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Biz tech

22 September 2015 by Clark 2 Comments

One of my arguments for the L&D revolution is the role that L&D could be playing. I believe that if L&D were truly enabling optimal execution as well as facilitating continual innovation (read: learning), then they’d be as critical to the organization as IT. And that made me think about how this role would differ.

To be sure, IT is critical. In today’s business, we track our business, do our modeling, run operations, and more with IT. There is plenty of vertical-specific software, from product design to transaction tracking, and of course more general business software such as document generation, financials, etc. So how does L&D be as ubiquitous as other software? Several ways.

First, formal learning software is really enterprise-wide. Whether it’s simulations/scenarios/serious games, spaced learning delivered via mobile, or user-generated content (note: I’m deliberately avoiding the LMS and courses ;), these things should play a role in preparing the audience to optimally execute and being accessed by a large proportion of the audience. And that’s not including our tools to develop same.

Similarly, our performance support solutions – portals housing job aids and context-sensitive support – should be broadly distributed. Yes, IT may own the portals, but in most cases they are not to be trusted to do a user- and usage-centered solution. L&D should be involved in ensuring that the solutions both articulate with and reflect the formal learning, and are organized by user need not business silo.

And of course the social network software – profiles and locators as well as communication and collaboration tools – should be under the purview of L&D. Again, IT may own them or maintain them, but the facilitation of their use, the understanding of the different roles and ensuring they’re being used efficiently, is a role for L&D.

My point here is that there is an enterprise-wide category of software, supporting learning in the big sense (including problem-solving, research, design, innovation), that should be under the oversight of L&D. And this is the way in which L&D becomes more critical to the enterprise. That it’s not just about taking people away from work and doing things to them before sending them back, but facilitating productive engagement and interaction throughout the workflow. At least at the places where they’re stepping outside of the known solutions, and that is increasingly going to be the case.

Agile?

17 September 2015 by Clark 6 Comments

Last Friday’s #GuildChat was on Agile Development. The topic is interesting to me, because like with Design Thinking, it seems like well-known practices with a new branding. So as I did then, I’ll lay out what I see and hope others will enlighten me.

As context, during grad school I was in a research group focused on user-centered system design, which included design, processes, and more. I subsequently taught interface design (aka Human Computer Interaction or HCI) for a number of years (while continuing to research learning technology), and made a practice of advocating the best practices from HCI to the ed tech community. What was current at the time were iterative, situated, collaborative, and participatory design processes, so I was pretty familiar with the principles and a fan. That is, really understand the context, design and test frequently, working in teams with your customers.

Fast forward a couple of decades, and the Agile Manifesto puts a stake in the ground for software engineering. And we see a focus on releasable code, but again with principles of iteration and testing, team work, and tight customer involvement. Michael Allen was enthused enough to use it as a spark that led to the Serious eLearning Manifesto.

That inspiration has clearly (and finally) now moved to learning design. Whether it’s Allen’s SAM or Ger Driesen’s Agile Learning Manifesto, we’re seeing a call for rethinking the old waterfall model of design. And this is a good thing (only decades late ;). Certainly we know that working together is better than working alone (if you manage the process right ;), so the collaboration part is a win.

And we certainly need change. The existing approaches we too often see involve a designer being given some documents, access to a SME (if lucky), and told to create a course on X. Sure, there’re tools and templates, but they are focused on making particular interactions easier, not on ensuring better learning design. And the person works alone and does the design and development in one pass. There are likely to be review checkpoints, but there’s little testing. There are variations on this, including perhaps an initial collaboration meeting, some SME review, or a storyboard before development commences, but too often it’s largely an independent one way flow, and this isn’t good.

The underlying issue is that waterfall models, where you specify the requirements in advance and then design, develop, and implement just don’t work. The problem is that the human brain is pretty much the most complex thing in existence, and when we determine a priori what will work, we don’t take into account the fact that like Heisenberg what we implement will change the system. Iterative development and testing allows the specs to change after initial experience. Several issues arise with this, however.

For one, there’s a question about what is the right size and scope of a deliverable. Learning experiences, while typically overwritten, do have some stricture that keeps them from having intermediately useful results. I was curious about what made sense, though to me it seemed that you could develop your final practice first as a deliverable, and then fill in with the required earlier practice, and content resources, and this seemed similar to what was offered up during the chat to my question.

The other one is scoping and budgeting the process. I often ask, when talking about game design, how to know when to stop iterating. The usual (and wrong answer) is when you run out of time or money. The right answer would be when you’ve hit your metrics, the ones you should set before you begin that determine the parameters of a solution (and they can be consciously reconsidered as part of the process). The typical answer, particularly for those concerned with controlling costs, is something like a heuristic choice of 3 iterations. Drawing on some other work in software process, I’d recommend creating estimates, but then reviewing them after. In the software case, people got much better at estimates, and that could be a valuable extension. But it shouldn’t be any more difficult to estimate, certainly with some experience, than existing methods.

Ok, so I may be a bit jaded about new brandings on what should already be good practice, but I think anything that helps us focus on developing in ways that lead to quality outcomes is a good thing. I encourage you to work more collaboratively, develop and test more iteratively, and work on discrete chunks. Your stakeholders should be glad you did.

ALIGN!

15 September 2015 by Clark 7 Comments

I’m recognizing that there’s an opportunity to provide more support for implementing the Revolution. So I’ve been thinking through what sort of process might be a way to go about making progress. Given that the core focus in on aligning with how we think, work, and learn (elements we’re largely missing), I thought I’d see whether that could provide a framework. Here’s my first stab, for your consideration:

Assess: here we determine our situation. I’m working on an evaluation instrument that covers the areas and serves as a guide to any gaps between current status and possible futures, but the key element is to ascertain where we are.

Learn: this step is about reviewing the conceptual frameworks available, e.g. our understandings of how we think, work and learn. The goal is to identify possible directions to move in detail and to prioritize them. The ultimate outcome is our next step to take, though we may well have a sequence queued up.

Initiate: after choosing a step, here’s where we launch it. This may not be a major initiative. The principle of ‘trojan mice‘ suggests small focused steps, and there are reasons to think small steps make sense. We’ll need to follow the elements of successful change, with planning, communicating, supporting, rewarding, etc.

Guide: then we need to assess how we’re doing and look for interventions needed. This involves knowing what the change should accomplish, evaluating to see if it’s occurring, and implementing refinements as we go. We shouldn’t assume it will go well, but instead check and support.

Nurture: once we’ve achieved a stable state, we want to nurture it on an ongoing basis. This may be documenting and celebrating the outcome, replicating elsewhere, ensuring persistence and continuity, and returning to see where we are now and where we should go next.

Obviously, I’m pushing the ALIGN acronym (as one does), as it helps reinforce the message. Now to put in place tools to support each step. Feedback solicited!

Culture Before Strategy

9 September 2015 by Clark Leave a Comment

In an insightful article, Ken Majer (full disclosure, a boss of mine many years ago) has written about the need to have the right culture before executing strategy. And this strikes me as a valuable contribution to thinking about effective change in the transformation of L&D in the Revolution.

I have argued that you can get some benefits from the Revolution without having an optimized culture, but you’re not going to tap into the full potential. Revising formal learning to be truly effective by aligning to how we learn, adding in performance support in ways that augment our cognitive limitations, etc, are all going to offer useful outcomes. I think the optimal execution stuff will benefit, but the ability to truly tap into the network for the continual innovation requires making it safe and meaningful to share. If it’s not safe to Show Your Work, you can’t capitalize on the benefits.

What Ken is talking about here is ensuring you have values and culture in alignment with the vision and mission. And I’ll go further and say that in the long term, those values have to be about valuing people and the culture has to be about working and learning together effectively. I think that’s the ultimate goal when you really want to succeed: we know that people perform best when given meaningful work and are empowered to pursue it.

It’s not easy, for sure. You need to get explicit about your values and how those manifest in how you work. You’ll likely find that some of the implicit values are a barrier, and they’ll require conscious work to address. The change in approach on the part of management and executives and the organizational restructuring that can accompany this new way of working isn’t going to happen overnight, and change is hard. But it is increasingly, and will be, a business necessity.

So too for the move to a new L&D. You can start working in these ways within your organization, and grow it. And you should. It’s part of the path, the roadmap, to the revolution. I’m working on more bits of it, trying to pull it together more concretely, but it’s clear to me that one thread (and as already indicated in the diagrams that accompany the book) is indeed a path to a more enabling culture. In the long term, it will be uplifting, and it’s worth getting started on now.

Accreditation and Compliance Craziness

8 September 2015 by Clark 3 Comments

A continued bane of my existence is the ongoing requirements that are put in place for a variety of things. Two in particular are related and worth noting: accreditation and compliance. The way they’re typically construed is barking mad, and we can (and need to) do better.

To start with accreditation. It sounds like a good thing: to make sure that someone issuing some sort of certification has in place the proper procedures. And, done rightly, it would be. However, what we currently see is that, basically, the body says you have to take what the Subject Matter Expert (SME) says as the gospel. And this is problematic.

The root of the problem is that SMEs don’t have access to around 70% of what they do, as research at the University of Southern California’s Cognitive Technology group has documented. However, of course, they have access to all they ‘know’. So it’s easy for them to say what learners should know, but not what learners actually should be able to do. And some experts are better than others at articulating this, but the process is opaque to this nuance.

So unless the certification process is willing to allow the issuing institution the flexibility to use a process to drill down into the actual ‘do’, you’re going to get knowledge-focused courses that don’t actually achieve important outcomes. You could do things like incorporating those who depend on the practitioners, and/or using a replicable and grounded process with SMEs that helps them work out what the core objectives need to be; meaningful ones, ala competencies. And a shoutout to Western Governors University for somehow being accredited using competencies!

Compliance is, arguably, worse. Somehow, the amount of time you spend is the important determining factor. Not what you can do at the end, but instead that you’ve done something for an hour. The notion that amount of time spent relates to ability at this level of granularity is outright maniacal. Time would matter, differently for different folks, but you have to be doing the right thing, and there’s no stricture for that. Instead, if you’ve been subjected to an hour of information, that somehow is going to change your behavior. As if.

Again, competencies would make sense. Determine what you need them to be able to do, and then assess that. If it takes them 30 minutes, that’s OK. If it takes them 5 hours, well, it’s necessary to be compliant.

I’d like to be wrong, but I’ve seen personal instances of both of these, working with clients. I’d really like to find a point of leverage to address this. How can we start having processes that obtain necessary skills, and then use those to determine ability, not time or arbitrary authority! Where can we start to make this necessary change?

Aligning

25 August 2015 by Clark 2 Comments

I’m realizing that a major theme of my work and the revolution is that what we do in organizations, and what we do as L&D practitioners, is not aligned with how we think, work, and learn. And to that extent, we’re doomed to failure. We can, and need to, do better.

Let’s start with thinking. The major mismatch here is that our thinking is done rationally and in our head. Results in cognitive science show, instead, that much of our thinking is irrational and is distributed across the world. We use external representations and tools, and unless we’re experts, we make decisions and use our brains to justify them rather than actually do the hard work.

What does this mean for organizations and L&D? It means we should be looking to augment how we think, with tools and processes like performance support, helping us find information with powerful search. We want to have open book learning, since we’ll use the book in the real world, and we want to avoid putting it ‘in the head’ as much as possible. Particularly rote information. We should expect errors, and provide support with checklists, not naively expect that people can perform like robots.

This carries over to how we work. The old view is that we work alone, performing our task, and being managed from above with one person thinking for a number of folks. What we now know, however, is that this view isn’t optimal. The output is better when we get multiple complementary minds working together. Adaptation and innovation work best when we work together.

So we don’t need isolation to do our work, we need cooperation and collaboration. We need ways to work together. We need to give people meaningful tasks and give them space to execute, with appropriate support. We need to create environments where it’s safe to share, to show your work, to work out loud.

And our models of learning are broken. The trend to an event comprised of information dump and knowledge test we know doesn’t work. Rote procedures are no longer sufficient for the increasing ambiguity and unique situations our learners are seeing. And the notion that “practice ’til they get it right” will lead to any meaningful change in ability is fundamentally flawed.

To learn, we need models to guide our behavior and help us adapt. We need to identify and address misconceptions. We need learners to engage concretely and be scaffolded in reflection. And we need much practice. Our learning experiences need to look much more like scenarios and serious games, not like text and next.

We’re in an information age, and industrial models just won’t cut it. I’m finding that we’re hampered by a fundamental lack of awareness of our brains, and this is manifesting in too many unfortunate and ineffective practices. We need to get better. We know better paths, and we need to trod them. Let’s start acting like professionals and develop the expertise we need to do the job we must do.

#itashare

Designing Learning Like Professionals

12 August 2015 by Clark 4 Comments

I’m increasingly realizing that the ways we design and develop content are part of the reason why we’re not getting the respect we deserve. Our brains are arguably the most complex things in the known universe, yet we don’t treat our discipline as the science it is. We need to start combining experience design with learning engineering to really start delivering solutions.

To truly design learning, we need to understand learning science. And this does not mean paying attention to so-called ‘brain science’. There is legitimate brain science (c.f. Medina, Willingham), and then there’s a lot of smoke.

For instance, there’re sound cognitive reasons why information dump and knowledge test won’t lead to learning. Information that’s not applied doesn’t stick, and application that’s not sufficient doesn’t stick. And it won’t transfer well if you don’t have appropriate contexts across examples and practice. The list goes on.

What it takes is understanding our brains: the different components, the processes, how learning proceeds, and what interferes. And we need to look at the right levels; lots of neuroscience is not relevant at the higher level where our thinking happens. And much about that is still under debate (just google ‘consciousness‘ :).

What we do have are robust theories about learning that pretty comprehensively integrate the empirical data. More importantly, we have lots of ‘take home’ lessons about what does, and doesn’t work. But just following a template isn’t sufficient. There are gaps where have to use our best inferences based upon models to fill in.

The point I’m trying to make is that we have to stop treating designing learning as something anyone can do. The notion that we can have tools that make it so anyone can design learning has to be squelched. We need to go back to taking pride in our work, and designing learning that matches how our brains work. Otherwise, we are guilty of malpractice. So please, please, start designing in coherence with what we know about how people learn.

If you’re interested in learning more, I’ll be running a learning science for design workshop at DevLearn, and would love to see you there.

Content engineering

11 August 2015 by Clark 2 Comments

We’ve heard about learning engineering and while the focus is on experience design, the pragmatics include designing content to create the context, resources, and motivation for the activity. And it’s time we step beyond just hardwiring this content together, and start treating it as professionals.

Look at business websites these days. You can customize the content you’re searching for with filters. The content reacts to the device you’re on and displays appropriately. There can even be content that is specific to your particular trace of action through the site and previous visits. Just look at Amazon or Netflix recommendations!

This doesn’t happen by hardwired sites anymore. If you look at the conferences around content, you’ll find that they’re talking industrial strength solutions. They use content management systems, carefully articulated with tight definitions and associated tags, and rules that pull together those content elements by definition into the resulting site. This is content engineering, and it’s a direction we need to go.

What’s involved is tighter templates around content roles, metadata describing the content, and management of the content. You write into the system, describe it, and pull it out by description, not by hard link. This allows flexibility and rules that can pull differentially by different contexts: different people, different role, different need, and different device. We also separate out what it says from how it looks, using tags to support rendering appropriately on different devices rather than hard-coding the appearance as well as the content and the assembly.

This is additional work, but the reasons are several. First, being tighter around content definitions provides a greater opportunity to be scientific about the role the content plays. We’re too lax in our content, so that beyond a good objective, we don’t specify what makes a good example, etc. Second, by using a system to maintain that content, we can get more rigorous in content management. I regularly ask audiences whether they have outdated legacy content hanging around, and pretty much everyone agrees. This isn’t effective content governance, and content should have regular cycles of review and expiry dates.

By this tighter process, we not only provide better content design, delivery, and management, but we set the stage for the future. Personalization and customization, contextualization, are hampered when you have to hand-configure every option you will support. It’s much easier to write a new set of rules and then your content can serve new purposes, new business models, and more.

If you want to know more about this, I hope to see you at my session on content at DevLearn!

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