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Record W2607200875 · doi:10.5539/elt.v10n5p104

Female Arab EFL Students Learning Autonomously Beyond the Language Classroom

2017· article· en· W2607200875 on OpenAlexvenueno aff
Mehtap Kocatepe

Bibliographic record

VenueEnglish Language Teaching · 2017
Typearticle
Languageen
FieldPsychology
TopicCommunication in Education and Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyClass (philosophy)FormalityMathematics educationLanguage acquisitionPedagogyEducational technologyDigital learningLinguisticsComputer science

Abstract

fetched live from OpenAlex

Benson’s (2011a; 2011b) identification of out-of-class learning as constituted by contexts, resources, levels of formality and more and less intentional pedagogic outcomes was used as a framework to investigate a group of tertiary level female Emirati EFL students’ autonomous out-of-class learning experiences. Data collected via a survey, learner journals and semi-structured interviews indicated that out-of-class use of English and out-of-class language learning played a significant role in the lives of students beyond the classroom. These students utilised naturally occurring material resources, in particular movies, television, the Internet and digital and print texts, with varying levels of pedagogic intentionality, in the privacy of homes and perceived such resources as conducive to facilitating language learning. Students created and utilised self-directed naturalistic learning opportunities more than self-instruction and naturalistic learning. The study found that exercising choice and being intrinsically motivated were integral to autonomous out-of-class learning. Discursive resources were influential in enabling or constraining recognition and utilisation of social resources in creating learning opportunities. The paper ends by giving recommendations to educators for creating effective support for autonomous out-of-class language learning.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.173
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0020.000
Research integrity0.0000.002
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 head0.026
GPT teacher head0.406
Teacher spread0.379 · 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 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
Published2017
Admission routes1
Has abstractyes

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