The Implementation of Problem-Based Learning (PBL) in a Year 9 Mathematics Classroom: A Study in Brunei Darussalam
Bibliographic record
Abstract
Problem-Based Learning (PBL) is a constructivist, student-centered instructional strategy in which students work collaboratively to solve problems and reflect on their learning experiences to advance or gain new knowledge. PBL was originally developed in medical school programs at the McMaster University in Canada in the 1960s. Since then, much research has highlighted the benefits of PBL for developing students’ mathematical knowledge in more flexible and novel ways than traditional teacher-centered teaching approaches. However, there has been a lack of studies examining how PBL can be applied to mathematics teaching and learning, since studies that have investigated the implementation of PBL outside a medical context are sparse in Brunei Darussalam. Therefore, in this study, we attempted to fill this research gap by exploring the implementation process of PBL in a Year 9 mathematics classroom and its possible impact on students’ learning in mathematics in the context of Brunei Darussalam. The participants of the study consisted of 17 Year 9 students (ages 14-15) from a secondary school in Brunei Darussalam The findings from our study showed that the implementation of PBL helped motivate the students to collaboratively work as a group and learn from their peers and therefore, gradually reduced their dependence on the teacher during the course of the intervention. The results from the students’ performances on the pre-test and the post-test also provided evidence to show that the implementation of PBL could have a positive impact on the students’ learning in mathematics. Directions for future mathematical PBL implementation are also discussed and offered.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".