Deepening Secondary Students’ Understanding of Coastal Management at Labrador Park through Fieldwork
Bibliographic record
Abstract
The impetus for action research on experiential learning of geography stems from a desire to introduce a more “engaged” form of geography, whereby students move beyond the academic study of geography in the classroom to making sense of geography in relation to their reality (Morgan, 2012). Through an environmental scan of the inclusion of fieldwork into the new Geography syllabus commencing 2013, we sought to find out how fieldwork is integral to the study of geography in Singapore schools. The choice of coastal geography as a topic for inquiry was strategically aligned to its inclusion in the new syllabus and its relevance to Singapore’s geography as an island. The feedback obtained from teachers participating in Professional Learning Circles (PLCs) also suggested that students found it challenging to understand abstract geography concepts, in particular, physical geography processes and how they take place in real world contexts. As such, a “disconnect” or a learning gap has been created between geography presented to the students in the textbook to that of their real world contexts. The decision to explore how to bridge students’ learning gaps through fieldwork as a pedagogical practice was also guided by our Humanities Department action plan to effectively engage our students through Outdoor Classroom Experiences (OCE). We chose Labrador Park as a research site due to various factors, such as its geographical proximity to the school, evidence of human management of coasts, preservation of historical features, and availability of resource materials.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".