Informal Practices of Localizing Open Educational Resources in Ghana
Bibliographic record
Abstract
Research on the use of Open Educational Resources (OER) often notes the potential benefits for users to revise, reuse, and remix OER to localize it for specific learners. However, a gap in the literature exists in terms of research that explores how this localization occurs in practice. This is a significant gap given the current flow of OER from higher-income countries in the Global North to lower-income countries in the Global South (King et al., 2018). This study explores how OER from one area of the world is localized when it is used in a different cultural context. Findings indicated complex encounters with decontextualized content and a variety of localization practices. Participants experienced challenges with technology due to low bandwidth and hardware problems, as well as language problems given Ghana’s history of colonial rule. Native speakers of Twi are less proficient reading Twi than their national language, English. As facilitators worked to overcome these challenges, they were most likely to informally localize content in intuitive ways during the class based on students’ needs. Informal, in-the-moment practices included translating content into Twi, persisting through technological challenges, using local stories and pictures, localizing through discussion, and teaching responsively. These findings have implications for designers to design collaboratively with technological and linguistic flexibility for localization. More research on the practice of OER localization would refine our understanding of how OER is localized and what barriers and affordances exist to this practice.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".