Learning Science

One of the areas we actively investigate, and apply, is learning science. Here we’re talking about the empirical evidence that suggests what the impacts of various interventions are. If we’re designing learning, shouldn’t we be professional about it? The results generally give us very good guidance, if we’re actually trying to have impact. Of course, they’re not directly applicable, so we’ll need some inference. Still, better to begin with the evidence than from folk superstitions.

There are way too many of the latter, by the way. I was asked to write a book in 2018 addressing the persistent training myths hindering our industry. Yet, 5 years after Millennials, Goldfish & Other Training Misconceptions, we still see evidence of myths like learning styles and generations. While I do see greater awareness of learning science and debunking of myths, it’s still far too little.

What people ideally would do is to start from an indicated problem, and identify the root cause. Then, they can design a remedy for that situation. When it’s about learning, the science tells us to focus on the performance objective, align meaningful practice, guide decisions with predictive models, and illustrate those models in context via examples. Also, address the emotional aspects.

Of course, it’s unlikely that learning science will have specifically addressed your context, so you’ll need a way to test your solution. We don’t do enough of this, but if you look at more recent design practices, whether Map It, LLAMA, SAM, or Pebble in a Pond, you’ll see prototyping and testing. That’s a necessity if we’re going to move beyond the unjustified “if we build it, it is good”. We want to be able to demonstrate that, not take it as a matter of faith!

Technology

We similarly need to take guidance about the use of technology. There are principles out there for existing technology, but what about new technology? How do we ascertain what the meaningful uses of new technologies will be? We need to apply a useful way to judge a priori when a technology may make sense. And then we will still want to prototype and test.

The best way to evaluate new technologies, before testing, is to think about their core affordances, that is, what they inherently support. In particular, their cognitive affordances, what they do that you can’t do in other ways. With an affordance perspective, you can see that Second Life was about social and immersive 3D (at any scale). It had enough cognitive and technological overhead that if you didn’t inherently need those two things, there were better ways. Ultimately, what persisted as value largely revolved around those capabilities.

That holds true today for new technologies. For instance, virtual reality (VR) and augmented reality (AR) have been an eager expenditure for organizations. Yet, where do they make inherent sense? Analyzing the benefits along a continuum from reality, through AR, then augmented virtuality, before VR, provides a useful framing. We can see when augmenting our existing world makes sense, versus using the existing world to supplement virtual goals, and then creating a fully immersive alternate world. We can then evaluate the benefits against the costs, considering alternatives, as most things have lower-tech equivalents.

One of the things we need to do is stop with the ‘shiny object’ syndrome. All too often, when a new technology comes along, we find people throwing their previous approaches into it. Remember going to an event in Second Life, only to be subjected to a slide show? We similarly are seeing the same thing now with VR. While we do want to be testing, we also need to apply some principled stances. Using technology in ways that align with how we think, work, and learn is going to be a positive investment; the alternative is money frittered away.

Artificial intelligence, in particular Generative AI, has become the latest ‘shiny object’. The hype ranges from being a ‘game-changer’ to the new era of intelligent computers. The reality is that these platforms have impressive new capabilities, but also with some known, and likely yet unknown, problems. (They definitely are not truly intelligent, however, as they don’t actually ‘know’ anything! Inside, it’s all symbol manipulation in ways that don’t have a linkage to the real world, unlike our intelligence.) Again, by understanding what they do (probabilistically create plausible prose from an unvetted database), you can know when they make sense, and what sorts of guardrails to use.

Which isn’t to say you should avoid these technologies. In the right place and time, they can be effective tools. You want to do some initial analysis, collecting data, and then you do want to experiment. However, do so at low cost, with low stakes, at first. When you can state with confidence when they add value, you’re ready to then take advantage of the opportunities in a meaningful way.

Organizational Approaches

Which raises the bigger issue: how does L&D in your organization operate? The above suggests a balance between exploration and application (with a ratio perhaps of 1/5). Innovation, in general, is an increasingly important aspect of organizational endeavor, and L&D isn’t immune. Instead, it should be mastered in L&D before being taken elsewhere. What you’re doing is taking ownership of a critical skill that facilitates moving from survival to ongoing success. It’s about thinking in terms of performance improvement, not just ‘learning’. That switch, from focusing on learning to achieve actual impacts, is a critical overview of the trends.

Focusing on performance isn’t new, but it is important. What it enables is to start looking for alternatives to learning. Learning science tells us that actually developing a persistent new ability to do differently is actually quite difficult, compared to other interventions. Which we’d know if we actually were evaluating the impact of our endeavors. We need to look at what the most efficient and effective solution to measurable organizational problems is, not just what it costs us to deliver an hour of training.

Once we start measuring, we need to do so effectively. While asking what people thought of a course may be a starting point, it’s not the end point by any means. Ideally, we work backwards from what needs to change in the org, to figuring out what would be done different, then finally to how to make that happen. It might be a job aid, or an organizational change, not just a course.

Associated with this is becoming more innovative, broadening the toolset to bring to bear upon the problems, and the approaches to solution. Ultimately, being effective in problem-solving will be critical, but L&D shouldn’t go out proselytizing before owning the nuances. Thus, becoming innovative in L&D is the first step to fostering innovation organization-wide.

Once such innovations yield new insights, they should be shared. This needs to happen within communities of practice, within the organization, and with the field as a whole. The language may change, and certain details may need to be changed or removed, but ultimately we all do better when we all improve our game.

Summarizing

What, then, are the takeaways? Here are the trends we’d like to see:

• More performance focus: thinking beyond ‘the course’
• More learning science: in solutions
• More cognitive science: in designing non-learning solutions, and in our processes
• More experimentation: a hallmark of innovation
• More evaluation: so we know how we’re doing

What we’d like to see less of:

• Pushing unsupported notions
• Cargo cult mentality: chasing shiny objects
• Order-taking for courses

That’s our hope for 2024. We don’t expect all organizations to make all these changes at once, but we would like to see increasing moves in these directions. We expect to see new technological announcements and new research results. What we also expect are over-enthusiastic claims, shiny new brandings and packaging, and over-produced and under-designed solutions. We’re hoping to see less of the latter, however. That’s the trends 2024 needs, we think. Now, what do you think?

The post 2024 Learning and Development Trends We’d Like to See first appeared on The Upside Learning Blog.

One of the myths was learning preferences. The claim was something like that with personalization we could support people’s preferences for learning. This is, really, the learning styles myth. There’s no evidence that adapting to learners’ preferred or identified styles makes a difference. Learner intuitions about what works is not well correlated with outcomes.. So this wasn’t a sensible statement.

There were several comments on unlearning. There is some controversy on this, some people saying that it’s necessary for organizations if not individuals. I still think it’s a misconception, at least. That is, your learning doesn’t go away and something replaces it, you have to actively practice the new behavior in response to the same context to learn a new way of doing things. It’s people, after all, and that’s how our cognitive architecture works!

Gamification also got a mention. Again, this is more misconception perhaps. That is, it matters how you define it. We had Karl Kapp on the LDA’s You Oughta Know session, talking about gamification (and micro learning). He talks about understanding that it’s more than just points and leaderboards. Yes, it is. However, that term leads people quickly to that mindset, hence my resistance to the term. However, the chat seemed to suggest that gamification, in combination with something else (memory fails), was a panacea. There are no panaceas, and gamification isn’t a part of any major advance. It’s a ‘tuning’ tool, at best.

A final one was really about tech excitement; with all the new tools, we’ll usher in a new era of productivity. Well, no. The transformation really is not digital. That is, if we use tech to augment our existing approaches, we’re liable to be stuck in the same old approaches. Most of which are predicated on broken models of human behavior. The transformation should be humane, reflecting how we really think, work, and learn. Without that, digitization isn’t going to accomplish as much as it could.

So, there’s significant myth persistence. I realize change can be hard and take time. Sometimes that’s frustrating, but we have to be similarly persistent in busting them. I’ll keep doing my part. How about you?

This blog was originally published on Learnlets.

The post Myth Persistence first appeared on The Upside Learning Blog.

The main reason to have a design process is to have a manageable and predictable process that we can use to estimate timelines and costs. When we design, we typically need to have a specified budget and schedule, at least once the analysis is done (and a separate budget and schedule for the analysis too, then). A second, and equally important reason, is to overcome our flaws as designers. Our cognitive architecture, as amazing as it is, inherently contains some systemic limitations. (No one architecture can do it all!) Thus, our design process needs to minimize those problems and provide remedies.

The flaws in our architecture include phenomena such as set effects, functional fixedness, premature evaluation, and more. ‘Set effects’ means we tend to solve new problems in ways that we solved previous problems, even if a different way would be better. ‘Set effects’ is where we tend to limit our use of tools to one approach, even if the tools can be used in other ways. We also tend to prematurely converge on some solutions without fully exploring all of them, despite the benefits of diverging in our search before converging on an approach.

There are other limitations to how our minds work, too: our working memory is small, we have some randomness in our actions, we typically have trouble remembering large amounts of arbitrary information, and so on. We have developed tools and practices to get around these limitations. For instance, we can use templates for parts of our information gathering and design, to avoid overloading our cognitive capacity. We also use checklists to help us not skip steps. Further, we use external representations such as spreadsheets to overcome our inability to remember large quantities of information.

We also have built into our practices things like prototyping and early evaluation, (proper) brainstorming, and more. Here, we recognize that those we design for are also human, and we can’t completely anticipate the ways in which they can interpret our ideas. Thus, we need to test and tune them. Yet we also want systematicity. We do analysis before design, before development.

We’ve got nuances within all of this. As designers, we should follow the best principles, using the right tools. My hope, here, is to lay out some of those practices with the cognitive rationale that underpins them. Learning science, like design, is based on how our brain works. Understanding that leads to not only better designs but better design processes. Which, after all, is what we all should want.

The post Design Process for Deeper Learning: An Overview first appeared on The Upside Learning Blog.

My presentation focused on the evidence that we’re still operating under the assumption that we’re logical reasoners (which I pointed out, isn’t apt). I mentioned annual reviews, bullet points presos, unilateral decisions, and more. I also cited evidence that L&D isn’t doing well, so it is a worry. Pointing to post-cognitive frameworks like predictive coding, situated & distributed cognition, and more, I argued that we need to update our practices. I closed by urging two major disruptions: measurement, and implementing a learning culture in L&D before taking it out to the broader org.

In a subsequent presentation, however, the presenter (from a sponsoring org) was touting how leadership needed to accommodate millennials. I’m sorry, but there’s considerable evidence that ‘generation differences’ are a myth. The boundaries are arbitrary, there’re no significant differences in workplace values, and every effect is attributable to age and experience, not generation. (Wish I could find a link to the ‘eulogy for millennials myth’ two academics wrote.)

Another talk presented a lot of data, but ultimately seemed to be about supporting user preferences. Sorry, but user preferences, particularly for novices, aren’t a good guide. There was also a pitch for an ‘all-singing, all-dancing’ solution. Which could be appealing, if you’re willing to live with the tradeoffs. For instance, locking into whatever features your provider is willing to develop, and living without best-0f-breed for all components.

Yes, it’s marketing hype. However, marketing hype should be based on reality, not myths. I can get promising a bit more than you can deliver, and focusing on features you’re strong on. I can’t see telling people things that aren’t true. My first step in dealing with the post-cognitive brain is to know the cognitive and learning sciences, so you’ll know what’s plausible and what’s not. Not to PhD depth, but to have a working knowledge. That’s the jumping off point to much that’s the necessary disruption, revolution, that L&D needs to have. And fewer myths, please!

This blog was originally published on Learnlets.

The post Fewer myths, please first appeared on The Upside Learning Blog.