Does Problem-Based Learning Improve Problem Solving Skills?—A Study among Business Undergraduates at Malaysian Premier Technical University
Bibliographic record
Abstract
Problem-based Learning (PBL) approach has been widely used in various disciplines since it is claimed to improve students’ soft skills. However, empirical supports on the effect of PBL on problem solving skills have been lacking and anecdotal in nature. This study aimed to determine the effect of PBL approach on students’ problem solving skills using a quasi-experimental non-equivalent group pretest–posttest design. Fifty management students from a premier Technical University in Malaysia were assigned to experimental and control groups. In the experimental group, students were given four problems to be solved and their solutions of the problems given were assessed in terms of their accuracy and quality. Students in the control group received conventional classroom instructional design. Results indicate that students in the experimental group have better problem solving skills (z: -4.220, p: 0.001 for accuracy and z: -2.594, p: 0.009 for quality) compared to those who were not exposed to the PBL approach. This finding substantiates the use of PBL as an effective instructional tool to improve students’ problem solving abilities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".