Do Emotional Intelligence and Academic Persistence Interrelated in Final Year Students?
Bibliographic record
Abstract
Final students are students who often face academic pressure, final assignments, and preparations for entering the world of work. Emotional intelligence is important for dealing with stress, maintaining motivation, and having academic persistence which gives a final student the ability to continue fighting to achieve their academic goals despite facing challenges. various obstacles. For final students, challenges such as final assignments and preparation for graduation can test their level of persistence. This quantitative research aims to explain the relationship between emotional intelligence and academic persistence in final students. Data was collected using The Schutte Self Report Emotional Intelligence Test (SSEIT) scale for emotional intelligence and The academic persistence scale for academic persistence, then analysis was carried out using reliability and validity tests, assumption tests and hypothesis tests. The results of this research show that specifically emotional intelligence and academic persistence in final students have a relationship with a Correlation Coefficient of (.755), which means it has a positive relationship and a significance value of 0.000 is smaller than 0.05, for linearity it shows (628.951) for the sig F value. The significance deviation of linearity is 0.000. From the results of this research which shows its significance towards emotional intelligence and academic persistence in final students, it shows that high emotional intelligence is associated with stronger academic persistence. Overall, research on the relationship between emotional intelligence and academic persistence can yield a variety of beneficial implications in improving college students' academic success, supporting their well-being, and preparing them for future challenges.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".