Jessica Kriegel closed DevLearn with a witty and wise presentation taking apart the ‘generations’ and ‘millennial’ myths. In short, it’s basically age discrimination. Don’t do it!
Emerging Online Learning Tools Research Session Mindmap
At AECT18, I dropped in on a session summarizing research on emerging technologies for online learning. There were experts in each area, so names like Vanessa Dennan on social media, Curt Bonk in MOOCs, Florence Martin on synchronous Learning, and David Wiley on Open Education Research. And I apparently missed the nuances in the description, it was more meta-research, e.g. research on the research! There were some interesting insights, but I cheekily pulled more (they were offering chocolates for questions anyway).
So, in addition to all the research on research, David Wiley presented some outcome findings for OERs. Specifically, OERs are at least as good as traditional copyrighted materials. Later he gave a more complex explanation why (e.g. everyone has no reason not to have access), but this is a worthwhile finding.
I pushed further on both social media and synchronous learning for some take-homes (Curt didn’t take the bait ;).
Vanessa had talked about how the different social media platforms people use (most have more than one) create conflicts when being impacted by the learning ones. She recommended being sensitive to this, and don’t assume any specifics. She also recommended only using social media for learner-centered reasons (I’d amend: for learning-centered reasons).
Florence shared that synchronous tools shouldn’t be required in asynchronous learning environments, and only to use for optional activities. Such activities would include Q/A sessions and office hours. She, in conjunction with her student, also talked about the value of doing group work this way.
It was valuable, and I was grateful to them for organizing this session.
Why Engaging Learning?
Someone asked me what I would say about my first book, Engaging Learning. And, coincidentally, my client just gave some copies to their client as part of our engagement, so I guess there’s still value in it! And while I recognize it’s now about 13 years old, I really do believe it has relevance. Since they asked…
I saw the connections between computers and learning as an undergraduate, and designed my own major. My first job out of college was designing and programming educational computer games. Long story short: I went back for a Ph.D. in what was effectively ‘applied cognitive science’, but games continued to play a role in my career. And I reflected on it, and ultimately what started as a research agenda manifested as a model for explaining why games work and how to do it. And then when I started consulting, Pfeiffer asked me to write the book.
To be clear, I believe engagement matters. We learn better when our hearts and our minds are engaged. (That’s the intent of the double meaning of the title, after all.) Learning sticks when we’re motivated and in a ‘safe’ learning situation. Learning can, and should, be ‘hard fun’. However, if we can’t do it reliably and repeatedly, it’s just a dream. I believe that if we systematically apply the principles in the book, we can do it (systematic creativity is not an oxymoron ;).
One of the concerns was that things were changing fast even then (Flash was still very much in play, for example ;). How to write something that wouldn’t be outdated even before it came out? So I tied it to cognitive principles, as our brains aren’t changing that fast. Thus, I think the principles in it still hold. I’ve continued to check and haven’t found anything that undermines the original alignment that underpins designing engaging experiences.
And the book was designed for use. While the first three chapters set the stage, the middle three dig into details. There you’ll find the core framework, examples, and a design process. The design process was focused mostly on adding to what you already do, so as not to be redundant. The final three chapters wrap up pragmatics and future directions.
While ostensibly (and realistically) about designing games, it was really about engagement. For instance, the principles included were applied backwards to branching scenarios, and what I called linear and mini-scenarios. The latter just being better written multiple choice questions!
The book couldn’t cover everything, and I’ve expanded on my thinking since then, but I believe the core is still there: the alignment and the design process in particular. There have been newer books since then by others (I haven’t stayed tied to just games, my mind wanders more broadly ;) and by me, but as with my other books I think the focus on the cognitive principles gives lasting guidance that still seems to be relevant. At a recent event, someone told me that while I viewed mobile as a known, for others it wasn’t. I reckon that may be true for games and engagement as well. If we’re making progress, I’m pleased. So, please, start engaging learning by making engaging learning!
PS, I wrote a Litmos blog post about why engagement matters, as a prelude to a session I’ll be giving at their Litmos Live online event (Nov 7-8) where I talk about how to do it.
Constraints on activities
When we design learning activities (per the activity-based learning model), ideally we’re looking to create an integration of a number of constraints around that assignment. I was looking to enumerate them, and (of course) I tried diagramming it. Thought I’d share the first draft, and I welcome feedback!
The goal is an assignment that includes the right type of processing. This must align with what they need to be able to do after the learning experience. Whether at work or in a subsequent class. Of course, that’s factored into the objective for this learning activity (which is part of an overall sequence of learning).
Another constraint is making sure the setting is a context that helps establish the breadth of transfer. The choice should be sufficiently different from contexts seen in examples and other practices to facilitate abstracting the essential elements. And, of course, it’s ideally in the form of a story that the learner’s actions are contributing to (read: resolve). The right level of exaggeration could play an (unrepresented) role in that story.
We also need the challenge in the activity to be in the right range of difficulty for the learner. This is the integration of flow and learning to create meaningful engagement. And we want to include ways in which learners typically go wrong (read: misconceptions). Learners need to be able to make the mistakes here so we’re trapping and addressing them in the learning situation, not when it could matter.
Finally, we want to make sure there’s enough variation across tasks. While some similarities benefit for both consistency and addressing the objective, variety can maintain interest. We need to strike that balance. Similarly, look at the overall workload: how much are we expecting, and is that appropriate given the other constraints outside this learning goal.
I think you can see that successfully integrating these is non-trivial, and I haven’t even gotten into how to evaluate this, particularly to make it a part of an overall assessment. Yet, we know that multiple constraints help make the design easier (at least until you constrain yourself to an empty solution set ;). This is probably still a mix of art and science, but by being explicit you’re less likely to miss an element.
We want to align activities with the desired outcome, in the full context. So, what am I missing? Does this make sense?
Processing
I’ve been thinking a lot about processing in learning of late; what processing matters, when, and why. I thought I’d share my thinking with you and see what you think. This is my processing! :)
We know processing is useful. You can consider Craik & Lockhart’s Levels of Processing model, or look to the importance of retrieval practice as highlighted in Brown, Roediger, and McDaniel’s Make it Stick. The point is that retrieving information from memory and doing things with it increases the likelihood of learning. One of the questions is “what sort of retrieval (or processing)?”
I’ve always advocated for applying the information, doing something with it. But there are actually a variety of useful things we can do:
- representing information (a form of reflection) whether rewriting, or mindmapping, or…
- connecting to other known information, personal or professional
- considering how it would be applied in practice
- applying it in practice, real or simulated
Of course, we want there to be scrutiny and feedback for the learning to be optimized, etc.
Now, this is in the individual instance, but I’m also looking at the sequence of processing. What would be a series of activities that would develop understanding. So, for instance, for a problem-solving practice like trouble-shooting a process, what might you do? You might have (say, after a model of the process, and examples) a sequence of :
- critique someone else’s performance
- try a simple example of performing
- try a more complex example (perhaps in a group)
- …(more examples of performing)
- try a very complex (read: typical) example
We could throw in related tasks as well either during or as a summary:
- create a checklist to follow
- draw a flow diagram
- create a representation
On a more categorical task, say determining whether a situation qualifies as this or not (with shades of grey in between), we would have a similar structure, but with different types of tasks (again, after initial content such as definition and examples):
- review a case where it clearly is (white)
- review a case where it clearly isn’t (black)
- group review a case of grey (but not too bad)
- group review a case of grey (more shady)
- …
Again, we could have interim or summary tasks:
- summarize the constraints
- document a proposed process
- make a plan for how to do it in the future
- …
What I’ve explicitly added here is when and why to go ‘social‘. There are benefits for the same, but should they all be social? I’ll argue that there’s some initial prep that’s individual, to get everyone on the same page. Since all are different, it helps if this is individual. Then there’s often value in doing it socially, for the reasons in the linked post. Then, I reckon there’s value in doing something independently, to consolidate the learning. And, of course, to determine what capability the individual has acquired.
The point I want to make is that the processing flow, the progression from activity to activity, matters. We want to introduce, diverge, and then converge. We do need to elaborate across contexts to support transfer, and of course increase complexity until they’ve developed the ability to deal with the typical difficulty of cases.
I’m thinking that, too often, we forget the consolidation phase. And we’re often doing processing that’s somewhat like what we need them to do, but ultimately tangential. There are multiple constraints here to be acknowledged, cognitive such as depth and breadth as well as pragmatic such as cost and time, but we want to find the right intersection.
And my practical question is: where does this fall apart? Are their situations where this doesn’t make sense? I realize there are other types of outcomes that I haven’t represented (I’m being indicative, not exhaustive ;), but is this a useful way to think about it?
Labels, models, and drives
In my post last week on engagement, I presented the alignment model from my Engaging Learning book on designing learning experiences. And as I thought about the post, I pondered several related things about labels, models, and drives. I thought I’d wrestle with them ‘out loud’ here, and troll (in the old sense) to see what you think.
Some folks have branded a model and lived on that for their career. And, in a number of cases, that’s not bad: they’re useful models and their applicability hasn’t diminished. And while, for instance, I think that alignment model is as useful as most models I’ve seen, I didn’t see any reason to tie my legacy to it, because the principles I like to comprehend and then apply to create solutions aren’t limited to just engagement. Though I wonder if people would find it easier to put the model in practice if it had a label. The Quinn Engagement model or somesuch?
I’ve also created models around mobile, and about performance ecosystems, and more. I can’t say that they’re all original (e.g. the 4Cs of mobile), though I think they have utility. And some have labels (again, the 4Cs, Least Assistance Principle…) Then the misconceptions book is very useful, but the coverage there isn’t really mine, either. It’s just a useful compendium. I expect to keep creating models. But it’d led to another thought…
I’ve seen people driven to build companies. They just keep doing it, even if they’ve built one and sold it, they’re always on it; they’re serial entrepreneurs. I, for instance, have no desire to do that. There are elements to that that aren’t me. Other folks are driven to do research: they have a knack for designing experiments that tease out the questions that drive them to find answers. And I’ve been good at that, but it’s not what makes my heart beat faster. I do like action research, which is about doing with theory, and reflecting back. (I also like helping others become able to do this.)
What I’m about is understanding and applying cognitive science (in the broad sense) to help people do important things in ways that are enabled by new technologies. Models that explain disparate domains are a hobby. I like finding ways to apply them to solve new problems in ways that are insightful but also pragmatic. If I create models along the way (and I do), that’s a bonus. Maybe I should try to create a model about applying models or somesuch. But really, I like what I do.
The question I had though, is whether anyone’s categorized ‘drives’. Some folks are clearly driven by money, some by physical challenges. Is there a characterization? Not that there needs to be, but the above chain of thought led me to be curious. Is there a typology of drives? And, of course, I’m skeptical if there is one (or more), owing to the problems with, for instance, personality types and learning styles :D. Still, welcome any pointers.
Engagement
In a meeting today, I was asked “how do you define engagement”, and I found it an intriguing question. I don’t know that I have a definition so much as steps to enhance it. Still, it made me think.
What engagement is not, let’s be clear, is tarting content up. It’s not just flashy visuals, stereotypes, and cute prose. Those things add aesthetics (or, done poorly, undermine same), but that’s not where to go.
Instead, I’m looking for an experience that has certain characteristics. One way of looking at it is through the ‘flow’ phenomenon, with cognitive immersion at a level that finds the sweet spot between frustration and boring. Similarly, for learning, it’s the Zone of Proximal Development, between what you can do with one hand tied behind your back, and what you can’t do no matter how much support you get. And it’s both.
You there by exploiting the alignment between the elements of practice and engaging experiences. So just as the above diagram can represent either Czikszentmihalyi or Vygotsky, there’s the alignment I highlighted in Engaging Learning between the elements in greater elaboration. It’s goal, context, challenge, meaningfulness, and more all aligned to create that subjective feeling. And in case you say “you’re extending engagement to learning”, I will note that Koster, in his book A Theory of Fun, explicitly tied what makes games work is that it’s about learning. So, yeah, that’s the type of engagement I’m interested in, regardless.
One of the simple ways I like to characterize it (and it’s not original with me), is ‘hard fun’. I think, if nothing else, that’s a great heuristic. It may be like the famous quote about pornography: “you know it when you see it”. Or maybe you can coin a concise definition. And you can attempt to quantify it through objective criteria like galvanic skin response or adrenalin levels. However, I’m perfectly happy to use subjective criteria. If people say they found it challenging but fun, I’m happy. If they say it’s the best way they can see to learn it, my job is done.
I don’t really yet have a good way to define engagement in a concise specification. Do you have a definition of engagement you like? I’d welcome hearing it!
Another Day Another Myth-Ridden Hype Piece
Some days, it feels like I’m playing whack-a-mole. I got an email blast from an org (need to unsubscribe) that included a link that just reeked of being a myth-ridden piece of hype. So I clicked, and sure enough! And, as part of my commitment to showing my thinking, I’m taking it down. I reckon it’s important to take these myths apart, to show the type of thinking we should avoid if not actively attack. Let me know if you don’t think this is helpful.
The article starts by talking about millennials. That’s a problem right away, as millennials is an arbitrary grouping by birthdate, and therefore is inherently discriminatory. The boundaries are blurry, and most of the differences can be attributed to age, not generation. And that’s a continuum, not a group. As the data shows. Millennials is a myth.
Ok, so they go on to say: “Changing the approach from adapting to Millennials to leveraging Millennials is the key…” Ouch! Maybe it’s just me, but while I like to leverage assets, I think saying that about people seems a bit rude. Look, people are people! You work with them, develop them, etc. Leverage them? That sounds like you’re using them (in the derogatory sense).
They go on to talk about Learning Organizations, which I’m obviously a fan of. And so the ability to continue to learn is important. No argument. But why would that be specific to ‘millennials’? Er…
Here’s another winner: “They natively understand the imperative of change and their clockspeed is already set for the accelerated learning this requires.” This smacks of the ‘digital native’ myth. Young people’s wetware isn’t any different than anyone else’s. They may be more comfortable with the technology, but making assumptions such as this undermines the fact that any one individual may not fit the group mean. And it’s demonstrable that their information skills aren’t any better because of their age.
We move on to 3 ways to leverage millennials:
- Create Cross-pollination through greater teamwork. Yeah, this is a good strategy. FOR EVERYONE. Why attribute it just to millennials? Making diverse teams is just good strategy, period. Including diversity by age? Sure. By generation? Hype. You see this also with the ‘use games for learning’ argument for millennials. No, they’re just better learning designs! (Ok, with the caveat: if done well.)
- Establish a Feedback-Driven Culture to Learn and Grow Together. That’s a fabulous idea; we’re finding that moving to a coaching culture with meaningful assignments and quick feedback (not the quarterly or yearly) is valuable. We can correct course earlier, and people feel more enagaged. Again, for everyone.
- Embrace a Trial-and-Error Approach to Learning to Drive Innovation. Ok, now here I think it’s going off the rails. I’m a fan of experimentation, but trial and error can be smart or random. Only one of those two makes sense. And, to be fair, they do argue for good experimentation in terms of rigor in capturing data and sharing lessons learned. It’s valuable, but again, why is this unique to millennials? It’s just a good practice for innovation.
They let us know there are 3 more ways they’ll share in their next post. You can imagine my anticipation. Hey, we can read two posts with myths, instead of just one. Happy days!
Yes, do the right things (please), but for the right reasons. You could be generous and suggest that they’re using millennials as a stealth tactic to sneak in messages about modern workplace learning. I’m not, as they seem to suggest doing this largely with millennials. This sounds like hype written by a marketing person. And so, while I advocate the policies, I eschew the motivation, and therefore advise you to find better sources for your innovation practices. Let me know if this is helpful (or not ;).
Why Myths Matter
I’ve called out a number of myths (and superstitions, and misconceptions) in my latest tome, and I’m grateful people appear to be interested. I take this as a sign that folks are beginning to really pay attention to things like good learning design. And that’s important. It’s also important not to minimize the problems myths can create. I do that in my presentations, but I want to go a bit deeper. We need to care about why myths matter to limit our mistakes!
It’s easy to think something like “they’re wrong, but surely they’re harmless”. What can a few misguided intentions matter? Can it hurt if people are helped to understand if people are different? Won’t it draw attention to important things like caring for our learners? Isn’t it good if people are more open-minded?
Would that this were true. However, let me spin it another way: does it matter if we invest in things that don’t have an impact? Yes, for two reasons. One, we’re wasting time and money. We will pay for workshops and spend time ensuring our designs have coverage for things that aren’t really worthwhile. And that’s both profligate and unprofessional. Worse, we’re also not investing in things that might actually matter. Like, say, Serious eLearning. That is, research-derived principles about what actually works. Which is what we should be getting dizzy about.
But there are worse consequences. For one, we could be undermining our own design efforts. Some of these myths may have us do things that undermine the effectiveness of our work. If we work too hard to accommodate non-existent ‘styles’, for instance, we might use media inappropriately. More problematic, we could be limiting our learners. Many of the myths want to categorize folks: styles, gender, left/right brain, age, etc. And, it’s true, being aware of how diversity strengthens is important. But too often people go beyond; they’ll say “you’re an XYZ”, and people will self-categorize and consequently self-limit. We could cause people not to tap into their own richness.
That’s still not the worst thing. One thing that most such instruments explicitly eschew is being used as a filter: hire/fire, or job role. And yet it’s being done. In many ways! This means that you might be limiting your organization’s diversity. You might also be discriminatory in a totally unjustifiable way!
Myths are not just wasteful, they’re harmful. And that matters. Please join me in campaigning for legitimate science in our profession. And let’s chase out the snake oil. Please.
Where’s Clark? Fall 2018/Spring 2019 Events Schedule
Here’re the events where I’ll be through the last quarter of this year, and into the next. Of course, you can always find out what’s up at the Quinnovation News page… But this is a more likely place for you to start unless you’re looking to talk to me about work. I hope to see you, virtually or in person, at one of these!
The week of October 22-26, Clark will be speaking (the same week!) at DevLearn on measurement and eLearning science, and at AECT on meta-learning architecture. (Yeah, both in one week…long story.)
On Litmos’ Live Virtual Summit on 7-8 November, Clark will talk Learning Experience. Stay tuned!
Clark will be a guest on Relate’s eLearnChat on 15 Nov.
2019
On the 9th of January, Clark will present The Myths that Plague Us as a webinar for HRDQ-U.
Clark will be presenting in the Modern Workplace Learning track at the LearnTec conference in Karlsruhe, Germany that runs 29-31 January.
Feb 25-27, Clark will serve as host of the Strategy Track at Training Magazine’s annual conference, opening with an overview and closing with a strategy-development session.
Clark will speak to the Charlotte Chapter of ISPI on the Performance Ecosystem on March 14.
At the eLearning Guild’s Learning Solutions conference March 25-28, Clark will be presenting a Learning Experience Design workshop, where we’ll go deep on integrating learning science and engagement.
If you’re at one of these events, please do introduce yourself and say hello (I’m not aloof, I’m just shy; er, ok, at least ’til we get to know one another :).