Emotional Intelligence and its Relationship to Academic Performance Among Saudi EFL Undergraduate Students
Bibliographic record
Abstract
AbstractThe purpose of the present study was to provide a description of the emotional intelligence level of Saudi EFL undergraduate students, as well as to examine the effect of emotional intelligence on success in foreign language learning. A total of 80 Saudi undergraduate students from the English Department at King Khalid University participated in this study. Data was collected by means of Schutte Self Report Emotional Intelligence Test (SSEIT), and by an English achievement test. SSEIT questionnaire data was matched with the students’ academic scores in the English language achievement test, and was analysed using SPSS. The findings indicated that Saudi EFL students scored a high level of emotional intelligence. The most popular intelligence subscales they used were “Utilization of Emotion” followed by “Management of Others Emotion” and “Management of Self Emotion” and finally, “Perception of Emotion”. Another finding indicated that two of the four subscales, “Utilization of Emotion and “Management of Others Emotion” were significantly associated with their English achievement level. The implications of the value of emotional intelligence in fostering academic achievement were considerable for both EFL teachers and academic policy makers.
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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 | 0.002 |
| 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 | 0.000 |
| 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".