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| Type | Journal Article |
| Scope | Discipline-based scholarship |
| Title | Co-adaptive visual data analysis and guidance processes |
| Organization Unit | |
| Authors |
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| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| Journal Title | Computers & Graphics |
| Publisher | Elsevier |
| Geographical Reach | international |
| ISSN | 0097-8493 |
| Volume | 100 |
| Page Range | 93 - 105 |
| Date | 2021 |
| Abstract Text | 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. |
| Digital Object Identifier | 10.1016/j.cag.2021.06.016 |
| Other Identification Number | merlin-id:21975 |
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