Re-Engineering Values into the Youth Education System: A Needs Analysis Study in Brunei Darussalam
Bibliographic record
Abstract
This study aimed to present a practical framework for designing values teaching program in youth education system. The choice of content, the nature of the students with respect to learning and their perception about the selected content for teaching values were studied. The study follows a Needs analysis design which drew upon document analysis and questionnaire. There were 104 respondents consisting of students from Universiti Brunei Darussalam (UBD) coming from different background and nationalities. Although qualitative paradigm was adopted, quantitative strategy was also used in order to gather views from a large sample size. Document analysis and questionnaire survey were used for data collection. The finding reveals that majority of respondents have strong interest in the selected content and exhibit most of Generation Y characteristics with respect to learning. Although they are technology addicts, they unexpectedly show a different approach to values learning; they preferred personal contact with teachers rather than online-based mode. Inferring from results, the authors recommend the content and method of teaching values in Universiti Brunei Darussalam and the need for further research on the effect of cultural and religious background on the adoption of global culture by 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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".