This week, Derek Sivers published a post entitled Walk and Talk, while Rich Bartlett posted Running a local lodge for your internet friends. Both of them encompass a similar theme: bringing people together to live alongside one another temporarily, creating space for serendipitous conversation and learning.
Derek walked 100km over seven days in Thailand with Liz Danzico, Kevin Kelly, Jason Kottke, Craig Mod and a few others. They naturally broke into small groups to talk while walking the trail. In the evening, the conversation over each dinner was on a topic chosen by one of the walkers, for example Where do you call home? And why?
It sounds like an amazing experience, and one that I personally would slightly prefer to Rich’s experiment in communal living. That’s mainly because I need something to do with myself during all of my waking hours and find unstructured time difficult. I always have done. So walking, which is a long-form activity and topic of conversation, is perfect for me.
What I appreciate about Rich’s post is his giving a peek behind the scenes to show how the economics work. If I was going to organise something like this, it would be based around a walk; perhaps part of Hadrian’s Wall. In fact, these posts are perfectly timed, as I’m going walking with Aaron tomorrow and last time we met we discussed how awesome it would be to invite people for some ‘Whisky and Wisdom’ walks. The whisky would be provided by us, and the wisdom by the group.
I don’t think there’s any ‘perfect’ gathering, and the two approaches — Walk and Talk, and Local Lodge — (quite rightly) reflect the preferences of the organiser. The structure of events is what includes or excludes people, so I guess you need to ensure you’re intentionally including the right people and not unintentionally excluding them. A simple example of this is location. For example, Rich points out that if they had rented a place north of the Pyrenees, more guests would take the train instead of flying. I guess some people might in fact refuse to come if they have to take a flight.
Why do this kind of thing? It’s all about increasing your serendipity surface, and allowing unexpected things to happen. All of the walkers linked above who have written about their experience in Thailand have mentioned the dog that accompanied them for 70km and who they eventually took to the vet. They really formed a bond with the animal, yet this couldn’t have been something that they planned for ahead of time.
This post is mainly me thinking out loud. I’d usually put this kind of thing over at Thought Shrapnel, but I’ve shut up shop there until the new year! More (perhaps) after talking with Aaron tomorrow, and having a think over Christmas…
Coda: after writing this, and just before hitting publish, I came across a post by Ethereum founder Vitalik Buterin about Zuzalu, an experiment that aiming to “create a pop-up mini-city that houses two hundred people, and lasts for two whole months”. It sounds like it was more successful than the crypto cruise ship, at least 😂
As I’ve explained in a previous post, the PFMS heuristic is at the core of the TB872 module I’m currently studying:
Practitioners (P) Which other practitioners do you work with? Framework of ideas (F) What ideas are informing your practice? Do you have a shared set of ideas or are you all working with different ideas? Are there particular ideas you have heard about that you would like to explore further? Methods (M) What methods and tools are you using? Situations of concern (S) Do you have a shared situation of concern? If so, what is it?
The next activity on my list is to fill in what seems like a straightforward 2×2 table, based on the work of De Laat and Simons (2002). The idea, I think, is to introduce the idea of social learning to those who are perhaps only really conceptualise the kind of individual learning done on traditional university undergraduate courses.
Outcomes
Processes
Individual
Collective
Individual
Individual learning
Individual learning processes with collective outcomes
Collective
Learning in social interaction
Collective learning
Taking both the PFMS model and the table together, it’s clear that in my day-to-day work through the co-op of which I’m a founding member, I engage in all four of the kinds of learning:
Individual learning: all knowledge and belief is contextual and theory-laden, so much of what I learn is based on my own personal experience, observation, and internal reflection. For example, I might learn what to say or not say to a colleague in a given situation. Or I might find out about something from a client who works in a slightly different way to me.
Individual learning processes with collective outcomes: although learning often occurs at an individual level, the knowledge or skills we acquire can contribute to a larger group’s collective goal. For example, we can pool the expertise we have as a cooperative, and the experience for clients is greater than if they engaged us as individual consultants. In this quadrant, there’s a symbiotic relationship between personal development and collective advancement.
Learning in social interaction: I’d say about half of my working week is spent ‘co-working’ with members and collaborators of the co-op. As such, learning happens through these interactions by sharing, discussing, and negotiating knowledge. This happens within Communities of Practice (CoP) we’re part of but WAO itself is a CoP, and a place for learning and development as well as for doing business.
Collective learning: although individual people learn, so do groups, communities and organisations. This goes beyond the simple aggregation of individual learning experience to include the creation of new knowledge through collective effort. To achieve this, there needs to be shared goals, co-creation of knowledge, and mutual engagement. In my working week, this happens most often through networks of co-ops we’re part of (e.g. workers.coop) and CoPs (e.g. ORE).
I’ve been working on the Open Recognition Toolkit this week, and during our working group call we discussed the Plane of Recognition we’re using on this page. Although, like De Laat and Simons’ grid, it involves quadrants, what’s really happening is a continuum. In the former case it’s from traditional, formal recognition to non-traditional, non-formal recognition. In the latter, it’s a continuum of learning that mvoes from the individual to the collective, emphasising the connections between personal knowledge acquisition and social, collaboration knowledge creation.
So, in my Situation (S), the Practitioners (P) I’m working with are primarily Laura, and then on few with John and Anne. In the past there have been other members and collaborators involved, too. The Framework of Ideas (F) that we implement has been negotiated over time, but was helped by us all working together for a few years at the Mozilla Foundation. At our monthly co-op days, we reflect on different aspects of our work together, for example creating pages such as Spirit of WAO which allow us to say together things like:
We believe in:
Placing ourselves and our work in historical and social contexts so that we can make thoughtful decisions about our behaviours and mindsets.
Seeing ourselves as part of nature not the rulers of it and acknowledging that there is a climate emergency. We are conscious of the lost lessons and spirit of the indigenous and strive for climate justice.
Sharing resources to help combat prejudice wherever we see it (including, but not limited to: racism, sexism, ageism, ableism, homophobia, transphobia, xenophobia, and hostility relating to education or socio-economic status).
In terms of our methods (M) we try and make these as explicit as possible. So we’re currently using software tools such as Trello, Google Docs, and Whimsical. But we’ve got a Learn with WAO site where we share tools and approaches, which include the templates we use with clients on a range of activities. These are all Creative Commons licensed, as we walk the talk of openness.
In considering the Situations (S) of concern, our work at the co-op often revolves around diverse and sometimes complex projects. Each project brings its own set of challenges and opportunities for learning. Returning to my earlier example of the Open Recognition Toolkit, there were some new things we had to learn about using MediaWiki, even though it’s a tool we’ve used before. Likewise, there was a time when I had to send a somewhat awkward, but necessary, email, to a contributor who was engaging in a way that wasn’t entirely pro-social. As such, the project has required not individual learning but also collective effort to bring together different expertise and perspectives.
A really interesting aspect of thinking through my practice using the PFMS heuristic is how it enables a fluid transition between individual and collective learning processes. For example, I often find that my own, individual, learning about Open Source technologies contributes significantly to the collective knowledge base of the group.
Social learning is essentially learning in practice. It’s not just about exchanging information, but full-bandwidth collaborative experience that inform and shape both our understanding and approaches to work. For example, I’ve seen many instances when people have taken things that they’ve seen us used (and which we learned from others), and then use them in their own practice. Sometimes they even verbalise it: “Oh, I’m going to steal that!”. This encourages a culture of continuous learning and adaptation, which is important in any kind of work environment.
I’m part of the Member Learning group of workers.coop, and in a meeting this week I was trying to explain the value of regular community calls. I was trying to get across the point that the kind of learning we want to foster in the network is not a series of transactional experiences, but rather building a constituency of people who are learning and growing together. It’s not something confined to formal training sessions or workshops. Instead, it’s embedded in our interactions, projects, and shared culture.
As I get further into the TB872 module, I am increasingly appreciative of the way that WAO works internally, with clients, and with other cooperatives. We’ve essentially set up a learning organisation. What’s useful to me is that the PFMS heuristic provides a really valuable lens through which to view and understand these processes, and I’m glad I’m forcing myself to blog all of this so that I can come back to it later!
Imagine we’re all surfers. The ocean we’re in is the educational system, and we’re all trying to ride the wave of knowledge to the shore of understanding. Some of us have master surfers as guides – personal tutors who are right there with us, helping us manoeuvre the currents and ride high on the knowledge wave. They know our strengths, they know our fears, and they ensure we don’t wipe out. These fortunate few reach the shore faster, more smoothly and often with a lot more fun.
Then there are the rest of us. We’re in a giant surf class. There’s one instructor and dozens of us learners. The instructor is doing their best, but they can’t give us all the personalised attention we need. Some of us catch the wave, some of us don’t. This is Bloom’s Two Sigma Problem.
Brought to the fore by educational psychologist Benjamin Bloom in the 1980s, the Two Sigma Problem highlights a gap in education. Personal tutoring can propel students’ performance by two standard deviations – like moving from the middle of a class right to the top 2%. The problem is, we can’t give everyone a personal tutor. It’s just not feasible. So, the question is, how do we give each student the benefits of one-on-one instruction, at scale?
Enter Artificial Intelligence (AI) and, in particular, Large Language Models (LLMs) such as ChatGPT. I’ve been experimenting with using ChatGPT as a tutor for my son during the revision period for his exams. It’s great at coming up with questions, marking them, and suggesting how to improve. This kind of feedback is absolutely crucial to learning. It’s also great at exploring the world and allowing curiosity to take you in new directions.
So, if we revisit the Two Sigma Problem based on what’s possible with LLMs, it looks like there’s a possible solution with multiple advantages:
Personalisation: Like a master surfer guiding us through the waves, AI offers individualised instruction. It can adapt to each learner’s pace, skill level, and areas of interest. It’s like your own personal Mr. Miyagi, providing the right lesson at the right time. Wax on, wax off.
24/7 Availability: With AI, it’s always high tide. The learning doesn’t stop when the school bell rings. Whether it’s the middle of the day or the middle of the night, your AI tutor is there to help, guide, and explain.
Scalability: One-to-one tutoring might not be feasible, but AI makes one-to-one-to-many a reality. An AI tutor doesn’t get exhausted or overbooked. It can help an unlimited number of students at once, ensuring everyone gets the ride of their lives on the knowledge wave.
Feedback and Assessment: Picture a surf instructor who can instantly replay your wipeouts, showing you exactly what went wrong and how to fix it. That’s what AI can do. It provides immediate feedback, helping learners understand and correct their mistakes right away.
Enhanced Resources: LLMs are like a treasure trove of knowledge. Trained on a vast array of educational content, they’re like having the British Library at your fingertips, ready to generate explanations, examples, and answers on a multitude of topics.
Removing Bias: AI doesn’t care about your background, your accent, or the colour of your board shorts. When designed and trained properly, it treats all learners equally, providing a level playing field.
No technology is a silver bullet. As an educator, I know that while curiosity and feedback is really important, there’s nothing like another human providing emotional input — including motivation. AI is here to support, not replace, our human guides.
Even though it’s early days, we’re already seeing some really interesting developments in the application of LLMs in education. I’m no fan of Microsoft, but I will acknowledge that a feature they have in development called ‘passage generation’ looks interesting. This tool reviews data to create personalised reading passages based on the words or phonics rules a student finds most challenging. Educators can customise the passage, selecting suggested practice words and generating options, then publish the passage as a new reading assignment. I find this kind of thing really useful in Duolingo for learning Spanish. Context matters.
As a former teacher, I know how important prioritisation can be for the limited amount of time you have with each student. And as a parent, I’m a big believer in the power of deliberate practice for getting better at all kinds of things. Freeing up teachers to be more like coaches than instructors has been the dream ever since someone came up with the pithy phrase “guide on the side, not sage on the stage”.
One of the main concerns I think a lot people have with AI in general is that it will “steal our jobs”. I’d point out here that the main problem here isn’t AI, it’s capitalism. Any tool or system be used for good or for ill. If you’re not sure how we can approach this post-scarcity world, I’d recommend reading Fully Automated Luxury Communism by Aaron Bastani. Of course, regulation is and should be an issue, too.
The main issue I see with this is centralised LLMs run by companies running opaque models and beholden to shareholders. That’s why I envisage educational institutions running local LLMs, or at least within a network that only connects to the internet when it needs to. Just as Google Desktop used to allow you to search through your local machine and the web, I can imagine us all having an AI assistant that has full context, while preserving our privacy.
So the way to approach any new tool or service is to ask critical questions such as “who benefits?” but also to fully explore what’s possible with all of this. I’m hugely hopeful that AI won’t lead us into a sci-fi dystopia, but rather help to even out the playing field when it comes to human learning and flourishing.
What do you think? I’d love to hear in the comments!
Image remixed from an original on the SkillUp blog. Text written with the help of ChatGPT (it’s particularly good at coming up with metaphors, I’ve found!)
