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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.060 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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; both teacher heads agree on what is shown here.
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".