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Record W3012455455 · doi:10.5539/ijel.v10n3p14

Teaching Grammar: Professional Needs of Saudi EFL Instructors

2020· article· en· W3012455455 on OpenAlexvenueno aff
Anas Almuhammadi

Bibliographic record

VenueInternational Journal of English Linguistics · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsGrammarMathematics educationEnglish grammarTeaching methodPsychologyCommunicative language teachingComputer sciencePedagogyLanguage educationLinguistics

Abstract

fetched live from OpenAlex

Grammar teaching has been a long tradition in EFL instruction in various parts of the world and Saudi Arabia is no exception to this. However, various approaches to teaching grammar have emerged over a period of time. For this, professional development (PD) programs are designed to meet the EFL teachers’ needs by enabling them to use a range of approaches and techniques. To do this successfully, professional needs analysis of teachers is essential. The present study investigates the beliefs of teachers regarding the use of various teaching approaches for grammar teaching and their need for professional development (PD). Questionnaire survey was conducted among 50 randomly chosen EFL teachers at a public sector university. The results showed that EFL teachers deem grammar as a foundational framework for teaching English as a foreign language. Furthermore, grammar is thought to be a major factor in developing accuracy and correct use of EFL. Moreover, the teachers have the theoretical knowledge of various grammar teaching methods using TBL, PBL and CLT. However, they need to develop practical skills for grammar instruction. Thus, the study recommends that the universities in Saudi Arabia need to arrange regular PD programs so that the EFL teachers with modern methods to teach English grammar successfully.

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)0.000
metaresearch head score (Gemma)0.043
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.959
Threshold uncertainty score0.965

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.043
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.028
GPT teacher head0.281
Teacher spread0.253 · 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.

Study designNot applicable
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

Citations17
Published2020
Admission routes1
Has abstractyes

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