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Record W2997760220 · doi:10.5539/ies.v13n1p32

The Use of Modern Teaching Methods in Teaching Arabic Language at Higher Education Phase from the Point View of Arabic Language Professors—A Case of a Premier University

2019· article· en· W2997760220 on OpenAlexvenueno aff
Nahla A. K. Alhirtani

Bibliographic record

VenueInternational Education Studies · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicArabic Language Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsArabicMathematics educationPoint (geometry)Teaching methodHigher educationLanguage educationPsychologySemitic languagesComputer sciencePedagogyLinguisticsMathematicsPhilosophyPolitical science

Abstract

fetched live from OpenAlex

The study aimed to identify the use of modern teaching methods at the higher education phase for Arabic language professors- a case of a Premier University. The researcher used the analytical descriptive method, based on the survey method and she has prepared a tool that includes 42 responses. The sample was 27 lecturers and the recovery rate was 88%. The data analysis showed that there is approval of the use of modern teaching methods among the lecturer by 83.6%. The results showed that the method of the lecture is the most common while the least common in the laboratory. As for the justifications, a large number of students, the difficulty of controlling them and the inability of lecturers to apply some methods in practice are among the most common reasons for not using some modern teaching methods.

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)0.005
metaresearch head score (Gemma)0.008
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.003
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
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 head0.113
GPT teacher head0.495
Teacher spread0.382 · 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

Citations23
Published2019
Admission routes1
Has abstractyes

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