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Record W4285310720 · doi:10.53103/cjlls.v2i1.23

Investigating Kurdish EFL Students’ Attitudes towards the Use of Authentic Materials in Learning English

2022· article· en· W4285310720 on OpenAlexvenueno aff
Sami Hussein Hakeem Barzani, Israa Hasko Azeez Barzani, Reman Sabah Meena

Bibliographic record

VenueCanadian Journal of Language and Literature Studies · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyCommunicative competenceCompetence (human resources)Communicative language teachingEnglish languageLanguage acquisitionMathematics educationPedagogyAuthentic learningLanguage educationSocial psychology

Abstract

fetched live from OpenAlex

A bulk of research and numerous experts in the field of language pedagogy endorse the idea of utilization of the authentic materials in second language teaching.Authentic resources offer several benefits to the learners, including motivation and interest in language learning, as well as the improvement of communicative competence.This study meant to explore the attitudes of Kurdish EFL university students about the use of authentic materials in learning English.To address its aim, the study used a quantitative research design in which data were collected using a questionnaire.150learners (68 males & 82 females) comprised the study participants and the collected data were analyzed using SPSS.The results showed that the overwhelming majority of the respondents have a positive attitude towards authentic materials.The findings further uncovered that most of the participants are of the idea that authentic materials assist them to learn the language better and faster especially the communicative aspects of the language.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.047
GPT teacher head0.273
Teacher spread0.226 · 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 designQualitative
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

Citations8
Published2022
Admission routes1
Has abstractyes

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Same venueCanadian Journal of Language and Literature StudiesSame topicSecond Language Learning and TeachingFrench-language works237,207