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Record W6949002931 · doi:10.5281/zenodo.1094864

An Early Stage Impact Study Of Localised Oer In Afghanistan

2017· article· en· W6949002931 on OpenAlexaffabout

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsCanadian Federation of University Women
Fundersnot available
KeywordsNonprobability samplingOpen educational resourcesQualitative researchDocumentationPlan (archaeology)Focus groupContext (archaeology)Subject (documents)

Abstract

fetched live from OpenAlex

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. Findings indicate that when the DDL was used by teachers, the OER accessed positively impacted teachers’ knowledge and helped them in lesson preparation. On average, the 33 teachers who visited the lab at least three times downloaded 12 OER each over the course of the study. However, a number of teachers did not download or use any OER, and many more preferred to continue using only the traditional textbook to prepare their lesson plans even after exposure to the DDL. Furthermore, while teachers found the OER helpful in creating assessment activities for their students, there was no observed improvement in teacher understanding and use of formative or summative assessment. Lastly, there was limited understanding among the teachers of the exact meaning of “open”, with most viewing OER as learning materials obtained from the internet, libraries or simply from outside of their school. Teachers made little reference to licensing or to the accessibility characteristics of OER. Thus, while teachers who used OER appeared to benefit from these resources, the concept was new to them, representing a disruption to the familiar way of preparing and delivering lessons. For further diffusion of OER as an innovation in teachers’ learning and practice, concerted action will be required to build the collection of OER available in Afghan languages, provide support in how teachers might integrate OER into their teaching, and ensure connectivity in the context of limited internet access in rural areas and a teacher population with widely varying levels of proficiency in using digital technology. The dataset arising from this study can be accessed at: https://www.datafirst.uct.ac.za/dataportal/index.php/catalog/622

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.130

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.002
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.056
GPT teacher head0.306
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes2
Has abstractyes

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