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Record W2473533155 · doi:10.5539/elt.v9n8p122

Exploring the Construct of Learner Autonomy in Writing: The Roles of Motivation and the Teacher

2016· article· en· W2473533155 on OpenAlexvenueno aff
Marine Yeung

Bibliographic record

VenueEnglish Language Teaching · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyConstruct (python library)AutonomyLearner autonomyNature versus nurtureMathematics educationMetacognitionConstruct validityPedagogySocial psychologyDevelopmental psychologyLanguage educationPsychometricsCognition

Abstract

fetched live from OpenAlex

<p>Learner autonomy is widely recognized as a desirable educational goal in second language contexts. However, the lack of domain-specificity in research related to learner autonomy, compounded with the diverse views on its connotations, makes it difficult to either nurture or measure. This paper reports on a study that explored the construct of learner autonomy in the area of writing using quantitative data collected in the naturalistic settings of three secondary school classrooms in Hong Kong. In this study, learner autonomy was proposed as a construct consisting of autonomous attitudes including motivation, self-confidence and independence from the teacher, and autonomous skills embracing strategy use and metacognitive knowledge. A questionnaire was designed accordingly to measure changes in the participants after a writing programme that adopted the process writing approach, the potential of which in fostering traits of learner autonomy had been demonstrated in previous studies and was further explored in this study. Findings gathered through factor analysis on the questionnaire data, followed by a paired-sample t-test to investigate changes in the participants after the writing programme, suggest that a degree of independence from the teacher may possibly be a prerequisite for autonomy development in terms of writing skills, while motivation may have a more important role to play in its subsequent development.</p>

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.187
Threshold uncertainty score0.262

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.036
GPT teacher head0.231
Teacher spread0.195 · 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.

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

Citations18
Published2016
Admission routes1
Has abstractyes

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