Co-constructing educational innovations for an uncertain future: Design thinking and developmental evaluation in the school reform process
Bibliographic record
Abstract
This qualitative research examines the question: In what ways does a developmental evaluation of a STEM program co-developed through a design thinking process illuminate program goal attainment? Students, parents, teachers, high school staff, school district administrators, advisory committee members, community, and college and university partners in a southern Ontario region participated in a collective effort to develop an innovative approach to STEM education in the context of the United Nations’ Sustainable Development Goals, collaboratively prototyping the concept in a design workshop, and further refining it with diverse stakeholders. Collaborators co-constructed a developmental evaluation framework to continuously learn from experimentation toward program goals. Data sources include participant observation, field notes, interviews, and document analysis. Findings highlight program strengths needed to sustain it, and challenges that threaten its longevity. The paper concludes with a program assessment based on its intended impacts and opportunities to scale a design thinking approach in education to other jurisdictions. • Design thinking and developmental evaluation are novel strategies for school reform implementation. • Collaborative educational re-imagining enables progressive curriculum innovation that meets individual and societal needs. • Developmental evaluation gives researchers, educators and policymakers new ways to evaluate and modify ongoing curriculum. • Considering depth, sustainability, spread and ownership shift possibilities assists scaling a reform from one school to many.
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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 | 0.226 | 0.143 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.014 | 0.048 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".