MétaCan
Menu
Back to cohort
Record W4283641338 · doi:10.5539/elt.v15n7p149

A Study on Developing Learner Autonomy Through the Reading Circle Method

2022· article· en· W4283641338 on OpenAlexvenueno aff
Ligang Han

Bibliographic record

VenueEnglish Language Teaching · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
FundersFundamental Research Funds for the Central Universities
KeywordsLearner autonomyAutonomyReading (process)PsychologyLanguage acquisitionScope (computer science)PedagogyMathematics educationExtensive readingTeaching methodEmpirical researchLanguage educationLinguisticsComprehension approachComputer scienceEpistemologyPolitical science

Abstract

fetched live from OpenAlex

Many countries in the world have regarded the cultivation of learner autonomy as one of the important goals of language teaching. During the past fifty years, researchers and scholars have explored the connotations of learner autonomy, and carried out some empirical researches in different contexts. There are more and more researches on learner autonomy in language teaching and learning, the research scope and content are constantly broadened and deepened, and the research methods are more diversified. Based on the review of the reading circle method and the discussion of a working definition for learner autonomy, the present study explored the application of the reading circle method in facilitating the cultivation of learner autonomy in the English for Academic Purposes course at a comprehensive university in China. The results showed that the reading circle could help to improve learner’s attitude and interest in English language learning. It also indicated that learner’s learning capabilities and strategies were improved. This study adopts a novel approach to foster the development of learner autonomy, and sheds light on the empirical research practice.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity, 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 score0.218
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.034
GPT teacher head0.301
Teacher spread0.267 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueEnglish Language TeachingSame topicSecond Language Learning and TeachingFrench-language works237,207