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Communication Strategies Used by Jordanian EFL Learners

2012· article· en· W1520090568 on OpenAlexvenueno aff
salah nimer abunawas

Bibliographic record

VenueCanadian social science · 2012
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsMeaning (existential)Competence (human resources)PsychologyRepertoireCommunicative competenceComputer scienceLinguisticsMathematics educationPedagogySocial psychology

Abstract

fetched live from OpenAlex

Due to long academic experience, it was noticed when some EFL learners encounter a problem in verbal communication in TL, they tend to employ different techniques. They may abandon the message, alter the meaning they intend to convey, omit some items of information, make their ideas simpler and less precise, or say something which is slightly different from the intended meaning. When a learner is able to anticipate such a communication problem, he may overcome it by avoiding communication or modifying what he intends to say. If the problem arises while the learner is already engaged in speaking, he may try to find an alternative way of getting the meaning across. The researcher witnessed various types of communication strategies used by learners in their interaction and performing tasks via English. This actual observation motivated the researcher to investigate the communication strategies (CSs) employed by EFL learners in communicating with others, e.g. their classmates and instructors. This observation is also in line with what other researchers (e.g., Littlewood, 1984; Poulisse, 1987) have noticed that EFL learners who venture to put their knowledge into practice often run into communication problems due to deficiencies in their linguistic repertoire. The present study deals with CSs and the proficiency level of 66 Jordanian students at Zarka University. Key words: Jordanian learners; Communication strategies; Strategic competence

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.820
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.041
GPT teacher head0.276
Teacher spread0.235 · 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
Published2012
Admission routes1
Has abstractyes

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