Why I picked it up: Families with young children, as well as school groups, are a key audience for museums. And we all know that museums can provide children with meaningful experiences …. experiences that are cherished, and sometimes change lives in obvious and not-so-obvious ways. But we meaningfully reach too few children, a social justice issue that museums have not truly reckoned with. So of course I picked this up.
What you need to know: The Annie E. Casey Foundation has been working on this annual "by the numbers" on child wellbeing since 1990; this 2017 book uses the most recent data (2015) from the US Census Bureau, the CDC, the US Department of Education, and other sources.
The Data Book examines child wellbeing across four factors, noting if there has been progress (or regression) over the past five years (so, since 2010).
Implications for museums: While this report doesn't directly mention museums, we have to be mindful that its focus is a key audience for all of us. In particular:
The website includes a wonderful tool for focusing in on one specific geographic area for a snapshot of child wellbeing. For some lucky states, it can even be refined by zip code. Go to datacenter.kidscount.org to start pulling your community's numbers.
Read or skip? Anyone who wants to serve all children in their community should skim through the report, check out page 53 for their state's ranking, and consider going to datacenter.kidscount.org to create more refined reports. They make it easy.
Full citation: "2017 Kids Count Data Book: State Trends in Child Well-Being." The Annie E. Casey Foundation. June 2017.
Have a suggestion for my reading list? Email it to me at susie (at) wilkeningconsulting (dot) com.
Why I picked it up: In my research, museums excel at affective learning. It is where true meaning-making takes place, and why arts and history museums particularly excel at it. (Science museums seem to do "information processing" better than affective learning, see below.) This article is 13 years old, yet I think the issue they highlight continues to be one of concern.
What you need to know: Researchers at the MIT Media Lab wrote this manifesto out of concern that thinking and learning is increasingly viewed as "information processing," with affective learning being discounted. Yet they are intertwined. On page 253 they note, "While it has always been understood that too much emotion is bad for rational thinking, recent findings suggest that so too is too little emotion -- when basic mechanisms of emotion are missing in the brain, then intelligent functioning is hindered."
What else is in the manifesto: The focus of the manifesto was on that perceived imbalance of affect and cognition (which I agree with). The authors (there are ten!) disagree on the details, but saw this manifesto as a first step "to begin to construct a science of affective learning." I couldn't help but see parallels between their initial argument and the increasing focus on the more cognitive STEM disciplines (sometimes at the expense of the more affective arts and humanities). They both matter, and they both suffer when they are out of balance.
My red flag: The manifesto then goes on to explore different ideas for measuring affect, or feelings, motivations, etc. But mid-way through I grew increasingly troubled as they seemed to look solely at technology, particularly artificial intelligence, for a solution here. While I agree with them that analog methods to date have drawbacks, it still doesn't make sense to me to go all-in in the opposite direction. It seems like an "information processing" approach to affective learning.
Additionally, their discussion didn't take into account the natural environment of the learner. That is, home life, childhood experiences that affect interest, engagement, and motivation, and just the overall affective environment we all live in every day. Instead, they seemed to be focused on more clinical, artificial environments for measurement.
Thus, I would have liked to see more nuanced ideas that built on the best tools out there … which are likely a mix of human, analog, and artificial intelligence. And also ideas that built on a more complex understanding of the individualized affective experiences we have and affective worlds we all inhabit. I hope in the 13 years since this was originally published their individual work has addressed my concerns. My fear is that we haven't made any progress reconciling the two types of learning or assessment.
Implications for museums: I think their assessment of the affective being discounted is accurate, and a contributor to a bit of an identity crisis about the value and impact of museums. We can have outsized affective impact, but does it matter when the broader, societal focus is on "information processing?" I say yes. More than ever. That being said, it would be interesting for museums to consider some of their measurement ideas (such as the Galvactivators, which one researcher has deployed in a museum setting - why not more in the past decade?) in studies that take into account how affective, and complicated, humans really are.
Read or skip? Skip. I had high hopes, and love their overall thesis. But I find their solutions problematic, since they seem to want to turn affect into more information processing.
Rabbit hole opportunity: The manifesto quotes Marvin Minsky, "… when we change what we call our 'emotional states,' we're switching between different ways to think." I may need to check out his book The Emotion Machine.
Full citation: Picard, R.W. et. al. "Affective Learning - A Manifesto." BT Technology Journal. October 2004.