149 | 0.110 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.017 | 0.028 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.008 | 0.017 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".