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Begin at the Beginning: A Constructionist Model for Interpreting Data Visualizations
Author(s) -
Wojton Mary Ann,
Hayde Donnelley,
Heimlich Joe E.,
Börner Katy
Publication year - 2018
Publication title -
curator: the museum journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.312
H-Index - 8
eISSN - 2151-6952
pISSN - 0011-3069
DOI - 10.1111/cura.12277
Subject(s) - affordance , simplicity , computer science , exhibition , constructionism , context (archaeology) , human–computer interaction , strict constructionism , presentation (obstetrics) , data science , epistemology , visual arts , medicine , art , paleontology , philosophy , radiology , biology , programming language
Within museums and science centers, the visual presentation of data represents a timely, relevant, and formidable interpretive challenge. Tackling this challenge head on, however, means employing a series of principles that position educators to support learners of all skill levels in interpreting data visualizations more generally. In this article, we introduce the Simplicity‐Familiarity Matrix, a research‐driven model for situating complex data visualizations in the context of exhibition design. This model emerges from a study of data literacy that was undertaken at five informal learning institutions, along with established principles of constructionist approaches to teaching. Specifically, it highlights key affordances and challenges we associate with data visualizations along two spectra: simplicity and familiarity. We propose the Simplicity‐Familiarity Matrix, along with criteria for each quadrant, to assist museum professionals when designing interpretative materials for an exhibition space. In light of these considerations, we close with several guiding principles for supporting learners’ apprehension of data visualizations in museums: (1) including the use of well‐designed, easy‐to‐understand data visualizations, (2) providing appropriate support and guidance, (3) offering multiple modes of access, and (4) making data visualizations relevant to visitors, e.g., via personalization.