Platforms for information gathering

One of my assignments for my digital badge in futures studies was to choose a platform for storing and retrieving online articles, posts, and content. The goal is to do regular "scans" of news, books, and web chatter to understand trends in a wide range of areas (social, political, environmental, economic, technological) as well as industry-specific trend (in my case, museums). In order to scan effectively, you need a place to store the articles of interest.

I've chosen Evernote.

I've used it for a few months now, so this will be a more formal use of a known platform for me. It has good visual display for information retrieval and integrates with social media sharing and blog aggregators.

My intuition says that the trick to good scanning will be to selectively save articles. I will have the tendency to save everything, but my goal is to keep up a strong filter and only save things worth looking at again.

Digital Badging Project Begins!

I'm a test pilot for Center for the Future of Museums' digital badging project. I'll be testing the concept of digital badging and at the same time earning my own badges in futures studies. I'll forego any other introduction, and dive in to lessons learned from my work today.

The first badge is in Scanning for Change, and its role in strategic foresight. My takeaways so far:

- The vital importance of "out-reading the other guy.
- The big question: how to organize what you have read, are reading, and want to read, in a way that you can recall and access it later. I am very into organization and process, and this is something that I haven't found a good solution for ... look for that in the coming weeks.

Reading Notes: Identity and the Museum Visitor Experience, Chapter 2

Reading Notes for "Identity and the Museum Visitor Experience", John H Falk, Chapter 2

Leisure

• Leisure becoming more central in last 10-30 years
• Leisure is connected to identity: pursue leisure to construct, situate, realize identity
• Satisfaction with leisure depends on the activity meeting expectations/motivations: not about "what" people do but "why" they do it. The success of the leisure activity depends on fulfilling the "why". This was defined as the "experiential approach" by Driver and Tocher. Driver defined 15 major motivational categories to describe why people engage in recreational activities

Research on Museum Motivations

• Describes several different studies
• Different motivations correspond to different learning outcomes

Learning in Museums

• For visitors museums are, at their core, places of leisure learning
• Learning in museums is about intrinsic identity building, not extrinsic performance goals
• We don't have good tools for measuring learning when learning is about identity rather than performance.

Identity-related visit motivations
1. Explorer
2. Facilitator
3. Experience seeker
4. Professional/Hobbyist
5. Recharger

• Visit motivations probably include a mix of these (not strictly pure)
• Education/learning is implicit in all 5 categories

My thoughts

• I like the 5 categories of identity-related visit motivations. I'm not sure I understand why this categorization is superior to other categorizations.
• I find 2 ideas in this chapter very compelling:
• 1. Learning is an inherent part of the museum experience. People go to museums expecting to learn something. Like people go to a restaurant expected to get full. Therefore, it doesn't make sense to make "Education" or "Learning" a motivation in and of itself.
• Lack of good tools for measuring learning when learning is about identity rather than performance. 
• How do you measure the identity-learning that occurs when someone already knows something, but the museum learning experience validates what they learned? 
• Or, how do you measure identity-learning when someone comes out of a museum feeling like they are more of a curious person (identify) but don't perform better on a pre- post- set of factual questions?

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.

eat your own dog food

I'm a professional evaluator for museums and libraries. Over the next three years, I want to hone my expertise in evaluating learning outcomes in free-choice learning environments.

I could re-enter the college scene to do a certificate in museum studies, or engage on long and arduous journey of a phd student in education, psychology, instruction design, or evaluation. But I remembered a phrase I heard in one of my graduate courses 5 years ago: "you have to eat your own dog food".

So I am engaging in a self-made, free-choice learning experience to become a knowledge leader in evaluation of learning in informal environments.

This blog is meant to help me learn by writing, document what I discover, and perhaps even be a knowledge center for others to draw from.

Wish me luck, and no small degree of self-motivation.