Sharing methodology: A worked example of theoretical integration with qualitative data to clarify practical understanding of learning and generate new theoretical development
Bibliographic record
Abstract
BACKGROUND: Theoretical integration is a necessary element of study design if clarification of experiential learning is to be achieved. There are few published examples demonstrating how this can be achieved. AIMS: This methodological article provides a worked example of research methodology that achieved clarification of authentic early experiences (AEEs) through a bi-directional approach to theory and data. METHODS: Bi-directional refers to our simultaneous use of theory to guide and interrogate empirical data and the use of empirical data to refine theory. We explain the five steps of our methodological approach: (1) understanding the context; (2) critique on existing applications of socio-cultural models to inform study design; (3) data generation; (4) analysis and interpretation and (5) theoretical development through a novel application of Metis. RESULTS: These steps resulted in understanding of how and why different outcomes arose from students participating in AEE. Our approach offers a mechanism for clarification without which evidence-based effective ways to maximise constructive learning cannot be developed. In our example it also contributed to greater theoretical understanding of the influence of social interactions. CONCLUSION: By sharing this example of research undertaken to develop both theory and educational practice we hope to assist others seeking to conduct similar 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 | 0.279 | 0.187 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.009 | 0.020 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.006 | 0.019 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 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".