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Record W3113540703 · doi:10.5296/ijl.v12i6.17859

Functions of L1 Use in EFL Classes: Students’ Observations

2020· article· en· W3113540703 on OpenAlexaff
Muath Algazo

Bibliographic record

VenueInternational Journal of Linguistics · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsWestern University
Fundersnot available
KeywordsPsychologyPraiseGrammarPronunciationClass (philosophy)Mathematics educationVocabularyForeign languageReading (process)ArabicPedagogyLinguisticsComputer science

Abstract

fetched live from OpenAlex

This study aims to identify functions (Note 1) of teachers’ first language (L1) use in English as a foreign language (EFL) classes in public schools in Jordan based on students’ observations. The questionnaire items were selected based on the most common uses of L1 in second language (L2) classrooms as identified in the literature. The questionnaire was designed to elicit students’ observations to identify functions of EFL teachers’ L1 in English classes. The participants were 104 EFL students in Grades 10 and 11 in four Jordanian public schools. Participants’ responses to the questionnaire were analysed quantitatively using SPSS, a statistical software package. The study found that the students observed that their teachers shared their L1 with them in English classes to: 1) Explain complex grammar points, 2) Define some new vocabulary items, 3) Explain difficult concepts or ideas, 4) Give instructions, 5) Praise the students, 6) Translate the reading texts, and 7) Explain the similarities and differences between Arabic and English in terms of grammar, structure or pronunciation. However, the students did not observe that their teachers used the L1 in order to maintain discipline in the class as previous studies have found. The findings suggest that teachers’ L1 use in the L2 classroom may indicate the usefulness of this practice and call to license EFL teachers to use their L1 in English classes in public school in Jordan and other similar EFL contexts.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.937
Threshold uncertainty score0.501

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
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 integrity0.0000.000
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 head0.172
GPT teacher head0.339
Teacher spread0.167 · 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 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

Citations3
Published2020
Admission routes1
Has abstractyes

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