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Record W4205559485 · doi:10.5539/hes.v12n1p60

A Suggested Proposal to Develop Distance Learning Programs in Border Schools in the Kingdom of Saudi Arabia

2022· article· en· W4205559485 on OpenAlexvenueno aff
Noura H. Al Sorour, Mohamed El-Hussein

Bibliographic record

VenueHigher Education Studies · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicSocioeconomic Development in MENA
Canadian institutionsnot available
Fundersnot available
KeywordsDistance educationKingdomSample (material)Developing countryMedical educationMathematics educationSociologyPolitical sciencePsychologyMedicineEconomic growth

Abstract

fetched live from OpenAlex

The aim of this research is to find a proposed vision for developing distance education programs in border schools in the Kingdom of Saudi Arabia, and to explore the reality of the proposed educational programs for developing distance education programs. In order to achieve the research objectives, the descriptive and analytical approach was used for its suitability for this research, as the questionnaire was used as a research tool. The research sample consisted of (150) female teachers from border schools. The results of the research revealed that the reality of female teachers ’practice on distance learning programs in border schools in the Kingdom of Saudi Arabia is central. And that the use of technology is the most important requirement for developing distance education programs in border schools from the Kingdom of Saudi Arabia. The results also showed the achievement of leadership in diversifying and developing teaching and learning methods through the distance education system based on employing modern information and communication technology, equipping schools with all technological equipment, and supporting students with modern equipment and teachers with modern training to achieve the goals of distance education. In light of the results, the research paper presented a proposal for developing distance education programs in border schools in the Kingdom of Saudi Arabia.

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)0.002
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 score0.381
Threshold uncertainty score0.341

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.073
GPT teacher head0.395
Teacher spread0.323 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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