Assessing the Influence of Global Cross-Cultural Collaboration in Capstone Design Projects on Engineering Education
Bibliographic record
Abstract
In today's interconnected world, engineering education must prepare students to collaborate across cultures, disciplines, and geographies. This study investigates the impact of cross-cultural collaboration on student learning outcomes in joint capstone design projects comprising students from two universities: one based in Canada and the other in China. The research focuses on how in-person team bonding activities enhance collaboration effectiveness and project performance. Students worked in mixed teams to complete real-world engineering design challenges (in the areas of robotics, automation, and thermo-fluids among other), involving concept development, engineering analysis, prototyping, and presentations. While most collaboration occurred virtually through digital platforms, students from the Canadian university traveled to visit students in the Chinese university during the Fall semester for face-to-face interaction with teammates and mentors. Similarly, Chinese students traveled to Canada at the end of the Winter term. Surveys and performance metrics were collected before and after the in-person visit to assess changes in collaboration quality and learning experience. Results indicate that virtual-only interactions, though convenient, limit rapport and communication depth. In contrast, in-person engagement significantly improved team cohesion, reduced communication-related anxiety, and enhanced participation. Notably, student team academic performance as measured by progress presentation scores improved relative to the class average following the in-person visit. The findings offer practical insights for global engineering educators, demonstrating the value of blending virtual tools with in-person experiences to enhance teamwork and project outcomes.
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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".