Best Practices for Integrating Virtual International Team Collaborations into Multidisciplinary Engineering Design Courses
Bibliographic record
Abstract
Policy makers, employers, and engineering educators have long recognized that in addition to technical skills that embrace engineering design, graduate engineers need to develop practical skills such as professionalism, multidisciplinary teamwork, global competence, and execution competence that can enhance their employability (ABET 2020; Shuman et al. 2005). Some strategies employed by engineering educators to support students’ development of these professional competencies include the augmentation of academic knowledge with virtual international collaboration experiences and the integration of external stakeholders including industry partners (Oladiran et al, 2011; Steghöfer et al, 2018) into design courses. The involvement of virtual international collaborators and external stakeholders in academic education can result in difficult experiences for all participating parties. Existing studies on virtual international teams and external stakeholder involvement in multidisciplinary engineering design courses are limited. This study reports on a survey of the literature on integrating multidisciplinary and virtual international teams into engineering design courses. A literature review was conducted on relevant engineering education articles published in leading academic journals on the intersection of engineering multidisciplinary teams and virtual international collaborations in academic settings between 2017 and 2020. Following the syntheses of the multidisciplinary teams’ literature, we identified eight challenges from past course instances namely multidisciplinary communication, negative relatedness, course organization, virtual team communication, trust, stakeholder management, team formation and dispersion. Recommendations for designing and enacting multidisciplinary engineering design courses with external and international collaborations were also proposed. Our study highlights best practices that can support instructors to plan the involvement of external and international stakeholders in engineering design course settings.
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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 | 0.052 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".