MétaCan
Menu
Back to cohort
Record W1603372852 · doi:10.5539/elt.v8n6p178

Korean Learners’ Metacognition in Reading Using Think-Aloud Procedures with a Focus on Regulation of Cognition

2015· article· en· W1603372852 on OpenAlexvenueno aff
Hyang-Il Kim, Kyung-Ae Cha

Bibliographic record

VenueEnglish Language Teaching · 2015
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsnot available
Fundersnot available
KeywordsMetacognitionPsychologyCognitionReading (process)Think aloud protocolFlexibility (engineering)Cognitive psychologyCognitive strategyProtocol analysisSelf-regulated learningScale (ratio)Mathematics educationCognitive scienceLinguisticsComputer science

Abstract

fetched live from OpenAlex

The primary goal of this study was to explore the changes that four Korean university students made in their regulation of cognition during reading processes. The students were trained using explicit reading strategy instruction based on the CALLA model. To this end, first, metacognition was framed and categorized by the definition from Baker and Brown (1984) and, second, a scoring scale for measuring the readers’ regulation of cognition was developed based on the study by Block (1992) to examine and trace any changes in their regulation processes. For data analyses, the participants’ think-aloud protocols were used. The results indicate that there were marked changes in the frequencies of their regulation processes over time. Specifically, the students’ overt strategic and regulatory behaviors in a regulation process showed more flexibility and organization toward the end of the strategy training. This study suggests that students would benefit from being provided with sufficient time for practice in order to build effective regulation of cognition in reading processes and that the teacher should understand the complex nature of the regulation processes that students go through. In addition, think-aloud procedures as an instructional tool for effective strategy training was shown to be a worthwhile technique in the classroom.

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)0.003
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 score0.079
Threshold uncertainty score0.769

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
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 integrity0.0000.001
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 head0.049
GPT teacher head0.358
Teacher spread0.309 · 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

Citations6
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueEnglish Language TeachingSame topicInnovative Teaching and Learning MethodsFrench-language works237,207