An Early Stage Impact Study Of Localised Oer In Afghanistan (Advance Online Publication)
Bibliographic record
Abstract
This study evaluates a group of Afghan teachers’ use of Open Educational Resources (OER) from the Darakht-e Danesh Library (DDL) – a digital library comprised of educational materials in English, Dari and Pashto – investigating whether these resources enabled improvements in teaching practice and led to improved subject knowledge. Conducted with secondary-school teachers in Parwan, Afghanistan, who accessed the DDL over a four-week period in 2016, the study asked the following research questions: To what extent did teachers in this study access and use OER in the DDL? Did access and use of OER in the DDL enhance teachers’ subjectarea content knowledge? Did access and use of DDL resources enhance teachers’ instructional practices? To what extent did teachers’ understanding of OER and its value change? The study utilised quantitative and qualitative methods to examine the behaviour and practices of 51 teachers in rural Afghanistan, all of whom were teaching at the secondary level or affiliated with a local teacher training college. The study collected data from server logs, pre- and post-treatment questionnaires, lesson plan analyses, teacher interviews and classroom observation. A purposive sampling technique was utilised to select the teachers, drawing from educational institutions with which the Canadian Women for Women in Afghanistan non-governmental organisation had previously interacted.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".