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An Affordance-Based Framework for Human Computation and Human-Computer Collaboration.

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Summary
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Visual analytics, combining human and machine intelligence, needs a common language. This study identifies key design patterns in 49 papers to create a framework for collaborative problem-solving tools.

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Area of Science:

  • Visual Analytics
  • Human-Computer Interaction
  • Data Visualization

Background:

  • Visual analytics integrates human and machine intelligence for complex problem-solving.
  • Existing research lacks a common framework for comparing human-computer collaboration systems.
  • A shared language is needed to describe the benefits of designing for human-machine synergy.

Purpose of the Study:

  • To establish a common framework for understanding visual analytics and human-computer collaboration.
  • To identify key design patterns in human-computer collaborative problem-solving.
  • To motivate future research by highlighting unexplored avenues in the field.

Main Methods:

  • Systematic review of 1,271 papers from top-ranking conferences in visual analytics, HCI, and visualization.
  • Identification and analysis of 49 representative papers on human-computer collaborative problem-solving.
  • Analysis of design patterns focusing on human and machine intelligence affordances.

Main Results:

  • Identified key patterns in visual analytics system design related to human and machine intelligence.
  • Provided a comprehensive overview of the state-of-the-art in human-computer collaborative problem-solving.
  • Highlighted unexplored research areas within visual analytics.

Conclusions:

  • A consensus on design attributes is crucial for advancing visual analytics.
  • The identified framework aids in understanding disparate research branches in the field.
  • This work aims to foster future research and development in human-machine collaborative problem-solving.