Reading Notes: Identity and the Museum Visitor Experience, Chapter 1

Reading Notes for "Identity and the Museum Visitor Experience", John H Falk, Chapter 1

Old paradigm of what to focus on to assess learning in museums
1. Museum-focused (content, exhibits).
Implication A: frequent visitors are the ones who care the most and know the most about the museum's content
Implication B: visitors who have more prior knowledge in the content area will learn more from the museum

2. Visitor-focused (demographics, frequency, social arrangement)
Contention A: Demographics are not predictive. If race/ethnicity correlates with museum going, how do we know that is causal? Example, race may have different results, but it's due to more variation within a race than variation between races.
(My counter, greater variation is still useful to understand. It's still a difference based in race. How we interpret the difference is different.)
Contention B: Visit frequency is not a quality of the visitor, but rather an action that indicates something deeper
Contention C: Social arrangement - not the same as the actual social interaction of a visit

New model
Supersedes the Contextual Model of Learning
1. Seeks to develop a predictive model
2. Stop thinking about museum exhibits and content as fixed and stable designed to achieve singular outcomes - instead see as intellectual resources capable of being experienced and used in different ways
3. Visitors aren't defined by a permanent quality or attritube, but each is unique and capable of having different museum visit experiences (even within same person). Call this visitor's identity-related visit motivations.

My thoughts
Overall, agree with this. I really like the idea of fluidity in visitors at their personal level (a given visitor could "be" a different kind of visitor at various museum visits) as well as fluidity in content/exhibits (this seems to be getting more and more true in the current age of customization, DIY, life hacking, and instant personalization).

I'm interested (and maybe a bit skeptical) to see a truly predictive model. This chapter was vague and I'm looking for precision and crispness in a predictive model.