A Statistical Analysis of the Effect of Collaborative Projects on Critical Thinking Skills among Architecture Students
Bibliographic record
Abstract
This research paper proposes the effect of collaborative projects on acquisition of critical thinking skills by architecture students in the form of a quantitative, cause and effect research design. Against this background, collaborative learning has been strongly encouraged in the face of the growing interest in problem-solving, creativity, and reflective thinking in architectural education. Nonetheless, there is not much empirical data on its effectiveness in terms of its measurement in architecture programs. A pre-test/ post-test control group study was conducted to fill this gap to access 120 undergraduate architecture students in three South Asian universities. Experimental group engaged in team-based design projects which were organized and the control group had projects of the same nature but were done individually. The Watson-Glaser Critical Thinking Appraisal was used to evaluate the abilities of critical thinking at the beginning and the end of the semester. Findings revealed that students who participated in team learning were much improved in terms of their critical thinking scores than those who learned on their own (p < 0.01). The results indicate that collaborative project-based learning is not just effective in improving design competence, but also in the development of very crucial cognitive skills that are important in future architectural practice. The research indicates the importance of the introduction of collaborative pedagogies in the architectural school curricula and offers evidence-supported suggestions to curriculum developers, educators, and academic policy makers.
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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 | 0.024 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".