In times of uncertainty, disruption, and change it’s easy to view intentional learning as a luxury. It is not.
Complexity — situations when the relations between causes and consequences are unclear and dynamic — requires we learn in order to thrive. Our ability to sense and respond to what we perceive and experience is what determines our ability to act with care in complex contexts. That requires learning.
However, it’s also important to disentangle intentional learning from its means and ends. Learning isn’t restricted to courses, books, websites, and data. In complex contexts, we’re often dealing with novel situations where established knowledge is of limited use. In these cases, existing knowledge requires that we sense-make to place it in the appropriate context. This means that we can’t take things ‘off the shelf’ and use them straight away and expect clear results.
Tiny Experiments
Learning by design involves being selective and what we devote time, care, and attention to in service of building our adaptive capacity.
One way to do this is through the use of ‘tiny experiments’. Anne-Laure Le Cunff has written and spoken about this and illustrated how small, low-risk experiments connects us to parts of our brains that were developed when we were children. Children are natural experimenters and fast learners. For kids, the world is full of unknowns, uncertainty and novelty. What makes them so skilled at adapting and growing is that they ask questions, keep curious, and conduct ‘tiny experiments’ constantly.
Le Cunff, in the video above, walks through her journey of learning that accompanied her studies in cognitive neuroscience. Both personally and professionally, she’s used the idea of tiny experiments to scaffold what she knew, with where she wanted to go.
Data Points
Experiments require data. Data can be in the form of numbers, observations, stories, or reflections. The more attentive we are to the data we collect and the more systematic we approach its collection, the more we can trust it. But unlike with simple, or stable situations, data in a complex, dynamic context needs sensemaking for it to be useful. Sensemaking (see here and here for more details) is a form of engagement with data and is often social, collaborative, and iterative. It’s what converts data into actionable insights.
That’s what learning is.
Build your data capacity be ensuring that you’re paying attention to what’s going on and taking the time to sense-make. Be curious.
It is tempting to pull back on intentional learning when there’s so much uncertainty. It’s tempting to think that, if there’s too much uncertainty and change, learning isn’t practical, especially if things aren’t stable. Instead, this is the time to invest in thoughtful, simple systems that can help you to learn.
Photo by Jennifer Griffin on Unsplash