Someone asked for a video on the Workplace of the Future project, so I created one. Thought I’d share it with you, too. Just a walkthrough with some narration, talking about some of the design decisions.
One learning for me (that I’m sure you knew): a script really helps! It took multiple tries, for a variety of reasons. I’m not a practiced video creator, so gentle, please!
Jane Hart is running her annual Top 100 Tools for Learning poll (you can vote too), and here’s my contribution for this year. These are my personal learning tools, and are ordered according to Harold Jarche’s Seek-Sense-Share models, as ways to find answers, to process them, and to share for feedback:
Those are my tools, now what are yours? Use the link to let Jane know, her collection and analysis of the tools is always interesting.
I’m going a bit out on a limb here, with a somewhat heretical statement: what you learn is more important than how you learn! (You could say pedagogy supersedes curricula, but that’s just being pedantic. ;) And I’m pushing the boundaries of the concept a bit, but I think it’s worth floating as an idea. It’s meta-learning, of course, learning how to learn! The important point is to focus on what’s being developed. And I mean this at two levels.
This was triggered by seeing two separate announcements of new learning opportunities. Both are focused on current skills, so both are focusing on advanced curricula, things that are modern. While the pedagogy of one isn’t obvious (though claimed to be very practical), the other clearly touts the ways in which the learning happens. And it’s good.
So the pedagogy is very hands on. In fact, it’s an activity-based curricula (in my terms), in that you progress by completing assignments very closely tied to what you’ll do on the job. There are content resources available (e.g. expert videos) and instructor feedback, all set in a story. And this is better than a content-based curricula, so this pedagogy is really very apt for preparing people to do jobs. In fact, they are currently applying it across three different roles that they have determined are necessary.
But if you listen to the longer version (video) of my activity-based learning curricula story, you’ll see I carry the pedagogy forward. I talk about handing over responsibility to the learner, gradually, to take responsibility for the activities, content, product, and reflection. This is important for learners to start becoming self-improving learners. The point is to develop their ability to do meta-learning.
To do so, by the way, requires that you make your pedagogy visible for the choices that you made, and why. Learners, to adopt their own pedagogy, need to see a pedagogy. If you narrate your pedagogy, that is document your alternatives and rationales of choices, they can actually understand more about the learning process itself.
And this, to me, is the essence of the claim. If you start a learning process about something, and then hand off responsibility for the learning, while making clear the choices that led there, learners become self-learners. The courses that are designed in the above two cases will, of necessity, change. And graduates from those courses might be out of date before long, unless they’ve learned how to stay current. Unless they’ve learned meta-learning. That can be added in, and it may be implicit, but I’ll suggest that learning to learn is a more valuable long-term outcome than the immediate employability.
So that’s my claim: in the long term, the learner (and society) will be better off if the learner can learn to self-improve. It’s not an immediate claim or benefit, but it can be wrapped around something that is of immediate benefit. It’s the ‘secret sauce’ that organizations could be adding in, whether internally or in their offerings. What surprises me is how seldom I see this approach taken, or even discussed.
In one of my networks, a recent conversation has been on Artificial Intelligence (AI) vs Intelligence Augmentation (IA). I’m a fan of both, but my focus is more on the IA side. It triggered some thoughts that I penned to them and thought I’d share here [notes to clarify inserted with square brackets like this]:
As context, I‘m an AI ‘groupie’, and was a grad student at UCSD when Rumelhart and McClelland were coming up with PDP (parallel distributed processing, aka connectionist or neural networks). I personally was a wee bit enamored of genetic algorithms, another form of machine learning (but a bit easier to extract semantics, or maybe just simpler for me to understand ;).
Ed Hutchins was talking about distributed cognition at the same time, and that remains a piece of my thinking about augmenting ourselves. We don‘t do it all in our heads, so what can be in the world and what has to be in the head? [the IA bit, in the context of Doug Engelbart]
And yes, we were following fuzzy logic too (our school was definitely on the left-coast of AI ;). Symbolic logic was considered passe‘! Maybe that‘s why Zadeh [progenitor of fuzzy logic] wasn‘t more prominent here (making formal logic probabilistic may have seemed like patching a bad core premise)? And I managed (by hook and crook, courtesy of Don Norman ;) to attend an elite AI convocation held at an MIT retreat with folks like McCarthy, Dennett, Minsky, Feigenbaum, and other lights of both schools. (I think Newell were there, but I can‘t state for certain.) It was groupie heaven!
Similarly, it was the time of emergence of ‘situated cognition‘ too (a contentious debate with proponents like Greeno and even Bill Clancy while old school symbolics like Anderson and Simon argued to the contrary). Which reminds me of Harnad‘s Symbol Grounding problem, a much meatier objection to real AI than Dreyfuss’ or the Chinese room concerns, in my opinion.
I do believe we ultimately will achieve machine consciousness, but it‘s much further out than we think. We‘ll have to understand our own consciousness first, and that‘s going to be tough, MRI and other such research not withstanding. And it may mean simulating our cognitive architecture on a sensor equipped processor that must learn through experimentation and feedback as we do. e.g. taking a few years just to learn to speak! (“What would it take to build a baby” was a developmental psych assignment I foolishly attempted ;)
In the meantime, I agree with Roger Schank (I think he was at the retreat too) that most of what we‘re seeing, e.g. Watson, is just fast search, or pattern-learning. It‘s not really intelligent, even if it‘s doing it like we do (the pattern learning). It‘s useful, but it‘s not intelligent.
And, philosophically, I agree with those who have stated that we must own the responsibility to choose what we take on and what we outsource. I‘m all for self-driving vehicles, because the alternative is pretty bad (tho‘ could we do better in driver training or licensing, like in Germany?). And I do want my doctor augmented by powerful rote operations that surpass our own abilities, and also by checklists and policies and procedures, anything that increases the likelihood of a good diagnosis and prescription. But I want my human doctor in the loop. We still haven‘t achieved the integration of separate pattern-matching, and exception handling, that our own cognitive processor provides.
A recent twitter spat led me to some reflections, and I thought I’d share. In short, an individual I do not know attacked one of my colleague Harold’s diagrams, and said that they stood against “everything classical and rigorous”. My somewhat flip comment was that “the classical and rigorous is also outdated and increasingly irrelevant. Time for some new thinking”. Which then led to me being accused of spreading BS. And I don’t take kindly to someone questioning my integrity. (I’m an ex-academic after all! ;) I thought I should point out why I said what I said.
Theories change. We used to believe that the sun circled the earth, and that the world was flat. More relevantly, we used to have management theories that optimized using people as machines. And typical business thinking is still visible in ways that are hierarchical and mechanical. We continue to see practices like yearly reviews, micromanagement, incentives for limited performance metrics, and curtailed communication. They worked in an industrial age, but we’re in a new environment, and we’re finding that we need new methods.
And, let me add, these old practices are not aligned with what we know about how our brains work. We’ve found that the best outcomes come from people working in environments where it’s safe to share. Also, we get better results when we’re collaborating, not working independently. And better outcomes occur when we’re given purpose and autonomy to pursue, not micromanagement. In short, many of the classical approaches, ones that are rigorously defined and practiced, aren’t optimal.
And it’s not just me saying this. Respected voices are pointing in new directions based upon empirical research. In XLR8, Kotter’s talking about leveraging more fluid networks for innovation to complement the hierarchy. In Teaming, Edmondson is pointing to more collective ways to work. And in Scaling Up Excellence, Sutton & Rao point to more viral approaches to change rather than the old monolithic methods. The list goes on.
Rigor is good. Classical, in the sense of tested and proven methods, is good. But times change, and our understanding expands. Just yesterday I listened to Charles Reigeluth (a respected learning design theorist) talk about how theories change. He described how most approaches have an initial period where they’re being explored and results may not be optimal, but you continue to refine them and ultimately the results can supersede previous approaches. Not all approaches will yield this, but it appears to me that we’re getting convergent evidence on theoretical and empirical grounds that the newer approaches to business, as embodied in stuff like Harold’s diagrams and other representations (e.g. the Revolution book), are more effective.
I don’t knowingly push stuff I don’t believe is right. And I try to take a rigorous approach to make sure I’m avoiding confirmation bias and other errors. It’s got to align with sound theory, and pass scrutiny in the methodology. I try to be the one cutting through the BS! I stand behind my claim that new ways of working are an improvement over the old ways. Am I missing something?
In yesterday’s post, I talked about how new tools need to be coupled with practices to facilitate exploration. And I wanted to explore more (heh) about what’s required. The metaphor is old style exploration, and the requirements to succeed. Without any value judgment on the motivations that drove this exploitation, er, exploration ;). I’m breaking it up into tools, communication, and support.
So, one of the first requirements was to have the necessary tools to explore. In the old days that could include means to navigate (chronograph, compass), ways to represent learnings/discoveries (map, journal), and resources (food, shelter, transport). It was necessary to get to the edge of the map, move forward, document the outcomes, and successfully return. This hasn’t changed in concept.
So today, the tools are different, but the requirements are similar. You need to figure out what you don’t know (the edge of the map), figure out how to conduct an experiment (move forward), measure the results (document outcomes), and then use that to move on. (Fortunately, the ‘return’ part isn’t a problem so much!) The digital business platform is one, but also social media are necessary.
What happened after these expeditions was equally important. The learnings were brought back, published, and presented and shared. Presented at meetings, debates proceeded about what was learned: was this a new animal or merely a variation? Does this mean we need to change our explanations of animals, plants, geography, or culture? The writings exchanged in letters, magazines, and books explored these in more depth.
These days, we similarly need to communicate our understandings. We debate via posts and comments, and microblogs. More thought out ideas become presentations at conferences, or perhaps white papers and articles. Ultimately, we may write books to share our thinking. Of course, some of it is within the organization, whether it’s the continual dialog around a collaborative venture, or ‘show your work’ (aka work out loud).
Such expeditions in the old days were logistically complex, and required considerable resources. Whether funded by governments, vested interests, or philanthropy, there was an awareness of risk and rewards. The rewards of knowledge as well as potential financial gain were sufficient to drive expeditions that ultimately spanned and opened the globe.
Similarly, there are risks and rewards in continual exploration on the part of organizations, but fortunately the risks are far less. There is still a requirement for resourcing, and this includes official support and a budget for experiments that might fail. It has to be safe to take these risks, however.
These elements need to be aligned, which is non-trivial. It requires crossing silos, in most cases, to get the elements in place including IT, HR, and operations. That’s where strategy, culture, and infrastructure can come together to create an agile, adaptive organization that can thrive in uncertainty. And isn’t that where you need to be?
I was reading about Digital Business Platforms, which is a move away from siloed IT systems to create a unified environment. Which, naturally, seems like a sensible thing to do. The benefits are about continual innovation, but I wonder if a more apt phrase is instead continual exploration.
The premise is that it’s now possible to migrate from separate business systems and databases, and converge that data into a unified platform. The immediate benefits are that you can easily link information that was previously siloed, and track real time changes. The upside is the ability to try out new business models easily. And while that’s a good thing, I think it’s not going to get fully utilized out of the box.
The concomitant component, it seems to me, is the classic ‘culture’ of learning. As I pointed out in last week’s post, I think that there are significant benefits to leveraging the power of social media to unleash organizational outcomes. Here, the opportunity is to facilitate easier experimentation. But that takes more than sophisticated tools.
These tools, by integrating the data, allow new combinations of data and formulas to be tried and tested easily. This sort of experimentation is critical to innovation, where small trials can be conducted, evaluated, and reviewed to refine or shift direction. This sort of willingness to make trials, however, isn’t necessarily going to be successful in all situations. If it’s not safe to experiment, learn from it, and share those learnings, it’s unlikely to happen.
Thus, the willingness to continually experiment is valuable. But I wonder if a better mindset is exploration. You don’t want to just experiment, you want to map out the space of possibilities, and track the outcomes that result from different ‘geographies’. To innovate, you need to try new things. To do that, you need to know what the things are you could try, e.g .the places you haven’t been and perhaps look promising.
It has to be safe to be trying out different things. There is trust and communication required as well as resources and permission. So here’s to systematic experimentation to yield continual exploration!
I was listening to a tale recounting a time when an organization was going through a change, and had solicited help. And the story surprised me. The short story is that the initial approach being taken weren’t leveraging technology effectively. And it led me to wonder how many organizations are still doing things the old way.
So the story was of a critical organizational change. The hired guns (the typical consulting agency) came into to do their usual schtick, interviewing some people and making recommendations. The problem was, there was no way to interview an appropriately representative sample, and consequently the outcome was going to be less than optimal. The resulting plan was large. dd
In this situation, a colleague stepped in and managed to arrange to use a social platform to do a better job of sharing the intentions and soliciting feedback. You might not be surprised to hear that the subsequent process also yielded greater buy-in. The process resulted in a fine-grained analysis of the plan, with some elements continuing to be executed by the initial partner, others taken on internally, and others discarded. The ultimate cost was reduced far more than the cost to implement this extra step.
The missed opportunity, it turns out, was that the process used didn’t get scaled and implemented for further changes. Some outside factors removed the instigator responsible for the change and it had been done as a ‘stealth’ operation, so awareness wasn’t spread. The hired guns, already entrenched, went back to business as usual.
The eye-opener for me was the fact that the approach initially taken wasn’t leveraging technology. In this day and age, that strikes me as completely unjustifiable! They were better able to support transparency and communication, and as typically happens that yielded both better outcomes and better engagement. Of course, that’s the point of the revolution, getting smarter about aligning technology with how our brains think, work, and learn. It’s just that I forget how far we still need to go.
Just thinking through changes, at every stage of initiatives there’s a benefit:
All these can be facilitated through technology in powerful ways that can’t be done across geographies and timezones without tech.
So here’s my question to you. Is your organization leveraging technology appropriately? And this is both at the level of L&D, and then also organization wide. Is your L&D group working transparently, leveraging social media to both support effective performance and continue to develop? And then are you using that experience to spread the possibilities throughout the organization? That’s the opportunity on tap, and I would really like to see L&D leading the way. Heck, we’re supposed to be the ones who understand how people learn, and when it comes to change, that’s learning too. Let’s own this!
A few posts ago, I wrote about how we might be rushing too fast into cognitive computing. Not that there’s anything wrong with augmenting us, but I was wondering if we’d be better off focusing on developing our non-cognitive systems first. And, of course, it occurred to me after a conversation that there might be another example of this ‘tech fix before smart fix’ problem: adaptive learning over good design. Is this a tech solution to the wrong problem?
So, I’m a fan of adaptive learning. Heck, I led a team that developed an adaptive learning system back circa 2000 (ahead of the times, as always ;). And I know that there are good things we can do with adaptive learning. Some are still relatively impractical (e.g. intelligent tutoring systems), some make sense (e.g. adapting on learner performance), and, of course, some are pretty silly (e.g. learning styles). Still, done well, adaptive learning could and should be a benefit. (And serious games are adaptive!)
But I would posit that before you charge off for adaptive learning, you make sure you’re doing good learning design first. Adaptation on top of good learning design is likely to add some extra benefits, but adaptation on old learning design just doesn’t make sense. And, I’ll suggest, good learning design is cheaper, and likely to have a bigger impact.
So, for instance, adapting a large knowledge-based course will still leave it as a problematic solution in search of a problem. Creating a better course focusing on critical skills with meaningful practice is going to have a bigger impact on the bottom line than adapting the course for learner progress. It’s not a bad thing, but it’s a secondary concern, I reckon.
Games are a special case of adapting on the basis of learner performance. When well done, they’re the next best thing to live practice. Yet, at core, they too need good design.
There are a lot of adaptive solutions emerging, with a strong push to optimize your outcomes. Some of them have a pretty good basis, too. But your goal is to achieve business impact, and what will hit that first, and best, is starting with good design. Adaptive isn’t a panacea, it’s a fillip to design, not a replacement. Master the rules of good design, then come back and add in those extra elements, whether adaptivity and/or game mechanics. Please?
Karen Hough kicked off ATD’s Core 4 event with a lively keynote talking about how improvisation reflects many core factors involved in successful organizational agility. Going through her trademarked elements, she had the audience up and participating and reflecting on interpersonal interactions. She covered important components of a learning organization like openness to new ideas, diversity, and safety and demonstrated ways to help break down the barriers.
