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Record W3138662332 · doi:10.23977/aetp.2021.51004

The degree of using the smart board in providing students with planning skills to teach Arabic language and their attitudes towards it among the three stages students in Kuwait

2021· article· en· W3138662332 on OpenAlexvenueno aff
Sultan Demaitheer Mansour Alanezi

Bibliographic record

VenueAdvances in Educational Technology and Psychology · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Technology Integration
Canadian institutionsnot available
Fundersnot available
KeywordsArabicMathematics educationProcess (computing)PsychologyMedical educationComputer sciencePedagogyEngineeringMedicineLinguistics

Abstract

fetched live from OpenAlex

With the technological advances that have revolutionized the different fields, the educational processes have been influence too. Smart board is considered as one of the promising approaches to enhance the educational process as approved by many studies. The aim of the current study is to examine the degree of using the smart board in providing students with planning skills to teach Arabic language and their attitudes towards it among the three stages students in Kuwait. This study has used the descriptive analytical approach to fulfill the aims of the study. A validated questionnaire was distributed on (90) students from the three stages in Kuwaiti public schools where the means and standard deviations for their answers were calculated. The results of the current study revealed that the smart board is used in a high degree in providing students with planning skills to teach Arabic language. The results also showed that the students have a positive and good attitude toward using smart board in teaching Arabic language. This study recommends involving smart board in wider classroom management skills and applies such scales on different samples including administrators and teachers.

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 score0.073
Threshold uncertainty score0.956

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.034
GPT teacher head0.432
Teacher spread0.399 · 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
Published2021
Admission routes1
Has abstractyes

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