Quinnovating

Classical and Rigorous

11 April 2017 by Clark 2 Comments

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 My somewhat flip comment was that “the classical and rigorous is also outdated and increasingly irrelevant.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?

Exploration Requirements

5 April 2017 by Clark Leave a Comment

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.

Tools

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.

Communication

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).

Support

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?

Continual Exploration

4 April 2017 by Clark Leave a Comment

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!

Leveraging Technology

29 March 2017 by Clark Leave a Comment

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:

collecting data and determining the issue, via surveys and discussion
developing ideas and approaches in collaboration (transparently, showing your work)
sharing visions about the resulting approach
providing support for expected problems
collaborating to address the unexpected problems
maintaining focus through the change
celebrating successes

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!

Adaptive or just good design?

28 March 2017 by Clark Leave a Comment

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?

Where is Clark?

22 March 2017 by Clark 1 Comment

So, where am I this spring? I was at ATD’s Techknowledge in January, and as this is published I’m on my way to Long Beach for their Core 4 event (sold out; if you’re one of the lucky ones there, say hi!). I’m taking the train (and a bus); look forward to watching the terrain roll by and writing. But there’re a couple more events this spring.

Next week (March 30th), I’ll be giving a talk to ATD’s East Bay chapter on innovation. It’ll cover the materials that were part of my presentation last fall to a government agency and my forthcoming CLO article. We’ll talk about what innovation is (there’s a surprising amount of confusion), what it takes, what the barriers are, and what the role is for L&D. If you’re here in the Bay Area, it should be fun and informative.

Then, in June, I’ll be at the eLearning Guild’s FocusOn Learning event in San Diego. There I’ll be talking about Focus Beyond Learning, i.e. the broader performance ecosystem picture in which mobile, video, and games fit in. Again, if you’re going, say hello! It’s also a chance to see my brother and his family (and hopefully get in a surf ;).

That’s pretty much it for the first part of the year. A bit quiet, but providing time for some writing. Of course, if you are needing a keynote or a workshop…let me know. I have to admit I’m thinking that workshops around the deeper cognitive aspects of learning would be a big boost to organizational L&D.

Top down or bottom up strategy?

21 March 2017 by Clark 1 Comment

In a recent discussion around HR strategy, the question arose about where to start. That is, if you’ve bought into moving into the digital age, where do you begin. The flip answer from the host of the event, a large consulting agency, was to hire them (and my flip reply is to ask whether you want newly minted MBAs following a process designed to be ‘heavy’, or someone coming in light and fast with an adaptive approach ;). But then they got serious, and responded that you shouldn’t be reactive to people’s stated needs, and you needed data to identify what problems are crucial. And I wasn’t satisfied with that, for two related reasons. In short, I thought that was still reactive and that it wasn’t going to help you focus ahead, and that you needed top-down to complement bottom up.

This was buttressed by a post pointed out to me by my ITA colleagues that was arguing a good design strategy was to find out what people needed. And I’m reminded of the quote by Steve Jobs that you can’t just give people what they want, because by the time you do, they’ve changed their minds. And just finding what people need and doing it is a bit reactive, it seems to me, regardless. Even, to be honest, finding the company’s biggest barriers, and addressing them, isn’t a sufficient response. It’s a good one, but it’s not enough.

Interestingly, an HR Director sitting next to me was nodding her head during that response about the data. So afterward I asked her what sort of data she had in mind. I asked about both survey data, and business metrics, and she indicated both (and anything else ;). And I think that’s a good basis. But not a sufficient one.

If you look at most design in the real world, you’ll see that designers cycle between top-down and bottom-up. It helps to check that you’re indeed draining the swamp, but also to ensure you’re not getting eaten by alligators. And that’s the point I want to make.

I’m (obviously) a believer in frameworks. I want conceptual clarity. And I don’t want best practices, I want to abstract best principles and recontextualize them. But I also believe you need to check how you’re going, and regularly test. There are some overarching results that should be incorporated: culture, innovation, performance support, etc. And they can be instituted in ways that address problems yet also develop your ability.

So I do think collecting data on what’s going on, and identifying barriers is important. But if you’re not also looking at the horizon and figuring out where you’re going in the longer term, you could be metaphorically ensuring no flat tires on a trip to the wrong neighborhood. My short answer to their question would’ve been to document where you are, and where you want to get, and then figure out which of the top issues the data indicate sets you on a path to address the rest and build your capability and credibility.

Technology or preparation?

15 March 2017 by Clark 2 Comments

In listening to a recent presentation on the trends affecting the workplace and HR, there was mention about how organizations were using more cognitive technology, AI, etc. and this was changing jobs. There were two additional notes. First, these efforts aren’t (largely) leading to job losses, as these folks were being reskilled. Second, HR wasn’t involved in 65% of this. That’s a concern. But one of the things I wondered was whether all the new, smart technology really would help as much as was intended or needed.

So here’s some context (I may have heard this in conjunction with an early experiment in using mobile devices to support drug trials). Pharmaceutical companies are continually trying new drugs. One claim is that if people would follow their medicine regimens, many of these new drugs wouldn’t be necessary. That is, the drugs are often times trying to require fewer doses with simpler instructions to make up for inappropriate use.

Likewise, the origin of performance support. The question is where does the locus of responsibility belong. Interface design people were upset about performance support systems, arguing (correctly) that performance support was being used to make up for bad system design in the first place. In fact, Don Norman’s book The Invisible Computer was about how interface design wasn’t being brought in early enough. The point being that properly designed interfaces would incorporate support for our cognitive limitations inherently, not externally.

So, many of the things we’re doing are driven by bad implementation. And that’s what I started wondering: are we using smart technology to enhance an optimized workforce, or to make up for a lack of adequate preparation? We could be putting in technology to make up for what we’ve been unsuccessful at doing through training and elearning (because we’re not doing that well).

To put it another way, would we get better returns applying what’s known about how we think, work, and learn than bringing in technology? Would adequate preparation be a more effective approach than throwing technology at the problem, at least in some of the cases? There are strong reasons to use technology to do things we struggle at doing well, and in particular to augment us. But perhaps a better investment, at least in some cases, would be to appropriately distribute tasks between the things our brains do well and what technology does better.

Let me be clear; there are technologies that will do things more reliably than humans, and do things humans would prefer not to. I’m all for the latter, at least ;). And we should optimize both technology and people. I’m a fan of technology to augment us in ways we want to be augmented. So my point is more to consider are we doing enough to prepare people and support them working together. Your thoughts?

Microdesign

14 March 2017 by Clark 3 Comments

There’s been a lot of talk about microlearning of late – definitions, calls for clarity, value propositions, etc – and I have to say that I’m afraid some of it (not what I’ve linked to) is a wee bit facile. Or, at least, conceptually unclear. And I think that’s a problem. This came up again in a recent conversation, and I had a further thought (which of course I have to blog about ;). It’s about how to do microdesign, that is, how to design micro learning. And it’s not trivial.

So one of the common views of micro learning is that it’s just in time. That is, if you need to know how to do something, you look it up. And that’s just fine (as I’ve recently ranted). But it’s not learning. (In short: it’ll help you in the moment, but unless you design it to support learning, it’s performance support instead). You can call it Just In Time support, or microsupport, but properly, it’s not micro learning.

The other notion is a learning that’s distributed over time. And that’s good. But this takes a bit more thought. Think about it. If we want to systematically develop somebody over time, it’s not just a steady stream of ‘stuff’. Ideally, it’s designed to optimally get there, minimizing the time taken on the part of the learner, and yet yield reliable improvements. And this is complex.

In principle, it should be a steady development, that reactivates and extends learners capabilities in systematic ways. So, you still need your design steps, but you have to think about granularity, forgetting, reactivation, and development in a more fine-grained way. What’s the minimum launch? Can you do ought but make sure there’s an initial intro, concept, example, and a first practice? Then, how much do we need to reactivate versus how much do we have to expand the capability in each iteration? How much is enough? As Will Thalheimer says in his spaced learning report, the amount and duration of spacing depends on the complexity of the task and the frequency with which it’s performed.

When do you provide more practice, versus another example, versus a different model? What’s the appropriate gap in complexity? We’ll likely have to make our best guesses and tune, but we have to think consciously about it. Just chunking up an existing course into smaller bits isn’t taking the decay of memory over time and the gradual expansion of capability. We have to design an experience!

Microlearning is the right thing to do, given our cognitive architecture. Only so much ‘strengthening’ of the links can happen in any one day, so to develop a full new capability will take time. And that means small bits over time makes sense. But choosing the right bits, the right frequency, the right duration, and the right ramp up in complexity, is non-trivial. So let’s laud the movement, but not delude ourselves either that performance support or a stream of content is learning. Learning, that is systematically changing the reliable behavior of the most complex thing in the known universe, is inherently complex. We should take it seriously, and we can.

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