Adopting Design-Based Research to Conduct a Doctoral Study as a Micro-Cycle of Design – A Practice Illustration
Bibliographic record
Abstract
In this practice illustration, I elaborate on the methodological aspect of my doctoral research, developing a multilayered participatory approach to explore learning spaces drawing on Design-Based Research (DBR). Reflecting on my work, I explain “why” and “how” I adopted DBR in my doctoral research in Education. I argue that DBR is feasible to conduct doctoral research as a micro-cycle of design to develop design methodology and/or domain theory. I provide a rationale for choosing DBR as an underpinning methodology through which I designed the study and selected the data collection and analysis methods. I also describe how DBR was interrelated with the tenets of my study and the research questions. Providing an explanation of the relationship between DBR and participatory design, I explain how design methodology was developed in the context of my study. At the end, I briefly outline the findings and the contextual design principles that emerged from the findings.
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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 | 0.271 | 0.160 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.010 | 0.035 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".