Co-design as a Catalyst for Becoming: Identity, Practice, and Learning Trajectories in Undergraduate Science
Bibliographic record
Abstract
Co-design is a collaborative methodology for creating educational innovations involving researchers, educators, and developers. While research-practice partnerships in the Learning Sciences have historically co-designed with teachers, recent developments have begun involving students. However, research on students' experiences of co-design remains limited. This study explores what students learn from engaging in co-design through a sociocultural learning framework linking learning to identity formation. Using multiple case study methodology, I examine the relationships between the identities, learning, and participation of six University of Toronto undergraduate science students in a co-design project creating educational virtual reality simulations. Findings reveal that identity, practice, and learning exist in dynamic and reciprocal relationships within co-design contexts. Students' existing identities informed their co-design engagement. Through practice, students developed collaboration skills and learned about themselves, which led to diverse identity development trajectories. These findings suggest engaging students as co-design partners creates unique opportunities for integrated learning and identity development.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| 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".