A New Framework for Universiti Kebangsaan Malaysia Soft Skills Course: Implementation and Challenges
Bibliographic record
Abstract
The importance of soft skills to the graduates to compete in the working world is undeniable. Soft skills are complementary to the academic qualifications held by students. Recognizing this, the University Kebangsaan Malaysia (UKM) has established a new framework for Soft Skills courses to improve the existing framework of the course. The implementation of this course, which is started about 6 months or approximately 1 semester; is based on learning outcomes (LO) into 8 main goals. Each of these LO can be achieved by following all 8 Soft Skills courses that serve as a compulsory course in the university. These courses are conducted based on the concept of learning contracts involve an agreement between students and lecturers to determine the assignments/projects to be completed by students in a given period. Proof of learning outcomes should be uploaded by students to iFolio system that can be evaluated by evaluator (lecturers). The implementation of the new framework deals with various problems that pose challenges to both students and lecturers. There are 50 identified issues and challenges involving students, lecturers and the systems/operations. The main cause for the challenges is lack of understanding on the implementation of this course, since it is just running for its first semester. This results in a great deal of confusion which triggers the issues on the ground. However, every issue has a solution. Therefore, the management should take proactive steps as to properly deal with the issues and challenges.
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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 | 0.014 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".