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Record W2229790666 · doi:10.5539/elt.v9n2p49

Strategies Training in the Teaching of Reading Comprehension for EFL Learners in Indonesia

2016· article· en· W2229790666 on OpenAlexvenueno aff
Junaidi Mistar, Alfan Zuhairi, Nofita Yanti

Bibliographic record

VenueEnglish Language Teaching · 2016
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsnot available
Fundersnot available
KeywordsReading comprehensionPsychologyMathematics educationAnalysis of covarianceTest (biology)Reading (process)Control (management)CovariateComprehensionLiteral (mathematical logic)Vocational educationPedagogyLinguisticsComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

<p>This study investigated the effect of reading strategies training on the students’ literal and inferential reading comprehension. The training involved three concrete strategies: predicting, text mapping, and summarizing. To achieve the purpose of this study, a quasi experimental design was selected with the experimental group being given reading strategies training and the control group being treated in a ‘business as usual’ mode. The subjects were students of two classes in a vocational senior high school in East Java, Indonesia (N=71). The students’ scores in the mid-semester teacher made test of English were used as the covariate to control possible initial differences between the two groups. Moreover, a test of reading comprehension was developed to measure the effect of the treatment. The analysis of covariance (ANCOVA) yielded that the experimental group outperformed the control group in both literal and inferential reading comprehension levels. Implications of this finding for classroom teaching are then discussed.</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)0.001
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 score0.141
Threshold uncertainty score0.428

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 studies0.0000.000
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 head0.025
GPT teacher head0.328
Teacher spread0.303 · 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

Citations32
Published2016
Admission routes1
Has abstractyes

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