Becoming Reflective and Inquiring Teachers: Collaborative Action Research for In-service Chilean Teachers
Bibliographic record
Abstract
This article discusses the outcomes of a case study that engaged Chilean in-service teachers in\nsystematic action research (AR) as a means of improving their pedagogical practice and effecting\nchanges in their educational context. The study involved six in-service teachers from a region of\nChile and two university researchers. The findings show that knowledge of systematic AR\nprovided the teachers with the necessary means to engage in a critical reflection and inquiry\nprocess regarding their own practice. The teacher participants also perceived the self-reflective\nspiral of reflection and action to be crucial in establishing new habits of inquiry and reflection\nabout their own pedagogical actions. The findings support earlier studies (e.g., Price & Valli, 2005;\nSteven & Kitchen, 2005, 2011) concerning the necessity of including knowledge of systematic AR\nin teacher preparation programs in order to foster strong habits of inquiry and reflection among\npreservice teachers. Finally, the study suggests that participation in a systematic reflection and\ninquiry process contributes to empowering in-service teachers to become agents of pedagogical\nchange through their own actions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".