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Record W2887183844 · doi:10.5430/ijhe.v7n4p76

Perceptions of ELT Students Related to Learner Autonomy in Language Learning

2018· article· en· W2887183844 on OpenAlexvenueno aff
Gokhan Yigit, Özgür Yıldırım

Bibliographic record

VenueInternational Journal of Higher Education · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsLearner autonomyAutonomyPerceptionPsychologyQualitative researchMathematics educationLanguage acquisitionPedagogyQualitative propertyLanguage educationComputer scienceSociologyPolitical scienceComprehension approach

Abstract

fetched live from OpenAlex

This study mainly aims to investigate the perceptions of ELT (English Language Teaching) students related to learner autonomy in language learning. In order to have a deeper understanding of the perceptions of ELT students related to learner autonomy a total of 20 students are interviewed. The findings gathered are analyzed according to the year of study in the ELT program (from 1st year to 4th year) and gender of the participants. Content analysis is done for the qualitative data and the findings of the qualitative data are organized according to the students’ study years and their gender. The findings indicate that the participants mainly state similar responsibilities, abilities, and activities in terms of their year of study. In addition, qualitative data reveal that although both female and male participants give importance to similar aspects related to responsibilities, abilities, and activities on learner autonomy, they state different reasons for giving importance to those aspects. Additionally, participants give some definitions related to learner autonomy and they prove how versatile the notion of learner autonomy is.

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)0.003
metaresearch head score (Gemma)0.008
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 score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0030.001
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.014
GPT teacher head0.362
Teacher spread0.348 · 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

Citations7
Published2018
Admission routes1
Has abstractyes

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