The Relationship between the Emotional Intelligence and Reading Comprehension of Iranian EFL Impulsive vs. Reflective Students
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".