John Medina of Brain Rules fame opened the second day of ATD’s TechKnowledge conference. In a rapid-paced and amusing presentation, he went through how we understand others, and can get better. This was, he hypothesized, the core of talent development: understanding others and helping them improve.
Kevin Carroll opened the TechKnowledge 18 conference with his story of triumphing over a rough beginning and the lessons he’s learned.
As a response to my post where I offered to ‘listen’, I’ve had several comments giving me topics, and so I thought I should respond. One asked about meta-learning (learning to learn), in the particular situation of courses with a variety of expertise levels, and getting into issues of learner responsibility. The author pointed to a presentation on learning to learn, that had a nice framework, and I thought I should elaborate.
The framework mentioned talked about three stages of expertise: apprentice, journeyman (using the traditional term, is there a move to ‘journey person’ or…?), and mastery. Within these, you watch as an apprentice, practice as a journeyman, and share as a master. Which isn’t a bad approximation of the whole ‘cognitive apprenticeship‘ approach.
The article misses some nuances, of course (and the author acknowledged this). For instance, in practice, the role of deliberate practice is important, it’s not just repetition, but the ‘right’ repetition. And my commenter brought up the role of epistemological stance, that the learners need to own their own learning.
The starting point from the comment, however, was the fact that the audiences being seen varied in background knowledge; some were relative novices, others were experienced. To me, that calls for a ‘leveling’ approach. Here, you have preparatory material that you can test out of, otherwise you go through it. This helps ensure that the audience starts the learning experience with a baseline of at least language. You don’t want to be presenting content in that valuable face-to-face time!
The details involved in making learning experiences work are many. It’s about what to teach, how, how to address audience diversity, and more. It’s about meta-learning for ourselves and our learners. That’s why I advocate learning about how we learn, the cognitive science that (should) drive how we do what we do. So, who wants to learn?
I mentioned in yesterday’s post that one thing I do in getting objectives is focus on decisions. And, simple ones will get automated; we can train AI to handle these. What will make the difference between ordinary and extraordinary organizations is the ability to make decisions in this new VUCA environment (volatile, uncertain, complex, and ambiguous). And it made me wonder how you develop the ability to make better choices.
AI can be trained in a couple of ways to answer questions and make these decisions. We can use machine learning to train a system on a historical database (watching out for bias). We can use semantic analysis to read documents and make a system that can answer questions about them. But such systems are very limited; they can’t handle questions at the periphery of the knowledge well, and fall apart at related areas. Which people are better at, if their expertise has been developed.
Now, developing this expertise isn’t straightforward. If there were simple decision trees, we could automate them as above. Instead, what works best is expert models that have been abstracted across dialog and practice. This needs to be augmented with an awareness of adjacent fields. So, for instance, for instructional design, we should have an awareness of interface design, graphic design, media production, etc. So how do we develop this?
We certainly need to develop the expert models we know play a role. But this gets circular with the above unless we find a way to break out of the predictable. I suggested one approach to this with my ‘shades of grey’ post, having groups work together to make categorization choices: is this or is this not legal. This was, however, more focused on compliance and there’s a much wider situation.
We first need to identify the situations, the relevant models, and the scope of likely variation. We can’t provide specific data (or we’d train the system on it), so we need to anticipate a spread. And we could just train that, but I want to go further.
I’d want to use such a process to choose situations, and then design group work, for the reasons I identified here. (Resourced with models and examples, of course.) We want to get learners working together to address complex problems. We want them to use their various understandings to illuminate the underlying models. If you can get productive discussion (and this needs to be designed in and facilitated), the learners’ thinking will be enriched. (And they may have folks to call on when the situations do arise ;).
Collaboration in learning is second best to collaboration in problem-solving. We should do the latter when we can, but we should do the former anyway. For better learning, and for those times when there isn’t the luxury of working with others.
I reckon this would lead to better decision-making ability. What do you think?
“Conversations are the stem cells of learning.” – Jay Cross
I recently read something that intrigued me. I couldn’t find it again, so I’ll paraphrase the message. As context, the author was talking about how someone with a different world view was opining about the views of the author. And his simple message was “if you want to know what I, or an X, thinks, ask me or an X. Don’t ask the anti-X.” And I think that’s important. We need to talk together to figure things out. We have to get out of our comfort zone.
It’s all too evident that we seem to be getting more divisive. And it’s too easy these days to only see stuff that you agree with. You can choose to only follow channels that are simpatico with your beliefs, and even supposedly unbiased platforms actually filter what you see to keep you happy. Yet, the real way to advance, to learn, is to see opposing sides and work to find a viable resolution.
Innovation depends on creative tension, and we need to continue to innovate. So we need to continue to engage. Indeed, my colleague Harold Jarche points to the book Collaborating with the Enemy and argues that’s a good thing. The point is that when things are really tough, we have to go beyond our boundaries. And life is getting more complex.
So I keep connections with a few people who don’t think like me, and I try to understand the things that they say. I don’t want to listen just to those who think like me, I recognize that I need to understand their viewpoints if we’re going to make progress. Of course, I can’t guarantee reciprocity, but I can recognize that’s not my problem.
And I read what academic research has to say. I prefer peer-review to opinion, although I keep an open mind as to the problems with academic research as well. I have published enough, and reviewed many submissions, so I recognize the challenges. Yet it’s better than the alternative ;).
This is, however, the way we have to be as professionals. We have to understand other viewpoints. It matters to our world, but even in the small little worlds we inhabit professionally. We need to talk. And face to face. It matters, it turns out. Which may not be a surprise. Still, getting together with colleagues, attending events, and talking, even disagreeing (civilly) are all necessary.
So please, talk. Engage. Let’s figure stuff out and make things better. Please.
The end of the calendar year, although arbitrary, becomes a time for reflection. I looked back at my calendar to see what I’d done this past year, and it was an interesting review. Places I’ve been and things I’ve done point to some common themes. Such are the nature of reflections.
One of the things I did was speak at a number of events. My messages have been pretty consistent along two core themes: doing learning better, and going beyond the course. These were both presented at TK17 that started the year, and were reiterated, one or the other, through other ATD and Guild events.
With one exception. For my final ATD event of the year, I spoke on Artificial Intelligence (AI). It was in China, and they’re going big into AI. It’s been a recurrent interest of mine since I was an undergraduate. I’ve been fortunate to experience some seminal moments in the field, and even dabble. The interest in AI does not seem to be abating.
Another persistent area of interest has been Augmented Reality (AR) and Virtual Reality (VR). I attended an event focused on Realities, and I continue to believe in the learning potential of these approaches. Contextual learning, whether building fake or leveraging real, is a necessary adjunct to our learning. One AR post of mine even won an award!
My work continues to be both organizational learning, but also higher education. Interestingly, I spoke to an academic audience about the realities of workplace learning! I also had a strategic engagement with a higher education institution on improving elearning.
I also worked on a couple of projects. One I mentioned last week, a course on better ID. I’m still proud of the eLearning Manifesto (as you can see in the sidebar ;). And I continue to want to help people do better using technology to facilitate learning. I think the Quinnov 8 are a good way.
All in all, I still believe that pursuing better and broader learning and performance is a worthwhile endeavor. Technology is a lovely complement to our thinking, but we have to do it with an understanding of how our brains work. My last project from the year is along these lines, but it’s not yet ready to be announced. Stay tuned!
Expertise is an elusive thing. It comes from years of experience in a field. However, it turns out that it doesn’t just accumulate. You need very specific practice and/or useful feedback to develop it. And the more expertise one has, the better you are able to apply it to situations. Which has implications for what you do and when and how you do it.
Expertise is valuable. The properties of expertise include that it’s compiled away to be essentially automatic. Which implies it’s not accessible for conscious introspection. (Which is why experts quite literally cannot tell you what they do!) On the other hand, their responses to situations in their area of expertise are likely to be as good as you can get. They apply mental models they’ve developed to solve problems.
If you want to develop expertise as an individual, you need to understand how to practice. Deliberate practice, as Ericsson details, is the key. You need to practice at the limits of your ability, and consciously learn from the outcomes. It’s not just doing the job, it’s pushing the boundaries, and actively reflecting.
If you want to develop expertise as an organization internally, the situation is very much the same. You need resources to develop people, and stretch assignments with feedback and coaching to optimally develop the expertise.
Of course, you can bring in expertise from outside, as well. The question then becomes one of when and who. You can contract out work, which makes sense when the activity isn’t part of your core ability. Outsourcing to technology or external expertise is fine for things that are in areas that are well developed.
Otherwise, you can bring in consultants. The latter is particularly useful when you are moving in a new direction or want to deepen your understandings. A good consultant will work with you to not only help address the situation, but internally develop your own understanding. The key is working collaboratively and transparently. Yes, I’m a vested interest, but I believe these things are true on principle and should be in practice.
Expertise is core to situations you know you need expertise in, but also in those that are new. When you need innovation, you need expertise in the complementary areas that you are applying to address the situation. You don’t want to develop learning except in the problem. At least, that’s my expert opinion. Which, of course, is on tap if needed ;).
Sparked by a colleague, I’m reading The Digital Transformation Playbook, by David Rogers. In the chapter on innovation, he talks about two types of experimentation: convergent and divergent. And I was reminded that I think of two types of innovations as well. So what are they?
He talks about how experimentation is the key to innovation (in fact, the chapter title is Innovate by Rapid Experimentation). His point is that you need to be continually experimenting, rapidly. And throughout the organization, not just in separate labs. Also, it’s ok to fail, as long as the lesson’s learned. And then he distinguishes between two types of experimentation.
The first is convergent. Not surprisingly, this is when you’re trying to eliminate options and make a decision. This is your classic A/B testing, for example. Here you might try out two or three different solutions, to see which one works best. You create the options, and have measures you’ll use to determine the answer. You might ask: should we use a realistic video or a cartoon animation? A situation where there isn’t a principled answer, and you need to make a decision.
Divergent experimentation is, instead, exploratory. Here you give folks some ideas, or a prototype, and see what happens. You don’t know what you’ll get, but you’re eager to learn. What would a scenario look like here?
These roughly correspond to the two types of innovation I think of. One is the ‘we need to solve this’ type. I think of this as short-term innovation. Here we are problem-solving or trouble-shooting. You bring together a team of relevant capabilities and otherwise as diverse as possible. You facilitate the process. And you’re likely to try convergent experimentation.
At the other end is the serendipitous, long-term innovation that happens because you create an environment where ideas can gestate. You’ve got access to the adjacent possible, and the opportunities to explore and share. It’s safe to experiment and fail. People are supposed to take time to reflect! This is more closely aligned to divergent experimentation.
Note that this is all learning, as you don’t know the answers when you start! The success of organizational learning, however, is a product of both. You need to solve the problems you know you have, and allow for ideas to generate solutions to problems you didn’t know you had. Or, more optimistically, to search through idea spaces for opportunities you didn’t know to look for.
Rogers is right that continual experimentation is key. It has to become baked into how you do what you do. Individually, and organizationally. And you can’t really get it unless you start practicing it yourself. You need to continually challenge yourself, and try things both to fix the problems, and to explore things that are somewhat tangential. Your own innovations will be key to your ability to foster them elsewhere.
Too many orgs are only focused on the short-term. And while that may solve shareholder return expectations, it’s not a receipt for longer-term organizational survival. You need both types of innovations. So, the question is whether you can assist your org in making a shift to the serendipitous environment. Are you optimizing your innovation?
I sat in on a twitter chat yesterday, #DLNChat, that is a higher ed tech focused group (run by EdSurge). The topic was the link between higher ed and job skills, and I was a wee bit cynical. While I think there are great possibilities, the current state of the art leaves a lot to be desired.
So, I currently don’t think higher ed does a good job of preparation for success in business. Higher ed focuses too much on knowledge, and uses assignments that don’t resemble the job activities. Frankly, there aren’t too many essays in most jobs!
Worse, I don’t think higher ed does a good job of developing meta-cognitive and meta-learning skills. There is little attempt to bridge assignments across courses, so your presentations in psychology 101 and sociology 202 and business 303 aren’t steadily tracked and developed. Similarly with research projects, or strategy, or… And there’re precious little (read: none) typically found where you actually make decisions like you would need to.
And, sadly, the use of technology isn’t well stipulated either. You might use a presentation tool, a writing tool, or a spreadsheet, maybe even collaboratively, but it’s not typically tied to external resources and data.
Yes, I know there are exceptions, and it may be changing somewhat, but it still appears to be the case. Research, write a paper, take a test.
Yet the role of developing higher skills is possible and valuable. We could be providing more meaningful assignments, integrating meta-learning layers, and developing both meaningful skills and meta-skills.
This doesn’t have to be done at the expense of the types of things professors believe are important, but just with a useful twist in the way the knowledge is applied. It might lead to a revision of the curriculum, at least somewhat, but I reckon it’d likely be for the better ;).
Our education system, both K12 and higher-ed, isn’t doing near what it could, and should. As Roger Schank says, only two things wrong: what we teach, and how we teach it. We can do better. Will we?
Ok, so I can be a bit of a pedant. Blame it on my academic background, but I believe conceptual clarity is important! If we play fast and loose with terminology, we can be be convinced of something without truly understanding it. Ultimately, we can waste money chasing unwarranted directions, and worse, perhaps even do wrong by our learners.
Where do the problems arise? Sometimes, it’s easy to ride a bizbuzz bandwagon. Hey, the topic is hot, and it sounds good. Other times, it’s just too hard to spend the effort. Yet getting it wrong ends up meaning you’re wasting resources.
Let’s be clear, I’m not talking myths. Those abound, but here I’m talking about ideas that are being used relatively indiscriminately, but in at least one interpretation there’s real value. The important thing is to separate the wheat from the chaff.
Some concepts that are running around recently and could use some clarity are the following:
Microlearning. I tried to be clear about this here. In short, microlearning is about small chunks where the learning aggregates over time. Aka spaced learning. But other times, people really mean performance support (just-in-time help to succeed in the moment). What you don’t want is someone pretending it’s so unique that they can trademark it.
70:20:10. This is another that some people deride, and others find value in. I’ve also talked about this. The question is why they differ, and my answer is that the folks who use it as a way to think more clearly about a whole learning experience find value. Those who fret about the label are missing the point. And I acknowledge that the label is a barrier, but that horse has bolted.
Neuro- (aka brain- ). Yes, our brains are neurologically based. And yes, there are real implications. Some. Like ‘the neurons that fire together, wire together’. And yet there’re a whole lot of discussions about neuro that are really at the next higher level: cognitive. This is just misleading folks to make it sound more scientific.
Unlearning. There’s a lot of talk about unlearning, but in the neurological sense it doesn’t make sense. You don’t unlearn something. As far as we can tell, it’s still there, just increasingly hard to activate. The only real way to ‘unlearn’ is to learn some other response to the same situation. You learn ‘over’ the old learning. Or overlearn. But not unlearn. It’s an unconcept.
Gamification. This is actually the one that triggered this post. In theory, gamification is the application of game mechanics to learning. Interestingly, Raph Koster wrote that what makes games fun are that they are intrinsically about learning! However, there are important nuances. It’s not just about adding PBL (points, badges, and leaderboards). These aren’t bad things, but they’re secondary. Designing the intrinsic action around the decisions learners need to acquire is a deeper and more meaningful implication. Yet people tend to ignore the latter because it’s ‘harder’. Yet it’s really just about good learning design.
There are more, of course, but hopefully these illustrate the problem. (What are yours?) Please, please, be professional and take the time to get clear about our cognitive architecture enough to ensure that you can make these distinctions on your own. We need the conceptual clarity! Hopefully then we can reserve excitement for ideas that truly add value.