Trait Emotional Intelligence and University Graduation Outcomes
Bibliographic record
Abstract
This study explored the utility of trait emotional intelligence (EI) for predicting students’ university graduation outcomes six years after enrolment in university. At the start of the program, 1,015 newly registered students completed a brief multidimensional self-report EI assessment and provided consent to track their subsequent degree progress via official university records. Using latent profile analysis (LPA), participants were sorted into five classes that differed in the overall EI level and in the relative strengths and weaknesses on individual EI dimensions. Greater likelihood of degree noncompletion at the 6-year follow-up was uniquely associated with having a low-EI profile with particularly pronounced weaknesses in the interpersonal and stress management domains, after controlling for high school grades and gender. Comparative levels of predictive utility could not be achieved by examining scores on each EI dimension independently. Authors discuss practical advantages of LPA over traditional variable-centered approaches for identifying and assisting students at risk for degree noncompletion.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".