Archives for December 2020

Someone on LinkedIn asked about a way to evaluate orgs on their learning infrastructure. And I had developed a Performance Ecosystem Maturity Model as part of Revolutionize Learning & Development, but…I hadn’t presented it. At least not in full.  Here I rectify that; my holiday gift to you!

(A bit over 5 mins.)  And, of course, if you want help with it, let me know! Also, the prose (and the diagram):

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Script

Hi, I‘m Clark Quinn, of Quinnovation. Recently, someone asked about an organizational learning maturity model. And, coincidentally, I had one. However, it was locked away in my book from 2018. So I want to present that model, and hope it‘s of use.  

In my book, Revolutionize Learning & Development, I pushed for the performance ecosystem, going beyond ‘the course‘ to talk about all the ways that L&D that could assist organizational learning. I posit that optimal execution is only the cost of entry, and the only sustainable differentiator is continual innovation. And I argued for what that meant. I want organizations to have a concrete picture of what this looked like.

In the book I had a set of six categories, each with two components. So I created a ‘maturity model‘. The intent is to provide a tool whereby organizations can understand what the possibilities are, assess themselves, and prioritize directions to move and improve. Let‘s be clear, this is conceptual, not empirical. This is a proposal about what should be the elements, and an effort to consider what fits in each stage of each component.  

The first area is culture. How we‘re learning, and what our orientation is. And you‘ll see as we fill out the table, what this means. Starting with learning, we transition from a competitive environment, (which is unhealthy), through being willing to cooperate (say for team projects), to where we‘re collaborating of our own initiative, ending up where we‘re working to also improve our processes as well as our outcomes. And our orientation shifts from learning for our own benefit, to learning as an organization, then to where we consider the impacts on society, ending up where we‘re looking to ensure this works across those elements.

Then we look at the formal learning elements, how we design and who‘s using the learning. We start with the traditional knowledge dump, then on making it more engaging and lean, to where we‘re focusing on making it meaningful to the org and the individual, and finally creating transformative experiences. And we look at who‘s using the learning. It starts with taking orders for courses, through having some times where the effort is to truly address a need, to where our efforts are aligned with key organizational needs, and finally where we‘re being discerning about methods, measures, and outcomes.

We then consider going beyond the course to times when knowledge can be in the world, not in the head. We worry about resources and the ways people can get access to them. Instead of idiosyncratic aids, we move to having L&D creating support, and then recognizing the value of curation, and finally with a mixed initiative with everyone contributing. And we move from the siloed access to these resources, through an intermediate stage of some focused development, to a user-centric access based upon the users needs (not the originators location), ending at a dynamic matching of need to resource.  

We also start looking at informal learning, the continual innovation. This includes both the status of networks and the usage of those networks. It starts where if there‘s network usage, it‘s not org-originated, through some units having a system, to having an organizational focus on communication, and then actively facilitating social learning. And we move from the Miranda organization (where anything you say can and will be used against you), to where some folks are experimenting, to where people are comfortable contributing, and finally where folks recognize that active engagement is key to organizational thrival.

We also consider the use of metrics in the organization, both to evaluate and to improve. We should move from just looking at the cost of a butt in a seat for an hour to whether that time is benefiting the org. Then we incorporate informal learning, and finally where measurement‘s naturally part of our practices. We move our focus from internal to a broader picture, and start evaluating our processes themselves, and move to looking at these as a synergistic whole  

Finally, we look at our infrastructure, our underlying approach and our systematicity. We move from handcrafted solutions to systemic approaches, then looking to incorporate semantics, and finally looking at systems with emergent capabilities. And we move to look at our approaches as platforms, and then integrate those platforms, and finally allow our systems to adapt as conditions change.  

This is an updated version of the model in the book, and I think provides a useful framework for thinking about L&D. I hope this made sense, and is of interest. And I welcome feedback! Thanks for listening. You can find out more at Quinnovation.com, and at revolutionizelnd.com.  

As frequently happens, I get asked for my predictions. And, of course, I have reservations. Here’s a video that provides the qualifications, and five trends for 2021 that I’d expect, or like, to see.

And the script:

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Hi, I‘m Clark Quinn, of Quinnovation (a boutique learning experience design strategy consultancy). I was recently asked about what trends I thought would be seen next year.  

Two relevant quotes to set the stage. For one, Alan Kay famously said “the best way to predict the future is to invent it.” So I tend to talk about trends we should see. The other is “never predict anything, particularly the future.” I heard an expert talk about having looked at predictions and outcomes, and the noticeable trend is that it went as expected, with one unforeseen twist. So, expecting I‘ll get it wrong, here are some trends I‘m either expecting or keen to see:

The first trend I‘m seeing and think will continue is an emphasis on learning science. And that‘s all to the good! Admittedly, I‘m part of this, what with running a course on learning science and having a forthcoming book on the topic. But I‘m seeing more and more people talking about it, and not all hype and even mostly right! There are more books, the Learning Guild‘s regular research reports are good, the launch of an event past summer and an associated new society focused on evidence-based learning (the Learning Development Accelerator) are all signs of growing momentum.

Second, when there‘s a lot of hype about something, it tends to be followed by a backlash.  This may be farther out than 2021, but with all the buzz about AI, I think we might see some more awareness of limitations. Yes, it can do some very useful things, but it also isn‘t a panacea. We‘re seeing a growing awareness of the problems with bias in data sets, the limitations of ungrounded knowledge, and concerns about the human costs.  

Three. On a related note, then, I expect more emphasis on the importance of meaningful practice. This comes from learning science, but also the focus on engagement. Thus, the push for Short Sims, and better written multiple choice questions, and in general a focus on ‘do‘, not know.   Hopefully, we‘ll see tool vendors aligning their content and assessment capabilities towards designing scenarios and contextualized practice, along with specific feedback for each wrong answer and support for reflection.

Fourth, I hope for a push towards content systems as well. This, too, may not be in the short term, but ultimately we have to realize that hardwiring experiences may make sense for formal systems, but not for adaptive learning.LXPs are a good move here, even if misnamed (really, they‘re smart portals, not learning experience platforms). Ultimately, we‘ll be better off if we can deliver content by description and rules, like recommendation system, rather than by having to handcraft content to create a ‘one-size fits all‘ solution.  

Finally, I think that our collaboration tools haven‘t lived up to the promise of technology. They‘re very much oriented towards particular modes, instead of supporting really rich interaction. This, too, is more long term, but we really should be able to talk together while working to create representations that capture our evolving thinking. Easily and elegantly! There‘s real opportunity here to engage multiple representations in an elegant suite.  

So there you have it, a wishful list of five trends for 2021. So what do you expect, or hope, to see?

Another video, this time (ok, again ;) about learning science.

They like me to do this to push the course, but I did hear the feedback on LinkedIn that the video format works. Nice to know. As always, also the script.

And, announcing one other thing…

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I‘ve argued in an earlier post for the value of learning science, but I want to go a little deeper. I want to talk a little about the evolution, and a little bit about what‘s involved. It‘s about establishing the foundations of learning science.

And I‘ve mentioned in a previous post  that learning science is interdisciplinary, and relatively new. While education had been proceeding for a long time, the approaches were ad hoc. Experimental science itself didn‘t emerge ‘til the medieval ages at the earliest. My take is that most of school still replicated what had been done since the Prussians invented school, somewhat modeled on religious lectures. The notion of scientific education had yet to emerge.

The first real systematic study of learning came from the field of educational psychology. Here, the focus was on schooling, and included cultural and motivational factors.

A different approach came from behavioral psychology roots. During World War II, the military was faced with training many soldiers, and behavioral psychologists created the field of instructional design. Here, the focus was more on training, including the influence of media and elements of instruction.

Learning science as a field was arguably created when the International Society for the Learning Science was created in the 1990s, perhaps sparked by the creation of the Institute of the Learning Sciences at Northwestern University. This is an integrative approach that looks beyond schools and training to more forms of learning including informal learning and even machine learning.

Having been involved in one way or another with all of these, I tend to create a synthesis. I think the care is cognitive science: how we process information. While there are neural underpinnings, most of the results and prescriptions operate at the cognitive level, or above. Within the information processing cycle we cover core processes like attention, elaboration, and retrieval. This is our core mental architecture.

Interesting results for learning emerge from this architecture, including the role of models and examples, and the core importance of practice. A post-cognitive perspective reflects that our thinking isn‘t formally logical, but instead is emergent, distributed, and social.

Two other important areas are the emotional aspects of learning, and meta-learning. The former is more the conative area of intent to learn, e.g. motivation and anxiety, rather than the affective area of personality. The latter has to do with learning to learn, including looking at our own learning processes.

All these affect the elements that contribute to learning. Our introductions, concepts, examples, practice, and closings should reflect what‘s known about learning. And the components of science and engagement need to be elegantly integrated to yield the best outcomes.

Of course, these foundations of learning science are what I cover in the learning science courses I‘m offering through HR.Com and the Allen Academy. Stay tuned for more ;).