Emotional Intelligence and Language Competence: A Case Study of the English Language Learners at Taif University English Language Centre
Bibliographic record
Abstract
Variation in general abilities of human beings gave birth to the concept of intelligence. Since 1990, when for the first time emotional intelligence was introduced, it has become a buzzword in many fields including education, management studies, and artificial intelligence. Within the context of foreign language learning, it is being applied in educational institutions for language competence. An empirical study was conducted on English language learners at Taif University English Language Centre (TUELC) to find out relationship between their Emotional Intelligence (EI) and language competence. For this study, a group of 200 (male and female) students were selected randomly studying English at the undergraduate level. Data collected through EI Inventory was matched with their academic achievement in English language based on assessment of four skills. The result revealed a close relationship between EI and language competence of undergraduate students at TUELC and EI also affects students’ English language competence.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".