Quinnovating

Reordering the Serious eLearning Manifesto

2 February 2017 by Clark 7 Comments

So, as you may know (and if you don’t, you should), almost three years ago now I teamed up with colleagues Michael Allen, Julie Dirksen, & Will Thalheimer (all worth knowing about) and put together the Serious eLearning Manifesto. And I believe it’s a good thing. But it needs an update.

So, we were (and are) frustrated with what was and is happening under the rubric of eLearning. Michael was intrigued by the concept of Serious Games, and wondered why we didn’t treat elearning seriously as well. (A rant I’ve made before ;). He came up with the idea of a manifesto, and we agreed to work with him on it. And we finalized a list of 8 ways in which typical elearning differed from what we call Serious eLearning, and 22 research concepts behind it (drawn from work across decades and around the world, we don’t claim to own it). And we put it out there for free (Michael graciously sponsored it through his company with no attribution).

We don’t claim that these are the only ways that good elearning differs from what’s typically seen, of course, we just feel that these are the eight most serious ones that, if followed, have the biggest impact on your learning outcomes. It wasn’t easy getting the four of us to agree, and we’ve received quite a few suggestions of how it could be expanded or improved, but we’re comfortable that this is a reasonable stance to take.

And it’s gotten a reasonable amount of attention. We had 30+ ‘trustees’ who put their names to it (and many more worthies would have), as well as sponsorship by the appropriate societies. We’ve been given opportunities to speak and present about it. And we’ve got an ever-growing list of signatories. People recognize that it’s right, even if it hasn’t gotten the traction we’d like (e.g. everyone making a concerted effort to shift to it since it’s release).

HOWEVER…

When I explain it to others, I realize that I have a trouble with the ordering. Most of it’s great, but one element somehow slipped out of position, in my mind. So I’ve made an attempt to remedy that, reordering the list. I’ve made this as similar to the original graphic as possible, except that I’m not using the right fonts. So sue me.

What’s different is that I’ve grouped Real-World Consequences with Authentic Contexts and Realistic Decisions. The consequences naturally follow from realistic decisions made in authentic contexts. Then we can talk about Spaced Practice and Individualized Challenges. The latter of which, by the way, is the only thing that is (mostly) specific to elearning, otherwise it’s applicable to learning in general. The rest is the same.

So this is the version that I’ll be using, going forward. I still hope you’ll visit the site, sign on, and work towards it. No one expects you to get all the way right away, but it is the right way to go. If you need help, I’m happy to assist.

Other writings

1 February 2017 by Clark Leave a Comment

It occurs to me to mention some of the other places you can find my writings besides here (and how they differ ;). My blog posts are pretty regular (my aim is 2/week), but tend to have ideas that are embryonic or a bit ‘evangelical’. First, I’ve written four books; you can check them out and get sample chapters at their respective sites:

Engaging Learning: Designing e-Learning Simulation Games

Designing mLearning: Tapping Into the Mobile Revolution for Organizational Performance

The Mobile Academy: mLearning For Higher Education

Revolutionize Learning & Development: Performance and Information Strategy for the Information Age

They’re designed to be the definitive word on the topic, at least at the moment.

I’ve also written or co-written a number of chapters in a variety of books. The books include The Really Useful eLearning Instruction Manual, Creating a Learning Culture, Michael Allen’s eLearning Annual 2009, and a bunch of academic handbooks (Mobile Learning, Experiential Learning, Wiley Learning Technology ;). These tend to be longer than an article, with a pretty thorough coverage of whatever topic is on tap.

Then there are articles in a variety of magazines. These tend to be aggregated thoughts that are longer than a blog post, but not as through as a chapter. In particular, they are things I think need to be heard (or read). So, my writing has shown up in:

eLearnMag

Learning Solutions

CLO

The topics vary. (For the eLearnMag ones, you’ll have to search for my name owing to their interface, and they tend to be more like editorials.)

And then there are blog posts for others that are a bit longer than my usual blog post, and close to an article in focus:

The Deeper eLearning series for Learnnovators

A monthly article for Litmos.

These, too, are more like articles in that they’re focused, and deeper than my usual blog post. For the latter I cover a lot of different topics, so you’re likely to find something relevant there in many different areas.

I’m proud of it all, but for a quick update on a topic, you might be best seeing if there’s a Litmos post on it first. That’s likely to be relatively short and focused if there is one. And, of course, if it’s a topic you’re interested in advancing in and I can help, do let me know.

Silo APIs?

26 January 2017 by Clark 3 Comments

I was in a conversation with my colleague Charles Jennings about organizational innovation, and one of the topics that arose was that of barriers to successful organizational function. In particular, we were talking about how the division of responsibility between organizational development (OD), leadership development, and learning & development is a problem. And I think the problem is bigger. Separating out functions into silos makes sense in a deterministic world, but that doesn’t characterize our current environment.

Now, separation of functions can be useful. Certainly in software engineering, having application program interfaces (APIs) have led to the ability to connect powerful capabilities. A program can call a function and get data returned via an API, and the software doesn’t have to care how the function’s carried out.

In the org equivalent we could have a business unit request a course, for example, and L&D responds with said course. In fact, that’s not atypical. Yet it’s problematic in human terms. The business unit may not have done the due diligence, the performance analysis, that ensures a course is the right solution.

Ok, we could change it: the business unit could indicate the performance problem and L&D could respond. However, again there’s a problem. Without understanding how things are done, L&D’s solution won’t be contextually accurate. Any intervention won’t reflect how things are done unless interactions occur.

And that’s the point. Any meaningful work – problem-solving, trouble-shooting, improvement, innovation, research, design etc – any learning, is complex. And, done right, they inherently require engagement and interaction. Moreover, we also know that the best solutions come from creative friction, people interacting. Communication and collaboration is key!

Engagement between silos works best when you mix members from each. Or, to put it another way, breaking down the silos is the only way to get the best outputs for the important work, the work that will advance the organization whether removing errors, creating new products or processes, etc.

People are complex (the human brain is arguably the most complex thing in the known universe). Solutions that tap into that complexity, instead of trying to avoid it, are bound to yield the best insights. We’ve now got a lot of insight into processes that facilitate getting the best outcomes. It’s time to engage with it, to the benefit of the organization.

Culture or Cultures?

25 January 2017 by Clark Leave a Comment

A twitter pointer led me to an HBR article arguing that We’re Thinking about Organizational Culture all Wrong. In it, the author argues that it’s fallacious to think that there’s just one organizational culture, , and that all people buy into it. I agree, and yet where the author leads us is, I think, misleading, or at least not as helpful as it could be.

The argument includes two major thrusts. The first is that the cultural values may be interpreted differently. What you mean by ‘free’ and what I mean may differ. Take, for instance, the difference between ‘free beer’ and ‘free speech’ (a classic example). And this certainly can be the case. The second is that people may comply with the culture even if they don’t agree with it. There are multiple reasons, such as job security, that could support this.

The result, according to the article, is that corporate ‘culture’ isn’t a set of shared values, it’s a “web of power relationships”. That’s quite a leap, but the point is apt: these relationships can facilitate, or hinder, individual goals. However, one statement near the end rings wrong for me:

“Reliance on culture as a way to create unity can mislead those in positions of power into thinking that the core values expressed by the organization are actually uncritically accepted by employees.”.

I agree, but I think it’s simplistic. No one in power should be naive enough to believe that anyone uncritically accepts any values. Instead, the view should be to recognize what core values facilitate the most effective outcomes for the organization, and then follow some well-tested rules about change:

sell the vision
make it a choice
support
know how to address the expected problems
be prepared to address the unexpected
evangelize
reward
test and tweak
persist

It may make sense to start small and spread virally rather than make it an overall change initiative. Still, I think it’s a worthwhile goal.

There is a clear value proposition about having a culture that supports innovation, and identifiable components. Abandoning the effort because culture is complex seems a missed opportunity. The benefits are big. Cultures are developed and do change. Doing so systematically, and systemically, seem to me to be the path to competitive success. What am I missing?

Errors and misconceptions

19 January 2017 by Clark Leave a Comment

When I was a grad student, my advisor looked a lot at error. HIs particular focus was to prevent it through good interface design. He characterized them as of two types: slips and mistakes. Slips are when you have the right intent, but from elements of our architecture end up making the wrong move. Mistakes are when your intentions are wrong. From the learning perspective, it’s the latter we want to address.

So, I’ve argued that there’s some randomness in our architecture. We’re bad at doing rote things, even when we know what to do. Athletes, for instance, practice for hours a day, day after day and their competitions are typically decided by who makes the fewest errors. In fact, we’ve identified and created support tools to minimize these errors, like checklists. We’re supposed to design systems so they minimize the opportunity for slips. These don’t always work; I was embarassed in my most recent presentation to find a couple of typos. They clearly had come in via auto-correct, but I’d missed them subsequently! There may well be one in this post, too, some form of slip or another.

More importantly are what are termed ‘mistakes’. Here we’re starting with the wrong intent. This, from a learning perspective, is what I term a ‘misconception’. Here, we’re supposed to be using a particular mental model to guide our performance, but we might not have the right model, or no model and we import a wrong one. Again, we should design to minimize these as well, but we also want to make sure we’ve got the right mental models to begin with. This happens if we haven’t been given the right one, or activated an irrelevant one inappropriately.

Ideally, we want to identify the most likely ways we go wrong, and then make sure we understand what those are and why. From there, then make them available as options in practice. Then we can remediate them at the moment. Of course, we’re also supposed to have the right model, and highlight how the model guides performance in examples, and then again through the feedback on both correct and incorrect performance.

Designing practice that supports making the right decisions in context, and also the opportunity to make the wrong choice and get useful feedback, is a key learning design skill. Quiz questions that just test knowledge aren’t likely to lead to meaningful difference in performance. Practice where the alternative to the right answer are silly or obvious (unless you really know the model) is a waste of your resources and your learner’s time. Make practice meaningful. Not doing so is an error, too!

Cognitive Business

18 January 2017 by Clark Leave a Comment

One of my mantras is that organizations need to align better with how we think, work, and learn. However, my focus has been specifically on what L&D can be doing (as that’s the folk I mostly talk to). But it occurs to me that it really goes farther. There are applications of cognitive science (including neuroscience, cognitive psychology, sociology, philosophy, anthropology, etc) to more areas of business than just L&D. And it’s worth being explicit about this.

I was recently reading how marketing has leveraged understanding of behavior change at a deep level. We need to incorporate this in our learning design, but it should go beyond training and learning and be involved in helping people understand why their work is important and how they contribute.

And similarly, the notion that our thinking is both situated (e.g. reconstructed in the moment, not formally abstract) and distributed (across representations, not all in the head) has broader implications. It’s not just about performance support, but should influence policies and tools as well.

And the fact that innovation is social, and an outcome of slow percolation, influences more than facilitating communication and collaboration. It should influence corporate culture and expectations and time frames.

The list goes on: research says that organizational change works better starting small and scaling rather than a monolithic effort. We know that design processes are better when they’re cyclical rather than waterfalls. We’ve discovered that our inability to perform rote tasks flawlessly argues for changes in work processes and expectation. And we’ve found out that treating people fairly leads to better outcomes including retention, loyalty, and more. Ultimately, we’ll want to be making smart cyborg choices about what to have people do and what technology should do.

In short, we can be working smarter in many ways. It’s hard to change from the old hierarchical models, but we’re continually learning that other approaches work better. Heck, we may even be able to start working wiser! Here’s hoping.

A Cognitive Audit?

11 January 2017 by Clark 2 Comments

In the recent Chief Learning Officer magazine, I wrote an article on the basics of the cognitive science of learning. Given the evidence that “L&D isn’t doing near what it could and should, and what it is doing it is doing badly, other than that it’s fine” (as I say), at least one of the potential barriers is that L&D isn’t truly aware of what science says about their profession.

And I truly believe that if you’re a professional, you should be aware of the fundamental scientific basis of your profession. Pilots need to know aeronautics, physicians need to know physiology, etc. And therefore, I reckon L&D needs to know the cognitive background. But there’s more.

Knowing a suitable level of cognitive science is one thing, using that to assess your practices is another. Too often, we have what we call ‘inert knowledge’: we know it, but we don’t apply it. That’s not helpful. What has to happen is that processes need to be evaluated, improvements identified, interventions prioritized, enablement enacted, and progress reviewed. It’s just part of being a professional!

There are other sorts of audits possible (I know folks who do performance audits, and knowledge audits, etc), but I’m increasingly thinking that the one that matters is the one that aligns with how our brains work. Not at the neural level (there’s little of impact there), but at the cognitive level. Note that cognitive science includes social, conative and affective components (e.g. the culture and motivation), and neural, for that matter ;).

This isn’t an academic exercise. The increasing competition enabled by technology already suggests that optimal execution is only the cost of entry, and continual innovation will be the only sustainable differentiator. Both are cognitive functions, and the best outcomes will only be achieved when organizations are acting in accordance with how we think, work, and learn. This is about equipping your organization to kick some proverbial tail.

I’m drafting an initial such instrument, with associated recommendations. I welcome your thoughts, and any interest in engaging around this.

Vale Seymour Papert

10 January 2017 by Clark 1 Comment

Imagine my surprise that I missed the demise of Seymour Papert this past year (yet another loss). I’ve looked back to see what I was doing on 31 July and how I missed it, and we were preparing for a week in the wilderness. So it’s certainly likely I wasn’t deeply involved in the news. This is a shame, because I’ve been a fan of Papert’s work for quite literally decades. So here’s a belated tribute.

My first job out of college was designing and programming educational computer games. I’d been exposed to some innovative thinking through my undergraduate thesis advisors, Hugh Mehan and Jim Levin. Having read Papert’s Mindstorms, I gave it to my parents to help them understand why I did what I did (unsuccessfully ;). The book is subtitled “Children, Computers, and Powerful Ideas”, and argued that learning computing was a vehicle for learning to think.

Papert had studied with Jean Piaget, and proceeded to be a leader of the constructivism movement applying the notion of exploratory learning environments. I subsequently learned about Piaget (and post-Piaget, and Vygotsky) in my graduate studies, so I can see how the notion of developmental readiness and opportunities to create understanding through exploration could lead to the work Papert did.

Logo, the computer language for learning, was developed by Papert along with Wallace Feurzieg. It’s simple commands controlling a ‘turtle’ and gradually getting richer play challenges was the start to computer understanding for learners for decades, and has influenced computer language learning in many ways. Apple’s Playgrounds uses similar small steps to control a creature to start teaching Swift.

He was invited to co-lead the MIT AI lab with Marvin Minsky. He worked with Minsky on Perceptrons, which were an early exploration of the connectionist networks now so prevalent in artificial intelligence. There remains a controversy over whether and how the book influenced research in the area of symbolic and sub-symbolic intelligence approaches.

Papert was instrumental in much of the thinking that has shaped what we do in learning technology. I’m grateful for his contributions.

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