A Problem Based Learning (PBL) Model in Developing Students' Soft Skills Aspect
Bibliographic record
Abstract
This study aims to find out how to improve students' soft skills through Problem-Based Learning (PBL) of Educational Sciences in order to prepare superior Human Resources (HR). This research is qualitative research, the research subjects are students of the First Semester Guidance and Counseling Study Program, Faculty of Teacher Training and Education, Private University in Solo Raya. The object of research is the improvement of students' soft skills through PBL of Education Science courses in order to prepare for Superior HR. Data collection using interviews, observation, documentation, and test methods. The validity of the data uses method triangulation, source triangulation, and perseverance of observation. Analysis of the data using qualitative analysis. The results showed that PBL courses in education can improve students' soft skills which include aspects of self-awareness, trust, adaptability, critical thinking, organizational awareness, attitude, initiative, empathy, integrity, self-control, leadership, problem-solving, risk-taking, and Time management, in order to prepare superior HR.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".