Analysis of Designer Emotions in Collaborative and Traditional Computer-Aided Design
Bibliographic record
Abstract
Abstract We developed a new method to link designer emotions with corresponding designer activities while using computer-aided design (cad) software. Our method employs automated facial emotion detection software and cursor tracking. We applied this method via an experiment with nine participants, each working with the same synchronously collaborative cad platform, and assigned a series of cad tasks in one of two distinct working styles: single participants working by themselves and paired participants working together. We analyzed and compared trends in emotion for these two working styles. Pairs, on average per person, experienced higher levels of emotion (measured as joy, sadness, anger, contempt, fear, and surprise) than individuals. We linked occurrences of each emotional response to their antecedent activities in the cad environment (navigating the model tree, sketching in the graphics area, making selections in the feature menu, and communicating using the chat window). Using a logistic regression analysis, we revealed statistically significant trends linking emotions and cad events, and we found that some emotions are more likely to occur with certain designer actions in the cad software. The method and conclusions presented in this paper allow us to better understand designer emotions in traditional and collaborative cad, which link to the established relationships between emotion and designer satisfaction, creativity, performance, and other outcomes increasingly valued by engineering designers and managers in virtually collaborative environments.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".