Trait Emotional Intelligence and University Graduation Outcomes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it