The Benefits of Adopting a Problem-Based Learning Approach on Students’ Learning Developments in Secondary Geography Lessons
Bibliographic record
Abstract
Problem-based learning (PBL) provides an appealing framework for teaching and learning not only within the subject of geography but also across other disciplines. It promotes a healthy environment for active learning with its diverse sets of activities, helping students carry out investigative inquiry in the learning processes. This study examines the potential benefits of adopting a PBL approach in teaching and learning in secondary geography classrooms. It takes into account the inputs needed from both teacher and students in determining the success of the approach implementation. The study shows how, through careful planning and preparation, PBL activities can effectively enhance students’ engagements and improve their grasp of geographical content knowledge. However, the teacher’s shortcomings in performing the role of facilitator did highlight a limitation for the research, which hindered the success of the implementation. Future research should continue to actively examine experiences from teachers in PBL applications, discussing the circumstances to identify the conditions necessary for successful implementation of PBL within a variety of contexts.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".