Supporting Inclusive Engineering Education using Global Virtual Teams
Bibliographic record
Abstract
Future engineers require global competencies to help them transition to the labor market in an increasingly complex, digitized, and evolving world economy. In response, an International Virtual Engineering Student Teams’ (InVEST) program was developed, in which multidisciplinary students were engaged in collaborative technical projects within a global virtual team learning environment. This study examines engineering students’ intercultural competencies and supports their development of those competencies within the context of a global engineering project where they work in culturally diverse teams. We report on two successive iterations of the engineering global virtual team (GVT) learning program. Each included a pre-survey to understand student’s initial knowledge and cultural orientation and a post-survey to assess students’ perceptions of their intercultural learning and experiences. We found that blending global virtual team learning with collaborative international projects was an effective strategy for helping engineering students gain international exposure in an inclusive manner while developing intercultural competencies, virtual team collaboration skills, and technical engineering knowledge.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".