Does Synchronous Collaboration Improve Collaborative Computer-Aided Design Output: Results From a Large-Scale Competition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it