Assessing Students’ Knowledge and Soft Skills Competency in the Industrial Training Programme: The Employers’ Perspective
Bibliographic record
Abstract
<p>The importance of developing soft skills competency among students should be the priority of all the Higher Educational Institutions (HEIs) in order to ensure their graduates are marketable. Therefore, it is essential for HEIs to distinguish the knowledge and soft skill levels of their students so that strategies and intervention could be implemented to rectify their capabilities. The main purpose of this study is to evaluate the knowledge and soft skills competency from the employer’s viewpoints on the Universiti Utara Malaysia (UUM) students participating in the industrial training programme. A total of 438 employers from different industrial backgrounds had participated in this study. A questionnaire consisting of five dimensions of soft skills which are basic knowledge, communication skills, practical skills, leadership, and attitude was utilized to collect data. The results of this study indicate that the employers were satisfied with the knowledge and soft skills competency portrayed by UUM students in preparing themselves for the real work environment. The employers from the service sectors were satisfied with students’ performance in all dimensions of soft skills measured. However, employers from the factory and commerce sector perceived as moderate satisfaction for all dimensions of soft skills. Additionally, the employers of the factory and commerce sector assessed by giving the lowest satisfaction score for “hands-on” skills, but generally they satisfied with the students’ communication skills. The information gathered can provide important insights from the perspective of organizations which is valuable in improving the overall hard and soft skills competency for future professionals and managers.</p>
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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 | 0.002 | 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".