Designing Curricula for an Uncertain World: A Critical Action Learning Approach
Bibliographic record
Abstract
In this paper, we focus on engaging student educational designers in critical action learning and design.While ongoing efforts have investigated how to build capacity amongst schoolteachers, we argue for building capacity amongst student educational designers who will be engaged in designing resources for schoolteachers and students.This research builds on theories of critical pedagogy and critical action learning.We follow a design-based research approach to iteratively design a critical action learning toolbox that can be used to foster a generation of designers capable of producing educational technologies that prioritize equity and social responsibility.Findings suggest that adopting a layered approach -CALE researcher/educator (Layer 0), student educational designer (Layer 1), and schoolteacher (Layer 2) -provide the required reflective space to design curricula using critical action learning approach.Scaffolds such as designing a collective problem case and emergent mirrors are important constituents in this reflective space.
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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 | 0.031 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.006 | 0.036 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".