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Record W3202961082 · doi:10.5539/ies.v14n10p77

Educational Change in Saudi Arabia: Insights from One USA/KSA Teacher Professional Development Collaborative

2021· article· en· W3202961082 on OpenAlexvenueno aff
Adil Bentahar, Kathleen Copeland, Scott G. Stevens, Carol Vukelich

Bibliographic record

VenueInternational Education Studies · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicTeacher Education and Leadership Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMindsetProfessional developmentFaculty developmentPedagogyMedical educationChristian ministrySociocultural evolutionCulture changePsychologyTeacher educationMultimethodologySociologyPolitical scienceMedicineSocial science

Abstract

fetched live from OpenAlex

Teacher professional development (PD) programs ideally evaluate how professional learning experiences empower teachers to be effective change agents in their disciplines and communities. The Khbrat [“experiences” in Arabic] program is a year-long, global teacher PD initiative launched by the Saudi Ministry of Education. The goal is to change the mindset of Saudi teachers through immersive experiences in the U.S. K-12 schools and university academic culture so that they can participate as effective “change agents” in the transformation of Saudi schools. Our mixed-methods study examined the impact of the Khbrat program on Saudi teachers’ leadership, classroom experiences, and sociocultural levels; the findings inspire new directions for program design with key insights into teacher PD program evaluation.

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)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score0.165

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0130.004
Scholarly communication0.0070.003
Open science0.0010.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.257
GPT teacher head0.483
Teacher spread0.226 · 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 designQualitative
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

Citations6
Published2021
Admission routes1
Has abstractyes

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