The Role of Co-Curriculum in Enhancing Students’ Soft Skills: Communication Skills
Bibliographic record
Abstract
This study attempts to highlight issues related to soft skills, namely communication skills among students of Universiti Utara Malaysia. Soft skills that include communication skills, problem solving skills, teamwork, leadership skills and social responsibility, are important elements that are highly emphasized and essential in addition to academic skills. The co-curriculum courses offered at ever public university in Malaysia, among others, aim to shape and develop the personality of students as well as provide them with various components of soft skills. Students with good soft skills will easily adapt to their working environment. Employers prioritize soft skills especially communication skills and social responsibility in employees’ selection sessions. Thus, this study was conducted with a major focus on communication skills. The three objectives of this study are to identify the level of communication skills among UUM students; to identify the significance of co-curriculum courses in influencing students’ communication skills and to identify the role of attitude in improving communication skills among UUM students. The primary data were gathered using questionnaires distributed to UUM students who are undertaking co-curriculum courses. The data will then be analyzed using Structured Equation Model (SEM), as well as descriptive analysis. A communication indexes has also been developed to identify the level of communication skills among UUM students. The findings show that the communication index for UUM students were between 0.70 (lowest score) and 0.78 (highest score) and the average score is 0.74. The score is normal since the value is above 0.5. Both the independent variables; co-curriculum courses and attitude were positively significant in developing students’ communication skills in UUM at the significant level of less than five percent.
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 | 0.001 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".