Problem-based Learning in Geography: Towards a Critical Assessment of its Purposes, Benefits and Risks
Bibliographic record
Abstract
This paper makes a critical assessment of problem-based learning (PBL) in geography. It assesses what PBL is, in terms of the range of definitions in use and in light of its origins in specific disciplines such as medicine. It considers experiences of PBL from the standpoint of students, instructors and managers (e.g. deans), and asks how well suited this method of learning is for use in geography curricula, courses and assignments. It identifies some 'best practices in PBL', as well as some useful sources for those seeking to adopt PBL in geography. It concludes that PBL is not a teaching and learning method to be adopted lightly, and that if the chances of successful implementation are to be maximized, careful attention to course preparation and scenario design is essential. More needs to be known about the circumstances in which applications of PBL have not worked well and also about the nature of the inputs needed from students, teachers and others to reap its benefits.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".