John Seely Brown spoke eloquently on extreme learning for coping with extreme change, e.g. now. He talked about how extreme learning resembles play and challenged us to create environments where imagination could flourish.
Designing Higher Learning
I’ve been thinking a lot about the higher education situation, specifically for-profit universities. One of the things I see is that somehow no one’s really addressing the quality of the learning experience, and it seems like a huge blindspot.
I realize that in many cases they’re caught between a rock and a hard place. They want to keep costs down, and they’re heavily scrutinized. Consequently, they worry very much about having the right content. It’s vetted by Subject Matter Experts (SMEs), and has to be produced in a way that, increasingly, it can serve face to face (F2F) or online. And I think there’s a big opportunity missed. Even if they’re buying content from publishers, they are focused on content, not experience. Both for the learner, and developing learner’s transferable and long-term skills.
First, SMEs can’t really tell you what learners need to be able to do. One of the side-effects of expertise is that it gets compiled away, inaccessible to conscious access. Either SMEs make up what they think they do (which has little correlation with reality) or they resort to what they had to learn. Neither’s a likely source to meaningful learning.
Even if you have an instructional designer in the equation, the likelihood that they’re knowledgeable enough and confident enough to work with SMEs to get the real outcomes/objectives is slim. Then, they also have to get the engagement right. Social engagement can go a good way to enriching this, but it has to be around meaningful tasks.
And, what with scrutiny, it takes a strong case to argue to the accrediting agencies that you’ve gone beyond what SMEs tell you to what’s really needed. It sounds good, but it’s a hard argument to an organization that’s been doing it in a particular way for a long time.
Yet, these institutions also struggle with retention of students. The learners don’t find the experience relevant or engaging, and leave. If you took the real activity, made it meaningful in the right way, learners would be both more engaged and have better outcomes, but it’s a hard story to comprehend, and perhaps harder yet to implement.
Yet I will maintain that it’s both doable, and necessary. I think that the institution that grasps this, and focused on a killer learning experience, coupled with going the extra mile to learner success (analytics is showing to be a big help here), and developing them as learners (e.g, meta-learning skills) as well as performers, is going to have a defendable differentiator.
But then, I’m an optimist.
Sach’s Winning the Story Wars
On a recommendation, I’ve been reading Jonah Sach’s Winning the Story Wars. While it’s ostensibly about marketing/advertising, which interests me not, I was intrigued by the possibilities to understand stories from a different perspective. I was surprised to find that it offered much more.
The book does cover the history of advertising, going through some classic examples of old-style advertising, and using some surprisingly successful examples to elicit a new model. Some personal stories and revelations make this more than a conceptual treatise.
The core premise is turning your customer into a potential hero of an important journey. You play the role of the mentor, providing the magic aid for them to accomplish a goal that they know they need, but for a variety of reasons may have avoided. The journey is motivated from core values, a feature that resonates nicely with my personal quest for using technology to facilitate wisdom.
The book also provides, as one of the benefits, a nice overview of story, particularly the hero’s journey as synthesized by Joseph Campbell across many cultures and time periods. If you find Campbell a tough read, as many do, this is a nicely digested version. It talks in sensible ways about the resistance, and trials, and ultimate confrontation.
The obvious focus is on new way to build your brand, tapping into higher purpose, not the more negative fears of inadequacy. So this book is valuable for those looking to market in a higher way. And I do intend to rethink the Quinnovation site as a consequence. But I suggest there’s more.
The notion of the individual being offered the opportunity to play a transformative role seems to be a useful framing for learning. We can, and should, be putting learners in meaningful practice roles, and those roles can be coming from learners’ deep motivators. One of the heuristics in learning game design is Henry Jenkins’ “put the player in a role they’d like to be in”. This provides a deeper grounding, put the learner in a role they aspire to be in.
I think this book provides not only practical marketing advice, but also guidance for personal journeys and learning. I think that the perspective of designing stories and roles that are based on personal values to be a great opportunity to do better design. I haven’t completely finished it yet, but I’ve already found enough value in the majority of it to recommend it to you.
Reflections on Experience
The API formerly known as Tin Can provides a consistent way to report individual activity. With the simple syntax of <who> <did> <this> (e.g. <Clark Quinn> <wrote> <a blog post>), systems can generate records across a wide variety of activity, creating a rich base of data to mine for contingencies that lead to success. While machine learning and analytics is one opportunity, there’s another, which is having people look at the data. And one person in particular.
As background, I was fortunate back in 1989 to get a post-doctoral fellowship to study at the Learning Research & Development Center at the University of Pittsburgh. One of the projects that had been developed was a series of intelligent tutoring systems (ITS) that shared an unusual characteristic. Unlike most ITS, which tutor on the domain, these three systems crossed domains (geometric optics, microeconomics, and electrical circuits, if memory serves) but the tutoring was about the systematicity in exploration. That is, the system tracked and intervened on whether you were varying one variable at a time, ensuring your data sampling was across a broad enough range of data points, etc. This reflected work done by the Valerie Shute and Jeffery Bonar some years before on your learning strategies.
I had the further benefit to work under the guidance of Leona Schauble, a very insightful researcher. One of her projects was with Kalyani Raghavan on working to make the learners’ paths in these systems visible and comprehensible to the learner, and they created the Dynamic And Reflective Notation (DARN, heh!) to capture and represent those paths.
Fast forward to today, and one of the big opportunities I see is for performers to reflect on their own paths of action. The granularity at which Tin Can can capture data, and systems might be instrumented to generate data, could be too small to be useful, so some way of aggregating activity to a reasonable level would be necessary, but looking at one’s own paths, and perhaps others, would be a useful way to reflect on process and look for opportunities to improve.
Reflection on action is a powerful learning and improvement process, but recollection isn’t as good as actual recording. The power of working out loud is really seen when those tracks are left for examination. The API has the opportunity to support more than system mining (“oh look, everyone who has this responsibility who touches this resource does way better than those who don’t”). Not that there’s anything wrong with that, but having performers do it too is a great opportunity not to be missed. As the work on protein folding has found, some patterns are better for computer solution, and others for human. We’d be remiss if we didn’t explore the opportunities to be found.
Yvonne Camus #LSCon Keynote Mindmap
Daniel Coyle #LSCon Keynote Mindmap
Daniel Coyle gave a wonderfully funny, passionate, and poignant keynote, talking about what leads to top performance. Naturally, I was thrilled to hear him tout the principles that I suggest make games such a powerful learning environment: challenge, tight feedback, and large amounts of engaging practice. With compelling stories to illustrate his points, he balanced humor and emotional impact to sell a powerful plea for better learning.
Barrier to scale?
I was part of a meeting about online learning for an institution, and something became clear to me. We were discussing MOOCs (naturally, isn’t everyone?), and the opportunities for delivering quality learning online. And that’s where I saw a conflict that suggested a fundamental barrier to scale.
When I think about quality learning, the core of it is, to me, about the learning activity or experience. And that means meaningful problems with challenge and relevance, more closely resembling those found in the real world than ones typically taught in schools and training. There’s more.
The xMOOCs that I’ve seen have a good focus on quality assessment aligned to the learning goal, but there’s a caveat. Their learning goals have largely been about cognitive skills, about how to ‘do’. And I’m a big fan of focusing on ‘do’, not know. But I recognize there’s more, there’s to ‘be’. That is, even if you have acquired skills in something like AI programming, that doesn’t mean you’re ready to be employed as an AI programmer. There’s much more. For instance, how to keep yourself up to date, how to work well with others, what are the nature of AI projects, etc.
It also came up that when polled, a learned committee suggested top things to learn were to lead, to work well on a team, communicate, etc. These are almost never developed by working on abstract problems. In fact, I’d suggest that the best activities are meaningful, challenging, and collaborative. The power of social learning, of working together to receive other viewpoints and negotiate a shared understanding, and creating a unique response to the challenge, is arguably the best way to learn.
Consequently, it occurs to me, that you simply cannot make a quality learning experience that can be auto-assessed. It needs to be rich, and mentored, scaffolded, and evaluated. Which means that you have real trouble scaling a quality learning experience. Even with peer assessment, there’s some need for human intervention with every group’s process and product. Let alone generating the beneficial meta-learning aspects that could come from this.
So, while there are real values to be developed from MOOCs, like developing perhaps some foundation knowledge and skills, ultimately a valuable education will have to incorporate some mechanism to handle meaningful activities to develop the desirable deep understanding. A tiered model, perhaps? This is still embryonic, but it seems to me that this is a necessary step on the way to a real education in a domain.
Leaving Trails
So I was away for the weekend at a retreat with like-minded souls, Up to All of Us, thinking deeply about the issues that concern us. I walked away with some new and renewed friendships, relaxed, and with a few new thoughts. Two memes stuck with me, and the first was “leaving trails”.
For context, the event featured designers – graphic, industrial, visual – but mostly learning designers. In a session on supporting the growth of design awareness, we were being led through an exercise on body-storming (using role plays to work through issues), and one of the elements that surfaced was posting your designs on the walls in places where it’s hard to see others’ work. And I had two reactions to this, the first being that the ability to share work was a culture issue, but the other was a transparency issue.
The point that I brought up was that just seeing the work wasn’t enough, ideally you’d want to understand what was the thinking behind it (not just working out loud, but thinking out loud). That can come from a conversation around the work, but that’s not always possible (particularly if it’s a virtual wall).
And I thought the leader of the exercise, an eloquent and experienced designer, said that you couldn’t really annotate your thoughts about the work. Which I fundamentally disagreed with, but he then went on to talk about showing interim work, specs, etc (and I’m filling in here with some inferences because memory’s not perfect).
What emerged in my thinking was the phrase leaving trails, not just your work, but the trajectories, constraints, and more. As I’ve argued before, I think showing the thinking behind decisions is going to be increasingly important at every level. At workgroup level, individuals will be better able to collaborate if their (prior) work is detailed. Communities of practice similarly need such evidence. Another colleague also presented work on B Corps, benefit corporations, in which businesses will move from shareholder returns to missions, and such transparency will be necessary here as well as for eGovernment. I reckon, what with ClueTrain, any org that isn’t being transparent enough will lose trust.
Of course, the comfort level in sharing gets back to the culture issue: people have to be safe to share their work and give and receive feedback in constructive ways to move forward. Which is really the subject of the next meme.
(NB: one of the principles of the event is Chatham House Rule, which basically says you can’t share personal details without prior approval, and I didn’t ask, so the perpetrators and victims shall remain nameless.)