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Data on multi-actor parameter design tasks by engineering students with variable problem size, coupling, and team

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Area of Science:

  • Engineering Design
  • Human-Computer Interaction
  • Cognitive Science

Background:

  • Parameter design tasks involve selecting variables to meet functional requirements under constraints.
  • Complexity in design tasks can stem from technical factors (parameter coupling) and social factors (team size).
  • Understanding these complexity sources is crucial for optimizing design processes and team collaboration.

Purpose of the Study:

  • To quantify the impact of technical and social complexity on the effort required for abstracted design tasks.
  • To provide empirical data on how design task parameters and team dynamics influence completion time and effort.

Main Methods:

  • Utilized a controlled experiment with 374 abstracted parameter design tasks.
  • Manipulated technical complexity (number and coupling of design variables) and social complexity (number of designers).
  • Measured task completion effort across varying levels of complexity, with task durations ranging from seconds to over 15 minutes.

Main Results:

  • Both technical complexity (parameter coupling) and social complexity (number of designers) were found to increase the effort required to complete design tasks.
  • The study provides raw and post-processed data detailing the relationship between complexity metrics and task effort.
  • Task difficulty varied significantly, with simple tasks completed rapidly and complex tasks demanding substantial effort.

Conclusions:

  • Design task complexity, encompassing both technical and social dimensions, directly correlates with increased effort.
  • These findings have implications for team formation, task allocation, and tool design in engineering and other complex problem-solving domains.
  • Further research can explore mitigation strategies for managing complexity in collaborative design environments.