Emotional Intelligence, Social Networking Skills and Online Counselling Communication Effectiveness Among Students of OAU, Ile-Ife, Nigeria
Bibliographic record
Abstract
With a view to providing empirical information on the factors that influence online counselling communication among Nigerian university students, this study investigated the influence of emotional intelligence and social networking skills on the effectiveness of online counselling communication among students of Obafemi Awolowo University (OAU). Through a descriptive survey research design, the study sampled 100 students purposively from users of the University online counselling platform, on the basis of being able to have established complete counselling interaction with any of the counsellors online during the harmattan and rain semesters of 2017/2018 session (or over a period of 12 months.). The results showed that 78.0%, 19.0% and 3.0% of the students demonstrated high, moderate and low levels of online counselling communication effectiveness respectively and that emotional intelligence has significant influence on online communication effectiveness (β = 0.790, p < 0.05). The results further showed that social networking skills has no significant influence on online communication effectiveness (F = 3.457, p > 0.05) and no significant interaction effect of emotional intelligence and social networking skill was found on online counselling communication effectiveness (F = 0.546, p > 0.05). The study concluded that the only factor that influenced online counselling communication effectiveness among the students under study is emotional intelligence
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".