Inventive Problem-Solving Skills Effectiveness in RBT Project-Based Learning (PBL)
Bibliographic record
Abstract
Examining how well a TRIZ-based Project-Based Learning (PBL) module fosters creative problem-solving abilities in secondary school students enrolled in Design and Technology (RBT) courses is the goal of this study. The combination of PBL with structured creative problem-solving techniques like TRIZ, particularly in the context of RBT topics, is still largely unexplored, even though PBL has been widely used in Malaysian education. In this quasi-experimental study, 118 Form 2 students were split into two groups: a control group (n = 58) that received traditional instruction and a treatment group (n = 60) that received the PBL intervention. The 12-week TRIZ methodology-based module was presented to the treatment group. The treatment group's mean scores increased by 34% (from 50.72% to 67.93%) compared to the control group's 16% improvement, according to pre- and post-test data. Significantly, the treatment group showed a stronger change in mastery levels and high order thinking abilities in every aspect of problem-solving. The results imply that the TRIZ-based PBL module fosters structured and innovative problem-solving skills more successfully than conventional methods. By giving a scalable methodology for developing 21st-century skills, the study offers new insights by presenting a verified pedagogical framework that incorporates TRIZ into project-based learning for secondary education.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".