Students’ Perspective on the Emotional Intelligence of Teachers on Student Engagement
Bibliographic record
Abstract
Student Engagement has emerged as a central theme in the context of Higher Education in recent years. Thus, there is great consent towards it. Due to several factors, undergraduates are diverted and disengaged consequently, especially the state universities of Sri Lanka currently experiencing this issue. Even there are many factors impact student engagement. This study explored the significance of teachers with their emotional intelligence competencies for getting student engagement. University students prefer to take student-centered teaching where it is possible if only teachers are ready and capable to take account of students' interests, needs, and perspectives on adaptation with their teaching approach. Having understood the phenomenon above, the current study designed to investigate the impact of the emotional intelligence of teachers in higher education on student engagement based on students’ perspectives. This is an explanatory study that the data collected from the sample of 1455 undergraduates selected from the state universities in Sri Lanka on which stratified random sampling method was adopted. Finding reveals that students are engaged but not actively and as per the students’ point of view lecturers are just good rather excellent enough on emotional intelligence competencies. However, there is a strong positive relationship as well as have a significant positive impact of emotionally intelligent teachers of higher education on student engagement. Ultimately it is concluded that higher education teachers require to expand the substantial amount of skill on emotional intelligence. Further, then active student engagement can be ensured.
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.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".