Co-adaptive visual data analysis and guidance processes. (November 2021) - Sperrle, Fabian; Jeitler, Astrik; Bernard, Jürgen; Keim, Daniel; El-Assady, Mennatallah
Highlights: A multigranular model of co-adaptation with a focus on making co-adaptation testable. Learning phases: define testable goals for user and system to structure design. Adaptation processes: actors adapt knowledge representation models. Interaction dynamics: applications enable adaptation in learning-teaching dynamics. A discussion of actor expectations towards adaptation. An overview of research opportunities in co-adaptive analytics. Graphical abstract: Abstract: Mixed-initiative visual data analysis processes are characterized by the co-adaptation of users and systems over time. As the analysis progresses, both actors – users and systems – gather information, update their analysis behavior, and work on different tasks towards their respective goals. In this paper, we contribute a multigranular model of co-adaptive visual analysis that is centered around incremental learning goals derived from a hierarchical taxonomy of learning goals from pedagogy. Our model captures how both actors adapt their data-, task-, and user/system-models over time. We characterize interaction patterns in terms of the dynamics of learning and teaching that drive adaptation. To demonstrate our model's applicability, we outline aspects of co-adaptation in related models of visual analytics and highlight co-adaptation in existing applications. We further postulate a set of expectations towards adaptation in mixed-initiative processes and identify open research questions and opportunities for future work in co-adaptation.