Reframing design in education: Proposing a framework to support pre-service teachers in adopting designerly stances
Bibliographic record
Abstract
Background K12 teachers need a wider range of design approaches to foster feasible qualitative improvements in all students’ schooling experiences. We propose a framework for conceptualizing pre-service teachers’ stances toward designerly thinking.Methods We developed the framework following Crismond and Adams’ scholarship of integration approach combined with an inductive content analysis of 43 pre-service teachers’ conceptualizations of design across two versions of a design course.Results The resulting framework focuses on six dimensions including: (a) co-evolution of problem frames and possible solutions, (b) problem complexity and structure, (c) idea fluency and scope, (d) inquiry and stakeholder engagement, (e) conceptualization of the design process, and (f) attitude toward ambiguity. Each dimension articulates anchor points and examples based on the literature and grounded content analysis of the data. The analysis and discussion consider the conceptual accessibility of the framework’s goals for pre-service teachers and whether they considered the goals feasible to implement in the context of curricular mandates.Contribution The proposed framework provides the foundation for a hypothetical learning trajectory that can be elaborated and validated through future research to support K12 teachers in their development as informed designers who might better serve the needs of all their students.
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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 | 0.042 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.003 |
| Science and technology studies | 0.009 | 0.053 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".