PRACTICING WHAT WE TEACH: USING ACTION RESEARCH TO LEARN ABOUT TEACHING ACTION RESEARCH
Bibliographic record
Abstract
In this article, action research is explored as a process for instructor reflection, professional learning and collaboration. The context for the professional learning was the teaching of graduate level education courses in which action research, in conjunction with a cohort-based, collaboratory approach to learning, was used to facilitate students' design and enactment of site-based action research. Action research was used as a research methodology by course instructors who reflected on the design of the course and their pedagogical practices in order to engage in continuous quality improvement. The collaboration took place over a two-year period in which the instructors taught multiple online sections of the course. Using action research to collectively deepen understanding about teaching action research proved to be a valuable reflective experience for the instructors and continues to inform ongoing instructional design processes and the development of future research agendas related to instructor collaboration and action research.
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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 | 0.147 | 0.144 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.009 | 0.060 |
| Scholarly communication | 0.024 | 0.024 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.008 | 0.011 |
| 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".