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Record W3045202400 · doi:10.5539/ies.v13n8p55

The Whole Grade Acceleration Policy in the Kingdom of Saudi Arabia and the State of Massachusetts, USA—An Analytical Comparative Study

2020· article· en· W3045202400 on OpenAlexvenueno aff
Abdulhamid Alarfaj, Reem Abdul Latif Al-Omair

Bibliographic record

VenueInternational Education Studies · 2020
Typearticle
Languageen
FieldPsychology
TopicEducation, Achievement, and Giftedness
Canadian institutionsnot available
Fundersnot available
KeywordsAccelerationSample (material)Mathematics educationState (computer science)Political sciencePsychologySociologyComputer sciencePhysics

Abstract

fetched live from OpenAlex

The research aims, through a comparative analytical study, to unveil whether there is an actual whole-grade acceleration policy in the kingdom of Saudi Arabia (KSA) if compared with that applied at Massachusetts, USA. Adopting such a policy secures the right of the gifted student to grow academically in proportion with his peculiar potentials. The research adopts the comparative analytical method (qualitative) using two tools: document analysis and semi-structured interview. The sample of the study comprised two education supervisors in charge of acceleration system in the department of the gifted in KSA and school principals applying the system in the state of Massachusetts. The foremost result, which the study came up to, was that the Saudi educational system has a comprehensive written acceleration policy based on scientific frameworks, while Massachusetts doesn’t have a specific document for applying a comprehensive acceleration policy. The research concluded with some recommendations among which are: The comprehensive acceleration policy in KSA still needs to develop, especially in the following areas: Classes and study levels on which the acceleration system and guidance services are applied, and The need to review acceleration procedures as they are among the obstacles that hinder an active application of the policy at the present time.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.309
Threshold uncertainty score0.258

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.216
GPT teacher head0.498
Teacher spread0.282 · 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 teacher head, 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
Published2020
Admission routes1
Has abstractyes

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