SoSTeM Model Development for Application of Soft Skills to Engineering Students at Malaysian Polytechnics
Bibliographic record
Abstract
Soft skills are some of the skills needed to ensure that graduates fulfill the needs of the job market. Until 2010, almost 30% of unemployed graduates in Malaysia are technical graduates and one third comes are graduates from polytechnic. Most engineering graduates are proficient in technical skills but lack in soft skills. The lack of relevant knowledge among lecturers in order to identify appropriate ways and methods in the process of teaching and learning is one of the causes of lack in soft skills application. This study aims to identify the suitable teaching methods for the application of soft skills in the engineering programs for engineering students in Malaysian polytechnics. 488 students and 332 lecturers in engineering courses at the polytechnic had been questioned using questionnaires and interviews. The results showed that there is a relationship between the level of application of soft skills element with the teaching & learning methods used by lecturers. Based on these relationships, researchers had produced SoSTeM model as the model of application of soft skills for engineering students. Researchers also discovered that the use of teaching & learning methods for applying soft skills in engineering programs vary according to the elements of soft skills.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".