Does Synchronous Collaboration Improve Collaborative Computer-Aided Design Output: Results From a Large-Scale Competition
Bibliographic record
Abstract
Abstract With the growing demand for distributed collaboration for large and complex design in modern engineering, the collaboration inefficiencies of traditional computer-aided design (CAD) tools are increasingly conspicuous. Emerging cloud-based multi-user computer-aided design (MUCAD) platforms bring a new working style for CAD in the form of real-time synchronous collaboration. Little research exists to characterize collaboration in CAD, and specifically the synchronicity of collaboration has yet to be examined. In this study, we analyzed the backend action logs of 101 teams’ design processes from a large-scale virtual robotic design competition, where all designs were modelled in a commercially available MUCAD platform. Metrics of interest were analyzed with regression and mediation analyses to uncover factors that correlated to a team’s success in the competition. Results show that team size is a positive predictor of team performance. Large teams, which tend to see a large amount of time commitment from members, were more likely to perform more CAD actions and achieve high scores from the competition. This suggests that the benefits of collaboration (e.g., economies of division of labor, learning) outweigh the potential downsides (e.g., coordination overhead, free riding) in this context. While controlling for team size, increased synchronous collaboration occurrences were observed to negatively correlate to teams’ performance — a novel finding which we discuss in detail. Thus, we conclude that although large teams benefited from the MUCAD environment, a tendency for synchronous real-time collaboration did not coincide with higher performance. This study provides important evidence in the ongoing design and innovation research fields aiming to better understand collaboration. Future research should investigate the characteristics of effective collaboration strategies in MUCAD environments to develop best practice for the increasing number of design teams moving to such tools.
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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 | 0.009 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".