The Score Difference of Emotional Intelligence among Engineering Students at Different Levels of Academic Year
Bibliographic record
Abstract
The number of students from the under-graduate level who have successfully completed their studies is on the increase every year. In the selection process for the best employee-candidate, employers have to take into consideration several factors other than academic excellence, including values that depict EQ or emotional intelligence. This study focuses on looking at the EQ achievement among the under-graduate students at the Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia. The EQ scores were measured using the MEQI Test (Malaysian EQ Inventory). The study began by monitoring EQ achievements of a group of students at three consecutive years: Year 1, Year 2 and Year 3 in the faculty. The study is expanded by measuring the EQ of all faculty students at the stage of Year 1, Year 2, Year 3, Year 4, also right after they had completed their studies of four years. Results have shown that, from the three-year data from 2010 to 2012, the overall EQ scores have recorded a marked increase.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".