Multiple Layers: Education Faculty Reflecting on Design-Based Research focused on Curricular Integration
Bibliographic record
Abstract
What insights emerge through researcher reflections on a Design-Based Research (DBR) curricular integration project that contribute to the professional learning of education faculty/ researchers? To answer this question, two researchers captured their debriefing discussions and reflections after monthly meetings with participating teachers. The meetings familiarized the teachers with DBR methods and enhanced teachers’ understanding of integrating literacy and science instruction. Data were open coded, collapsed into sub-categories and interpretations were then clustered into three themes. The first theme is our acknowledgement of the layers that needed to be peeled back to understand teacher participants’ planning and assessment. The second theme is the realization that the teacher participants were novices with respect to understanding and practicing curricular integration. The final theme honors the value of DBR as a research and professional learning method. Findings are discussed in light of the scant literature that describes the experience of DBR educational researchers.
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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 | 0.076 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.017 | 0.023 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".