English as a Foreign Language Teacher Flow: How Do Personality and Emotional Intelligence Factor in?
Bibliographic record
Abstract
Teaching is one of the professions that creates opportunities for individuals to experience flow, a state of complete absorption in an activity. However, very few studies have examined ESL/EFL teachers’ flow states inside or outside the classroom. As such, this study aimed to explore the quality of experience of 75 EFL teachers in flow and also examine the relationships between their emotional intelligence, the Big Five personality traits and the flow state. To this end, the teachers filled out recurrent flow surveys for a week, and also completed emotional intelligence and the Big Five personality questionnaires. It was found that reading was the major flow trigger outside the classroom and teaching and delivering lessons was the most significant flow-inducing activity for the teachers inside the classroom. Furthermore, correlations and independent samplest-tests indicated that all emotional intelligence and personality traits had significant relationships with flow except agreeableness. Finally, multiple regression analysis showed that two personality traits, conscientiousness and openness to experience were the strongest predictors of the flow state. The implications for future flow-related research in the field of applied linguistics are discussed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".