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Record W2626467960 · doi:10.5539/elt.v12n7p75

Fostering Metacognitive Reading Strategies in Thai EFL Classrooms: A Focus on Proficiency

2019· article· en· W2626467960 on OpenAlexvenueno aff
Charinwit Seedanont, Suphawat Pookcharoen

Bibliographic record

VenueEnglish Language Teaching · 2019
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsnot available
Fundersnot available
KeywordsMetacognitionPsychologyReading comprehensionReading (process)Mathematics educationTest (biology)Affect (linguistics)ComprehensionCognitionPedagogyLinguistics

Abstract

fetched live from OpenAlex

EFL readers tend to experience a number of challenges while learning, due to a number of factors that affect how these readers achieve their learning goals. Metacognitive strategies, referring to one’s deliberate, goal-directed control over cognitive enterprises, are considered crucial for assisting EFL learners to be able to accomplish comprehension while reading. Previous studies have enriched the knowledge of metacognitive reading strategies in EFL settings. However, only few investigations yielded statistically significant effects on learners’ reading performance. This present study hence foresees an opportunity to shed new light on this issue by focusing on EFL learners’ proficiency. The objectives of this research are twofold: exploring the effects of the metacognitive strategy instruction on the strategy awareness, and perceiving the effects of the instruction on the reading performance in taking a standardized test. Forty-three students enrolling in a private male school in Bangkok, Thailand participated in the study, lasting ten weeks. A wide range of research tools were administered: SORS, IELTS reading test, and lesson plans. The findings suggested that the students’ awareness of reading strategies used in terms of sub-categories and IELTS reading test score improved with statistical significance. Pedagogical implications and suggestions for future research studies are discussed based on the findings.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.413
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.374
Teacher spread0.342 · 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

Citations6
Published2019
Admission routes1
Has abstractyes

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