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Record W2791880172 · doi:10.5861/ijrsll.2018.2005

The impact of effective reading strategy instruction on EFL learners

2018· article· en· W2791880172 on OpenAlexaff
Mojtaba Bozorgian, Mehdi Aalaam

Bibliographic record

VenueInternational Journal of Research Studies in Language Learning · 2018
Typearticle
Languageen
FieldComputer Science
TopicEducational Methods and Media Use
Canadian institutionsBrock University
Fundersnot available
KeywordsReading (process)PsychologyMathematics educationPedagogyLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

This study investigated the relationship between Iranian elementary EFL learners' reading strategy instruction and their performance on different reading comprehension test types.In addition, it studied the differences among these learners' reading strategy preferences based on their personality types.A total of 60 Iranian EFL learners at the elementary level within the range of 15-30 years old were selected from among 100 participants.The participants were then divided into two groups of control (n=30) and experimental (n=30).Subsequently, reading strategies were taught to the experimental group during ten sessions.The findings indicated that reading strategies instruction had a significant impact on reading comprehension of the participants in the experimental group at the end of the treatment.These participants of different personality types did better in posttest of CL-test and multiple-choice test.Additionally, analysis of the results showed that participants tended to use metacognitive reading strategies more than cognitive and support strategies.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
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 integrity0.0000.000
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 head0.096
GPT teacher head0.558
Teacher spread0.462 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2018
Admission routes1
Has abstractyes

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