Comparison of Emotional Intelligence Scores among Engineering Students at Different Stages of an Academic Program
Bibliographic record
Abstract
Intelligence quotient (IQ) has been widely used as a measure of an individual’s intellectual abilities. Emotional intelligence or emotional quotient (EQ) is equally important in defining excellent work performance. An increasing number of employers have started considering fresh graduates with high EQ because the job market is already full of academically competent candidates. With this motivation considered, this study aims to compare the EQ levels of four groups of undergraduate students in their first year of enrollment in their academic program and at the start of each succeeding academic year in the Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia (UKM). The EQ scores of these students were also monitored until their graduation. The EQ levels were determined using the Malaysian EQ Inventory (MEQI) test developed by UKM researchers. A comparative study of EQ levels among five batches of students was conducted, starting from their first enrollment in their respective programs. One batch of students has completed the study, and their MEQI results indicated a slight reduction in the total EQ scores. However, two domains recorded improvement: social skills and maturity. Thus, tertiary education is not expected to change student EQ levels, completely because EQ level comprises cognitive and emotional qualities developed during primary and secondary years of education. Innovative strategies on effective teaching and learning activities should be identified to determine their positive influence on the development of EQ domains.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| 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 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".