The Aesthetics of OER, Deaf Pedagogy, and Curriculum Design Contra the “Wicked” Policy of Deaf Education
Bibliographic record
Abstract
Designing inclusive education for deaf learners is a complex dilemma affecting multiple spheres and agents. In the US and Canada, despite considerable work by students, parents, educators, school administrators, curriculum developers, and lawmakers to address education policy about deaf bilingual literacy, the provision of deaf education through open access educational resources is a wicked problem exacerbated by gaps in curriculum and pedagogy. Despite increasingly hypermodern technologies and mandated early assessment, most deaf high schoolers in North America have unsatisfactory literacy skills (Qi & Mitchell, 2012). To better manage this “wicked problem,” involving policy, pedagogical methods, and curriculum design, we explore how aesthetic forms of knowledge and deaf positive design operations are used in conjunction with Open Educational Resources (OER). We reviewed the literature and constructed a novel framework about OER and e-books in deaf education. The synthesis generated three key takeaways that assisted our understanding of the complex issue. We presented our new framework alongside structured questions to 382 attendees hailing from 20 nations at the WUN/UNESCO Conference (2021, October), focused on inclusive and open access education technologies. We empirically analyzed this rich corpus using qualitative coding and represented our findings using a multipart Ecocycle Model. Following basic analysis, we describe four broader implications for deaf education research about teaching and curriculum using OER and e-book materials. Our analysis shows that deaf curriculum design is an educational problem embedded in a larger policy debate concerning methods and philosophies of pedagogy.
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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 | 0.017 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.074 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".