Emotional intelligence in higher education: Perspectives of Nepalese college students
Bibliographic record
Abstract
This paper explores the perspective of college students regarding emotional intelligence in higher education. A great number of changes in the education system globally has developed new expectations for teachers. These days, teachers are not just the authority in a classroom but a mentor. Thus, emotions play a significant role in the teaching and learning process. This paper argues that mastery in subject matter does not make the best teacher in the eyes of students, rather emotional intelligence does. Emotional intelligence is neither the opposite of intelligence nor just the battle between mind and heart but it is the unique intersection of both. Quantitative research was done with 201 college students from different educational backgrounds. The data was analyzed with the theoretical modality influenced by Daniel Goleman's ‘Emotional Intelligence' method. The first part of the research explores what aspects of teacher’s students associate with being the best, and the second portion explores what behaviors the students want in their teachers in general. This research helps to identify emotional intelligence, a new domain introduced in the teaching and learning process, as significant, even from the student's perspective. Keywords: Emotional Intelligence, higher education, teaching-learning, perspectives
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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 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".