MétaCan
Menu
← Back to cohort
Record W2791034030 · doi:10.5539/mas.v12n4p13

A Proposed Future Vision for Improving the Virtual Learning Culture in Jordanian Schools

2018· article· en· W2791034030 on OpenAlexvenueno aff
Lina Kleaf AlQallab, Mohammad Saleem Al-Zboon

Bibliographic record

VenueModern Applied Science · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsPoint (geometry)Mathematics educationSample (material)Virtual realitySociologyPsychologyPersonalityComputer scienceMedical educationPedagogyArtificial intelligenceMathematicsSocial psychologyMedicine

Abstract

fetched live from OpenAlex

The present study aimed to identify the future vision for developing the culture of virtual education in Jordanian schools by identifying the reality of the virtual education culture and the difficulties in applying this type of education.The study sample consisted of (2000) teachers and teachers representing all the directorates of education in Jordan, and were selected in the random stratified manner.The results showed that the reality of the virtual education culture in Jordanian schools from the point of view of the primary stage teachers in Jordan was high and that the difficulties facing virtual education in Jordan were high. Based on the results, the paper recommended to Bringing up people who accept the culture of change and adapt to it which shall enable them to seek achieving their ambitions and develop their potentials. and Promoting a culture that is based on a scientific methodology and employing people’s mental skills and scientific methods to find practical solutions for societal problems. The vision’s outlines include developing a personality that is capable to reach knowledge through using various sources of information.

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)0.009
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0040.003
Scholarly communication0.0110.008
Open science0.0020.005
Research integrity0.0030.003
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 head0.010
GPT teacher head0.306
Teacher spread0.296 · 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 designTheoretical or conceptual
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
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueModern Applied Science→Same topicOnline and Blended Learning→French-language works237,207→