Implementing Action Research in a Teacher Preparation Program: Opportunities and Limitations
Bibliographic record
Abstract
Action research has the potential to reconstruct schools into professional learning communities that are able to identify educational issues and develop appropriate solutions for 21st century learning. Increasingly, teacher education programs are providing action research experiences to encourage analytical thinking and problem-solving skills (Darling-Hammond, 2009, 2012). The purpose of this study was to critically examine the experiences of the teacher educator and teacher candidates involved in the implementation of an action research component over four years in a revised consecutive initial teacher preparation program. A case study design using action research methodology was used in the research, which provided the tools to explore a complex phenomenon within its context: the implementation of an action research assignment in a core course in a teacher preparation program. The perceptions of the faculty teaching the course and the teacher candidates (n=544) in each of the four years provided insight into challenges, benefits, and lessons learned. The discussion centers on the implementation of action research in a compulsory course in a teacher education program; identifying opportunities and limitations settled into four main categories: structural incongruence, reflection, growth, and recommendations.
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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 | 0.642 | 0.583 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.015 | 0.020 |
| Scholarly communication | 0.021 | 0.026 |
| Open science | 0.018 | 0.018 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".