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Record W2955905209 · doi:10.5539/elt.v12n8p30

Promoting Learner Autonomy Through Cooperative Learning

2019· article· en· W2955905209 on OpenAlexvenueno aff
Weixuan Shi, Ligang Han

Bibliographic record

VenueEnglish Language Teaching · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsLearner autonomyPsychologyAutonomyLanguage acquisitionPedagogyCompetence (human resources)Foreign languageIntercultural competenceLanguage educationMathematics educationSocial psychologyComprehension approach

Abstract

fetched live from OpenAlex

Learner autonomy has become a hot topic and goal in the research of foreign language education. However, it is the most difficult question to define language learner autonomy and any answer to it is likely to be subjective. On the basis of expounding upon the different definitions concerning the research on learner autonomy in language teaching and learning, this study was to explore how cooperative group learning helps to improve learner autonomy. The survey’s findings indicate that the group work helps to improve students’ learning attitude, interest and motivation. It also reveals that students’ language competence and awareness of using learning resources are improved. This article discusses plausible explanations for the survey findings and makes recommendations on the roles and knowledge that language teachers should play and have to facilitate the development of learner autonomy.

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)0.008
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.019
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0010.008
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 head0.012
GPT teacher head0.244
Teacher spread0.232 · 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 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

Citations35
Published2019
Admission routes1
Has abstractyes

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