PHENOMENOGRAPHY: IMPLICATIONS FOR EXPANDING THE EDUCATIONAL ACTION RESEARCH LENS
Bibliographic record
Abstract
Action research is a growing tradition for improving teachers’ practice and students’ learning outcomes, and it draws from a variety of methods for collecting and analysing data. In this article, phenomenography is proposed as an innovative approach for enhancing action research. With an emphasis on mapping variations on students’ experience, using their own voices, phenomenography offers an analytic system for revealing how students differ in their perspectives, and results from this approach can potentially lead to action research for tailoring curriculum to fit a diverse student population. Though popular among researchers who are interested in studying variation, especially educators, phenomenography is absent in the action research literature. Qualitative analytic approaches tend to reduce data to a few common themes, yet phenomenography is about purposefully coding the data to explore differences. In diverse communities, phenomenography offers a system for tapping a variety of perspectives/conceptions/experiences, including oppositional ones, and action research offers the means for improving educational conditions. While much of action research is committed to inviting multiple voices to resolve educational problems as participatory action research, phenomenography has not been explicitly indicated as a methodological approach throughout the literature. This article draws attention to the potential union of these two disciplines as phenomenographic 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 | 0.319 | 0.233 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.024 | 0.149 |
| Scholarly communication | 0.039 | 0.060 |
| Open science | 0.009 | 0.034 |
| Research integrity | 0.014 | 0.022 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".