Problem-Based Learning in the Field for Schools in Hong Kong: PBL Programs in Kowloon Technical School
Bibliographic record
Abstract
The future development of Hong Kong owes a radical change in our current education curriculum. Modern views of learning argue that the conventional instruction mode of science curriculum should shift from a transmission approach to a constructivist approach through Problem-based learning (PBL). This is a radically different pedagogical strategy of posing significant, real world situations and providing resources, instruction to learners. The field is an ideal setting for conducting Problem-based learning because field problems are those authentic but often referred to as ill-structured. They are innately challenging in part because of no definitive or simple answers, thus require a full integration of knowledge across disciplines in the problem solving process. It is believed that students working collaboratively as a PBL group will benefit through repeated goal setting, planning, acting, sharing, reflecting, rethinking and refining. Kowloon Technical School has put into practice two PBL programs for S.6 and S.2 students with the support of the Department of Earth Sciences, the University of Hong Kong through her pioneering project, the “Problem-Based Learning in the Field”.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".