Universiti Kebangsaan Malaysia Learning Contract Course: Experience and Performance of the First Cohort
Bibliographic record
Abstract
Research from different parts of the world recognizes the effectiveness of a learning contract course in improving the personal skills of students. Therefore, UKM has chosen this approach to improve the personal soft skills of its students. The university has carried out this approach by making HHHC9118-Soft Skills as a compulsory course for all students. This course requires the students to implement a project or activity in accordance with a written contract that they plan together with their instructors. Moreover, the students are awarded with eight credit hours after taking up this course. This research evaluated the effectiveness of the learning contract course in developing the soft skills of the 2,378 participants, who had already finished the course. A literature review and a survey were conducted to obtain the data. The questionnaire contained 10 statements, which were either positive or negative, that asked about the self-esteem levels of the respondents. Data were analyzed using the Statistical Package for Social Science (SPSS) software. The results showed the respondents positively interpreted all the statements in the questionnaire, which proved that a learning contract successfully improved the soft skills and self-esteem of UKM students. Therefore, this approach effectively improved the soft skills of the students.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".