So, my attention has swung back to learning to learn. So that requires me to think what about meta-learning is worth paying attention to, promulgating, etc. Which is kind of meta-learning-to-learn, no? So, why, and what?
To begin with, as I was explaining to my LDA co-director, Matt Richter, I’ve been a fan of learning to learn for literally decades. As a grad student, I served as a teaching assistant for a class on critical thinking (using Halpern’s Thought and Knowledge). My own PhD thesis was on teaching thinking skills! I joined up with the late Jay Cross to form the Meta-Learning Lab (MLL), promoting these views, after finding it was a shared interest amongst those of us in the East Bay who knew Bill Daul.
So, it makes sense that this theme, connected strongly to my life-long pursuit of learning through technology, would resurface. I think the MLL was ahead of it’s time, but I’m seeing more resurgence of interest. I think that’s for several reasons, for one orgs are realizing that they need to become more agile, and learning skills are key. Also, generative artificial intelligence is raising questions about what value we add, and I’ll go out on a limb and state that to learn is human. Yes, machines learn, but…they really don’t have an understanding, as I’ve pointed to Harnad’s Symbol Grounding Problem before. Arguably, we do increase understanding through learning.
It’s also clear that we’re not optimal. Some argue that there aren’t ‘learning to learn’ skills, yet point out that certain ways of doing things (across domains, mind you), are more and less efficient. You can be better or worse at reading, experimenting, even interacting with others. Sorry, but to me, that’s learning to learn.
Also, I see more and more folks recognizing that these skills are desirable, and undeveloped. So, if we wanted to turn that around, and develop them, what’s needed? That’s where I’m at, compiling a list of skills, and trying to generate a unified model. Yes, there are components – Jarche’s PKM, cognitivce science’s plan-monitor-evaluate, etc – but I reckon it’s best if they’re integrated. That includes breaking down so-called 21C skills into actual component.
Still underway, and I welcome pointers to comprehensive models. I confess that I quibble if I think they’re insufficient (there are lots of models that have necessary, but not sufficient, coverage). I don’t mind reviewing (and stealing :) any elements I think I’ve missed, but I also won’t tout anything that’s incomplete. That includes, BTW, culture, and importantly, how L&D facilitates this. That’s where I am going, of course; we need to find days to build these skills into what we do. For instance, one of our LDA members talked about making pedagogy clear when you deliver a learning experience. That’s exactly right. So, what else? What’s the comprehensive list? Stay tuned.

