Deep Personalization: A Case Study of Systems Thinking within an Art MuseumPaper
Paul Fishwick, University of Texas at Dallas, USA
How can museums become more personal—to connect better with each unique visitor? A visitor will have a general profile that is a combination of the visitor’s background, topical subject interests, and learning style. We recommend a deep personalization strategy that begins with learning as much as possible about the visitor, with special attention paid to learning topical interests within STEAM (Science, Technology, Engineering, Art, and Mathematics). Each visitor will enter a museum with a specified combination of these five topics, and so a visitor model can be viewed as a five-dimensional space with each visitor being a point in, or region of, this space. This region is termed a topical visitor profile. Systems thinking is one example of a profile that is pervasive across the four areas of STEM.
We chose to discuss systems thinking within an art museum because we hypothesize that a subset of art museum visitors will have this profile, and therefore the museum is tailoring its object interpretations to these visitors by exposing them to systems thinking on any particular object. We describe a hybrid software/hardware architecture based on 1) the Physical Web concept, which represents a natural extension of the Semantic Web; 2) a collaborative wiki-style approach to curating knowledge about each object; and 3) a desire to seamlessly connect the informal museum setting with formal learning classes, subjects, and their associated subject standards. Deep personalization represents the broad goal described in the paper, and systems thinking is an example of personalization for STEM learning.
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