Development of Model of Selecting Visualization of Joint Task-Solving in Human-Machine Cloud

Digital platforms are coming to be used more and more to organize the interaction of experts in joint task-solving. They provide many opportunities for structuring tasks, attracting experts, evaluating their competencies, and managing joint task solving. However, a kind of visualization for ensuring simple and visible progress has yet to be created. This paper analyzes the most common task-solving visualizations and evaluates their strengths and flaws with regard to typical tasks available on existing services for joint task-solving. The result of this work is a model that allows evaluating and comparing task-solving visualizations and choosing a visualization for use on a platform for collaborative task-solving in a human-machine cloud.