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Record W2590598280 · doi:10.5430/wje.v7n1p71

Analyzing ICT Policy in K-12 Education in Sudan (1990-2016)

2017· article· en· W2590598280 on OpenAlexvenueno aff
Adam Tairab, Ronghuai Huang

Bibliographic record

VenueWorld Journal of Education · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicGender and Technology in Education
Canadian institutionsnot available
Fundersnot available
KeywordsInformation and Communications TechnologyChristian ministryEducation policyPolitical scienceOrder (exchange)Information technologyHigher educationPedagogySociologyBusiness

Abstract

fetched live from OpenAlex

The aim of this study of ICT policy in K-12 education in Sudan is to investigate the status of planning for technologyin education and then determine how the advantage of ICT can best serve the educational system and improveeducational outcomes. The study examined two plans for ICT in education, addition to an interview with theeducational planning manager, and information center of federal ministry of general education, and other documentsfrom the ministry of education, as well as recommendations of previous studies which emphasized the need forpolicy to be compatible with other countries may face semi conditions of Sudan, and importance of compatible withUNESCO declarations (Incheon& Qingdao, 2015). The results of this study showed the need for policy emphasis onusing technology in education, K-12 education in Sudan requires better technology equipment, inclusive ICT policyincludes primary and secondary education need to formulate. The study also suggests that evaluation and assessmentare required in order to get more effective solutions and cope with the international educational progress of ICT inK-12 education.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.240
Threshold uncertainty score0.477

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0020.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.027
GPT teacher head0.393
Teacher spread0.366 · 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

Citations26
Published2017
Admission routes1
Has abstractyes

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