Environmental Learning Using a Problem-Based Approach in the Field: A Case Study of a Hong Kong School
Bibliographic record
Abstract
This study investigated the environmental learning of a group of senior geography students through a problem-based learning (PBL) field programme to see if the goals of education for the environment could be accomplished. In the PBL field programme, the students were given a problem statement concerning a real-life scenario of an old lady living in a remote village of Hong Kong. During the PBL field programme which also has adopted an action research framework, the students were observed to have identified the problem statement, set hypotheses, constructed the research methods and collected empirical data in an authentic environment all by themselves. This paper reports on the first cycle of this piece of action research. Data were collected both quantitatively and qualitatively via questionnaires, students' reflective journals, field observation notes and group interviews. The initial finding of this PBL field programme reveals that students had acquired more in-depth knowledge and extended their comfort zones in learning. They were able to develop and practice their critical thinking and problem-solving skills while they were working on the problem in the field. Although there was no evidence of sustained self-directed learning among the students, the PBL field programme offered them a framework for developing self-directed learning. An initial conclusion is that PBL in the field can provide a useful framework and direction for EE to accomplish the goals of education for the environment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 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".