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Record W2556967290 · doi:10.5539/ijel.v6n6p221

The Relationship between the Emotional Intelligence and Reading Comprehension of Iranian EFL Impulsive vs. Reflective Students

2016· article· en· W2556967290 on OpenAlexvenueno aff
Amir Reza Nemat Tabrizi, Leila Esmaeili

Bibliographic record

VenueInternational Journal of English Linguistics · 2016
Typearticle
Languageen
FieldPsychology
TopicEmotional Intelligence and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsReading comprehensionPsychologyEmotional intelligenceTest (biology)Reading (process)ComprehensionMathematics educationDevelopmental psychologyLinguistics

Abstract

fetched live from OpenAlex

<p>The present article aimed at investigating the relationship between the emotional intelligence and reading comprehension of Iranian EFL impulsive and reflective students. To do so, 121 students based on a PET test were selected. Then, they answered a translated version of emotional intelligence and did a reading comprehension test. Later, they filled out impulsiveness questionnaire reflectiveness questionnaire. The results of these questionnaires and the reading test were compared. The first finding of the research revealed that there was a significant relationship between Iranian EFL learners’ emotional intelligence and their reading comprehension. Based on the next result, it was concluded that there was significant relationship between impulsive Iranian EFL learners’ emotional intelligence and their reading comprehension. On the other hand, there wasn’t such relationship between reflective Iranian EFL learners’ emotional intelligence and their reading comprehension. The last finding indicated that the Iranian impulsive EFL female students who possessed more degrees of emotional intelligence outperformed reflective students on reading comprehension. The findings of the research could be employed by EFL teachers, educational researchers, and English learners in an attempt to develop a more learner-centered method of second language reading comprehension.</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)0.001
metaresearch head score (Gemma)0.029
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.029
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 science0.0010.000
Research integrity0.0000.000
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 head0.073
GPT teacher head0.403
Teacher spread0.331 · 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 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

Citations8
Published2016
Admission routes1
Has abstractyes

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