Framing, Reframing, and Teaching: Design Decisions Before, During and Within a Project-based Unit
Bibliographic record
Abstract
This design case follows the instructional planning and decision making before and during a nine-week project-based unit co-taught by three of the authors at a not-for-profit charter high school in the American Southwest. The school serves students who have not been well served by traditional schooling. The teachers partner with industry professionals to create authentic learning projects. The project detailed in this design case aimed to have students design and test temporary shelters for people who were homeless, and later learn about semi-permanent shelters. Students consistently sought to design longer-term solutions to homelessness, rather than shorter-term solutions for individual homeless clients. An embedded researcher documented project implementation and design conversations—including formal planning prior to and emergent conversations during teaching. Analysis of these conversations reveals that many design decisions made prior to instruction were guided by learning objectives, constraints and opportunities, whereas those made during teaching practice were focused directly on supporting learning. Analysis also made clear that students played a role in steering the project to focus more on solutions to long-term homelessness; based on student interests, the final project included writing letters to government representatives about their ideas for solutions. The design case concludes with reflections by the teachers on the design decisions and attendant learning. This case helps to clarify how context and timing influenced design decisions and provides an exemplar of teacher-designed complex instruction, illustrating how learners might take part in reframing a problem about which they are learning.
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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 | 0.040 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| 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".