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Clark Quinn’s Learnings about Learning

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The (Post) Cognitive Perspective

5 October 2021 by Clark 5 Comments

I’m deeply steeped in the cognitive sciences, owing to a Ph.D. in cognitive psych. Fortuitively, this was at the time my advisor was creating the cognitive science program (and more). So I’ve a bias. Yet I also have a fair bit of empirical evidence that taking a cognitive perspective accomplishes things that are hard to do in other ways. So let me make the case that the cognitive perspective is more than just a useful one, but arguably a necessary one.

I‘ll start by reflecting back on something I wrote before, about virtual world affordances. At the time, platforms like Second Life were touting the advantages of an immersive navigable world. Of course, the promises were all-encompassing: everything would move to virtual worlds. In retrospect, it didn‘t eventuate. Why? I argue it’s because the cognitive overhead of virtual worlds means that there has to be a sustained value proposition, and that came from when you truly need 3D immersion and social.  

Similarly, when I wrote my books on games and mobile, I focused on the cognitive impacts. The first reason was because technology was changing so fast that anything hardware-specific would be out of date before the book was published. The second is because our brains don‘t change that fast, so what works will work regardless of the technology .  

Note that our understanding of cognition has changed. We‘re now in a ‘post-cognitive‘ era, where the notion that all our formal, logical thinking is done in our heads is wrong. Research is showing that we‘re far more ‘situated‘ than we think, and distributed as well. That includes distributed across external representations and other people! It’s very contextual, and it’s not all in our heads!

So these days, when I look at things, I try to look with a cognitive (ok, post-cognitive) perspective. I look to see how things align, or not, with how our brains work. When I evaluate learning technologies, for instance, I look to see how well they do things like provide meaningful practice: active and contextualized. You can also see when particular technologies (e.g. VR/AR/AI) will be valuable, and not. Similarly, when I look at workplace change proposals, I look at how well they reflect our mechanisms for adapting to change.  

I‘ll argue that these perspectives are valuable. You can quickly see why most training doesn‘t work, cut through hype from vendors, create explanations about why myths are mythtaken, etc. You can save money, be more effective, etc when you align with how our brains work. I‘ve talked before about how there are gaps. This is the flip side, how to avoid those gaps, and do better.   In short, you‘re better able to assist your organization in being more effective (and efficient).  

That‘s why I‘m pleased that I am able to put these basics into the learning science book, and workshops. It‘s possible to get better at this sort of perspective. It‘s also possible to get it on tap as needed. However, it does take both the cognitive understanding and the experience in applying it. So, how‘s your cognitive perspective?

On a side note, I want to encourage you to consider my workshop at DevLearn on Make It Meaningful, a full day exploring how we make learning experiences deeply engaging (adding to effectiveness). This is also the topic of my online workshop through the Learning Development Accelerator. This is, to me, the most important topic to  complement  learning science. (Available as a book and workshop. ;) In both cases, I’m trying  to help us  stop making boring courses that people want to avoid, and suggest that this  can be done for most any topic. It also leads to more effective learning outcomes! Hope to see you at one! (Of course, if your organization would like your own private version, let me know!)

Complexity in Learning Design

21 September 2021 by Clark Leave a Comment

a fractalI recently mentioned that one of the problems with research is that things are more interconnected than we think. This is particularly true with cognitive research. While we can make distinctions that simplify things in useful ways (e.g. the human information processing system model*), the underlying picture is of a more interactive system.  Which underpins why it makes sense to talk about Learning Experience Design (LXD) and not just instructional design. We need to accommodate complexity in learning design.  (* Which I talk about in Chapter 2 of my learning science book, and in my workshops on the same topic through the Allen Academy.)

We’re recognizing that the our cognition is more than just in our head. Marcia Conner, in her book  Learn More Now  mentioned how neuropeptides passed information around the body. Similarly, Annie Murphy Paul’s  The Extended Mind talks about moving cognition (and learning) into the world. In my Make It Meaningful workshops (online or F2F at DevLearn 19 Oct), I focus on how to address the emotional component of learning. In short, learning is about more than just information dump and knowledge test.

Scientifically, we’re finding there are lots of complex interactions between the current context, our prior experience, and our cognitive architecture. We’re much more ‘situated’ in the moment than the rational beings we want to believe. Behavioral economics and Daniel Kahneman’s research have made this abundantly clear. We try to avoid the hard mental work using shortcuts that work sometimes, but not others. (Understanding when is an important component of this).

We get good traction from learning science and instructional design approaches, for sure. There are good prescriptions (that we often ignore, for reasons above) about what to do and how. So, we should follow them. However, we need more. Which is why I tout LXD  Strategy! We need to account for complexity in learning design approaches.

For one, our design processes need to be iterative. We’ll make our best first guess, but it won’t be right, and we’ll need to tune. The incorporation of agile approaches, whether SAM or LLAMA or even just iterative ADDIE, reflects this. We need to evaluate and refine our designs to match the fact that our audience is more complex than we thought.

Our design also needs to think about the emotional experience as well as the cognitive experience. We want our design processes to systematically incorporate humor, safety, motivation, and more. Have we tuned the challenge enough, and how will we know?  Have we appropriately incorporated story? Are our graphics aligned or adding to cognitive load? There are lots of elements that factor in.

Our design process has to accommodate SMEs who literally can’t access what they do. Also learner interests, not just knowledge. We need to know what interim deliverables, processes for evaluation, times when we shouldn’t be working solo, and tools we need. Most importantly, we have to do this in a practical way, under real-world resource constraints.

Which is why we need to address this strategically. Too many design processes are carry-over from industrial approaches: one person, one tool, and a waterfall process. We need to do better. There’s complexity in learning design, both on the part of our learners, and ourselves as designers. Leveraging what we know about cognitive science can provide us with structures and approaches that accommodate these factors. That’s only true, however, if we are aware and actively address it. I’m happy to help, but can only do so if you reach out. (You know how to find me. ;) Here’s to effective and engaging  learning!

Iterating and evaluating

7 September 2021 by Clark Leave a Comment

Design cycleI’ve argued before about the need for evaluation in our work. This occurs summatively, where we’re looking beyond smile sheets to actually determine the impact of our efforts. However, it also should work formatively, where we’re seeing if we’re getting closer. Yet there are some ways in which we go off track. So I want to talk about iterating and evaluating our learning initiatives.

Let’s start by talking about our design processes. The 800 lb gorilla of ADDIE has shifted from a water flow model to a more iterative approach. Yet it still brings baggage. Of late, more agile and iterative approaches have emerged, not least Michael Allen’s SAM and Megan Torrance’s LLAMA. Agile approaches, where we’re exploring, make more sense when designing for people, with their inherent complexity.

Agile approaches work on the basis of creating, basically, Minimum Viable Products, and then iterating.  We evaluate each iteration. That is, we check to see what need to be improved, and what is good enough. However,  when are we done?

In my workshops, when talking about iteration, I like to ask the audience this question. Frequently, the answer is “when we run out of time and money”. That’s an understandable answer, but I maintain it’s the  wrong answer.

If we iterate until we run out of time and money, we don’t know that we’ve actually met our goals. As I explained about social media metrics, but applies here too, you  should be iterating until you achieve the metrics you’ve set. That means you know what you’re trying to do!

Which requires, of course, that you set metrics about what your solution should achieve. That could include usability and engagement (which come before and after, respectively), but most critically ‘impact’. Is this learning initiative solving the problem we’re designing it to achieve?  Which also means you need to have a discussion of why you’re building it, and how you know it’s working.

Of course, if you’re running out of time and money faster than you’re getting close to your goal, you have to decide whether to relax your standards, or apply for more resources, or abandon your work, or…but at least you’re doing so consciously. Yet this is still better than heuristically determining that three iterations is arbitrarily appropriate, for example.

I do recognize that this isn’t our current situation, and changing it isn’t easy. We’re still asked to make slide decks look good, or create a course on X, etc. Ultimately, however, our professionalism will ask us to do better. Be ready. Eventually, your CFO should care about the return on your expenditures, and it’ll be nice to have a real answer. So, iterating and evaluating  should  be your long term approach. Right?

Making it Meaningful

31 August 2021 by Clark 1 Comment

I volunteer for our local Community Emergency Response Team (CERT; and have learned lots of worthwhile things). On a call, our local organizer mentioned that she was leading a section of the train-the-trainers upcoming event, and was dreading trying to make it interesting. Of course I opened my big yap and said that’s something I’m focusing on, and offered to help. She took me up on it, and it was a nice case study in making it meaningful.

Now, I have a claim that you can’t give me a topic that I can’t create a game for. I’m now modifying that to ‘you can’t give me a topic I can’t make meaningful’.  She’d mentioned her topic was emergency preparedness, and while she thought it was a dull topic, I was convinced we could do it. I mentioned that the key was making it visceral.

I had personal experience; last summer our neighbor was spreading the rumor that we were going to have to evacuate owing to a fire over the ridge. (Turns out, my neighbor was wrong.) I started running around gathering sleeping bags, coats, dog crate, etc. Clearly, I was thinking about shelter. When I texted m’lady, she asked about passports, birth certificates, etc. Doh!

However, even without that personal example, there’s a clear hook. When I mentioned that, she mentioned that when you’re in a panic, your brain shuts down some and it’s really critical to be prepared. However, she mentioned that someone else was taking that bit, and her real topic was different types of disasters. Yet my example had already got her thinking, and she started talking about different people being familiar with an earthquake (here in California).

I thought of how when talking with scattered colleagues, they disclaim about how earthquakes are scary, and I remind them that  every place has its hazards. In the midwest it could be tornados or floods. On the east coast it’s hurricanes. Etc. The point being that everyone has some experience. Tapping into that, talking about consequences, is a great hook.

That’s the point, really. To get people willing to invest in learning, you have to help people see that they  do need it. (Also, that they don’t know it now,  and that this experience will change that.). You need to be engaged in making it meaningful!

Again, in my mind learning experience design (LXD) is about the elegant integration of learning science with engagement. You need to understand both. I’ve got a book and a workshop on learning science, and I’ve a workshop at DevLearn on the engagement side. I’ve also got a forthcoming book and an online workshop coming for more on engagement. Stay tuned!

More Marketing Malarkey

10 August 2021 by Clark 2 Comments

As has become all too common, someone decided to point me to some posts for their organization. Apparently, interest was sparked by a previous post of mine where I’d complained about  microlearning. While this one  does a (slightly) better job talking about  microlearning, it is riddled with other problems. So here’s yet another post about  more marketing malarkey.

First, I don’t hate microlearning; there are legitimate reasons to keep content small. It can get rid of the bloat that comes from contentitis, for one. There are solid reasons to err on the side of performance support as well. Most importantly, perhaps, is also the benefit of spacing learning to increase the likelihood of it being available. The thing that concerns me is that all these things are different, and take different design approaches.

Others have gone beyond just the two types I mention. One of the posts  cited a colleague’s more nuanced presentation about small content, pointing out four different ways to use microlearning (though interestingly,  five were cited in the referenced presentation). My problem, in this case, wasn’t the push for microlearning (there were some meaningful distinctions, though no actual mention how they require different design). Instead, it was the presence of myths.

One of the two posts opened with this statement: “The appetite of our employees is not the same therefore, we must not provide them the same bland food (for thought).” This seems a bit of a mashup. Our employees aren’t the same, so they need different things? That’s personalization, no? However, the conversation goes on to say: “It‘s time to put together an appetizing platter and create learning opportunities that are useful and valuable.”  Which seems to argue for engagement. Thus, it seems like it’s instead arguing that people need more engaging content. Yes, that’s true too. But what’s that got to do with our employees not having the same appetite? It  seems to be swinging towards the digital native myth, that employees now need more engaging things.

This is bolstered by a later quote: “When training becomes overwhelming and creates stress, a bite-sized approach will encourage learning.” If training becomes overwhelming and stressful, it  does suggest a redesign. However, my inclination would be to suggest that ramping up the WIIFM and engagement are the solution. A bite-sized approach, by itself, isn’t a solution to engagement. Small wrong or dull content isn’t a solution for dull or wrong content.

This gets worse in the other post. There were two things wrong here. The first one is pretty blatant:

There are numerous resources that suggest our attention spans are shrinking. Some might even claim we now have an average attention span of only 8 seconds, which equals that of a goldfish.

There are, of course, no such resources pointed to. Also, the resources that proposed this have been debunked. This is actually the ‘cover story’ myth of my recent book on myths! In it, I point out that the myth about attention span came from a misinterpreted study, and that our cognitive architecture doesn’t change that fast. (With citations.) Using this ‘mythtake’ to justify microlearning is just wrong. We’re segueing into tawdry marketing malarkey here.

This isn’t the only problem with this post, however. A second one emerges when there’s an (unjustified) claim that learning should have 3E’s: Entertaining, Enlightening, and Engaging. I do agree with Engaging (per the title of my first book), however, there’s a problem with it. And the other ones. So, for Entertaining, this is the followup: “advocates the concept of learning through a sequence of smaller, focused modules.” Why is smaller inherently more entertaining? Also, in general, learning doesn’t work as well when it’s just ‘fun’, unless it’s “hard fun”.

Enlightening isn’t any better. I do believe learning should be enlightening, although particularly for organizational learning it should be transformative in terms of enhancing an individual’s ability to  perform. Just being enlightened doesn’t guarantee that. The followup says: “Repetition, practice, and reinforcement can increase knowledge.” Er, yes, but that’s just good design. There’s nothing unique to microlearning about that.

Most importantly, the definition for Engaging is “A program journey can be spaced enough that combats forgetting curve.” That is spacing! Which isn’t a bad thing (see above), but not your typical interpretation of engaging. This is really confused!

Further, I didn’t even need to fully parse these two posts. Even on a superficial examination, they fail the ‘sniff test’. In general, you should be avoiding folks that toss around this sort of fluffy biz buzz, but even more so when they totally confound a reasonable interpretation of these concepts. This is just more marketing malarkey. Caveat emptor.

(Vendors, please please please stop with the under-informed marketing, and present helpful posts. Our industry is already suffering from too many myths. There’s possibly a short-term benefit, however the trend seems to be that people are paying more attention to learning science. Thus, in the long run I reckon it undermines your credibility. While taking them down is fun and hopefully educational, I’d rather be writing about new opportunities, not remedying the old.  If you don’t have enough learning science expertise to do so, I can help: books, workshops, and/or writing and editing services.)

 

Doing Gamification Wrong

22 June 2021 by Clark 8 Comments

roulette wheelAs I’ve said before, I’m not a fan of ‘gamification’. Certainly for formal learning, where I think intrinsic motivation is a better area to focus on than extrinsic. (Yes, there are times it makes sense, like tarting up rote memory development, but it’s under-considered and over-used.)  Outside of formal learning, it’s clear that it works in certain places. However, we need to be cautious in considering it a panacea. In a recent instance, I actually think it’s definitely misapplied. So here’s an example of doing gamification wrong.

This came to me via a LinkedIn message where the correspondent pointed me to their recent blog article. (BTW, I don’t usually respond to these, but if I do, you’re going to run the risk that I poke holes. 😈) In the article, they were talking about using gamification to build organizational engagement. Interestingly, even in their own article, they were pointing to other useful directions unknowingly!

The problem, as claimed, is that working remote can remove engagement. Which is plausible. The suggestion, however, was that gamification was the solution. Which I suggest is a patch upon a more fundamental problem. The issue was a daily huddle, and this quote summarizes the problem: “there is zero to little accountability of engagement and participation “.  Their solution: add points to these things. Let me suggest that’s wrong.

What facilitates engagement is a sense of purpose and belonging. That is, recognizing that what one does contributes to the unit, and the unit contributes to the organization, and the organization contributes to society. Getting those lined up and clear is a great way to build meaningful engagement. Interestingly, even in the article they quote: “to build true engagement, people often need to feel like they are contributing to something bigger than themselves.” Right! So how does gamification help? That seems to be trying to patch a  lack of purpose. As I’ve argued before, the transformation is not digital first, it’s people first.

They segue off to microlearning, without (of course) defining it. They ended up meaning spaced learning (as opposed to performance support). Which, again, isn’t gamification but they push it into there. Again, wrongly. They do mention a successful instance, where Google got 100% compliance on travel expenses, but that’s very different than company engagement. It’s  got to be the right application.

Overall, gamification by extrinsic motivation can work under the right circumstances, but it’s not a solution to all that ails an organization. There are ways and times, but it’s all too easy to be doing gamification wrong. ‘Tis better to fix a broken culture than to patch it. Patching is, at best, a temporary solution. This is certainly an example.

 

Update on my events

17 June 2021 by Clark Leave a Comment

In January I posted about my upcoming webinars (now past), workshops, etc. As things open up again (yay, vaccines), some upcoming events will be happening live!  And, of course, virtual. In fact, one starts next week! So I thought it time to update you on the things I’ll be doing. Then we’ll get back to my regular posts ;). So here’s an update on my events.

First, starting next week, is the Learning Development Conference, by the Learning Development Accelerator (caveat: I’m on their advisory board). Last year, it was an experiment. They did several things very well: it was focused on evidence-based approaches, it created timings that worked for a broad section of the world’s populace (e.g. live sessions were offered twice, once early once late), and it had asynchronous content as well as synchronous. It also had ways to maintain contact and discussions. As a result, it was a success, leading to the Accelerator and this second event.

It’s for six weeks, and first I’ve got an asynchronous course on learning science (a subset of the bigger one I do as a blended workshop for HR.com/Allen Academy). I’m also doing two live sessions (at different times) on some of the new results from cognitive science. I’m already dobbed in for one debate, and they’ll likely call on me for more. There are also a suite of the top names in evidence-based L&D appearing doing either or both of live or asynchronous content.

Second, at the end of August, I’ll be speaking at ATD’s International Conference and Exposition. This is a live event in Salt Lake City. (My first since the pandemic!) Of course I’m speaking on learning science; the topic of my book with them. There could even be a book-signing event!  If you don’t know ATD’s ICE, it’s huge, both a blessing and curse. Lots of quality content (ok, mostly ;), almost too many people to find your friends, but lots of new friends to make, with broad coverage. Also, a big exposition (maybe smaller this year ;).

Third, I’ll be at the Learning Guild’s DevLearn again this year. This has always been one of the best conferences because the Guild runs good events (caveat: I’m their first Guild Master). They want it to grow, of course, but as yet it’s still be reasonably sized, and with quality content. For one, I’ll be speaking on learning science implications.

I’ll  also be running a pre-conference workshop on Making Learning Meaningful. And this is, I suggest, truly of interest. I’ve been seeing more and more examples of well-designed content that’s still lacking in engagement, and this workshop is all about that. It’s an area I’ve been actively exploring and synthesizing into practical implications. Like in the series I did on the topic here, I cover how to hook initial interest, then maintain it through the experience. Also considered are the implications for the elements of learning, and a process to make it practical.

I recommend all three (or I wouldn’t be inclined to speak at them). So that’s the current update on my events. Hope to see you at one or another!

New recommended readings

8 June 2021 by Clark Leave a Comment

My Near Book ShelfOf late, I‘ve been reading quite a lot, and I‘m finding some very interesting books. Not all have immediate take homes, but I want to introduce a few to you with some notes. Not all will be relevant, but all are interesting and even important. I‘ll also update my list of recommended readings. So here are my new recommended readings. (With Amazon Associates links: support your friendly neighborhood consultants.)

First, of course, I have to point out my own Learning Science for Instructional Designers. A self-serving pitch confounded with an overload of self-importance? Let me explain. I am perhaps overly confident that it does what it says, but others have said nice things. I really did design it to be the absolute minimum reading that you need to have a scrutable foundation for your choices. Whether it succeeds is an open question, so check out some of what others are saying. As to self-serving, unless you write an absolute mass best-seller, the money you make off books is trivial. In my experience, you make more money giving it away to potential clients as a better business card than you do on sales. The typically few hundred dollars I get a year for each book aren‘t going to solve my financial woes! Instead, it‘s just part of my campaign to improve our practices.

So, the first book I want to recommend is Annie Murphy Paul‘s The Extended Mind. She writes about new facets of cognition that open up a whole area for our understanding. Written by a journalist, it is compelling reading. Backed in science, it’s valuable as well. In the areas I know and have talked about, e.g. emergent and distributed cognition, she gets it right, which leads me to believe the rest is similarly spot on. (Also her previous track record; I mind-mapped her talk on learning myths at a Learning Solutions conference). Well-illustrated with examples and research, she covers embodied cognition, situated cognition, and socially distributed cognition, all important. Moreover, there‘re solid implications for the redesign of instruction. I‘ll be writing a full review later, but here‘s an initial recommendation on an important and interesting read.  

I‘ll also alert you to Tania Luna‘s and LeeAnn Renninger‘s Surprise. This is an interesting and fun book that instead of focusing on learning effectiveness, looks at the engagement side. As their subtitle suggests, it‘s about how to Embrace the Unpredictable and Engineer the Unexpected. While the first bit of that is useful personally, it‘s the latter that provides lots of guidance about how to take our learning from events to experiences. Using solid research on what makes experiences memorable (hint: surprise!) and illustrative anecdotes, they point out systematic steps that can be used to improve outcomes. It‘s going to affect my Make It Meaningful  work!

Then, without too many direct implications, but intrinsically interesting is Lisa Feldman Barrett‘s How Emotions Are Made. Recommended to me, this book is more for the cog sci groupie, but it does a couple of interesting things. First, it creates a more detailed yet still accessible explanation of the implications of Karl Friston‘s Free Energy Theory. Barrett talks about how those predictions are working constantly and at many levels in a way that provides some insights. Second, she then uses that framework to debunk the existing models of emotions. The experiments with people recognizing facial expressions of emotion get explained in a way that makes clear that emotions are not the fundamental elements we think they are. Instead, emotions social constructs! Which undermines, BTW, all the facial recognition of emotion work.

I also was pointed to Tim Harford‘s The Data Detective, and I do think it‘s a well done work about how to interpret statistical claims. It didn‘t grip me quite as viscerally as the afore-mentioned books, but I think that‘s because I (over-)trust my background in data and statistics. It is a really well done read about some simple but useful rules for how to be a more careful reviewer of statistical claims. While focused on parsing the broader picture of societal claims (and social media hype), it is relevant to evaluating learning science as well.  

I hope you find my new recommended readings of interest and value. Now, what are you recommending to me? (He says, with great trepidation. ;)

The case for model answers (and a rubric)

3 June 2021 by Clark 4 Comments

Human body modelAs I‘ve been developing online workshops, I‘ve been thinking more about the type of assessment I want. Previously, I made the case for gated submissions. Now I find another type of interaction I‘d like to have. So here‘s the case for model answers (and a rubric).

As context, many moons ago we developed a course on speaking to the media. This was based upon the excellent work of the principals of Media Skills, and was a case study in my  Engaging Learning book. They had been running a face to face course, and rather than write a book, they wondered if something else could be done. I was part of a new media consortium, and was partnered with an experienced CD ROM developer to create an asynchronous elearning course.  

Their workshop culminated in a live interview with a journalist. We couldn‘t do that, but we wanted to prepare people to succeed at that as an optional extra next step. Given that this is something people really fear (apocryphally more than death), we needed a good approximation. Along with a steady series of exercises going from recognizing a good media quote, and compiling one, we wanted learners to have to respond live. How could we do this?

Fortunately, our tech guy came up with the idea of a programmable answering machine. Through a series of menus, you would drill down to someone asking you a question, and then record an answer. We had two levels: one where you knew the questions in advance, and the final test was one where you‘d have a story and details, but you had to respond to unanticipated questions.  

This was good practice, but how to provide feedback? Ultimately, we allowed learners to record their answers, then listen to their answers and a model answer. What I‘d add now would be a rubric to compare your answer to the model answer, to support self-evaluation. (And, of course, we’d now do it digitally in the environment, not needing the machine.)

So that‘s what I‘m looking for again. I don‘t need verbal answers, but I do want free-form responses, not multiple-choice. I want learners to be able to self-generate their own thoughts. That‘s hard to auto-evaluate. Yes, we could do whatever the modern equivalent to Latent Semantic Analysis is, and train up a system to analyze and respond to their remarks. However, a) I‘m doing this on my own, and b) we underestimate, and underuse, the power of learners to self-evaluate.  

Thus, I‘m positing a two stage experience. First, there‘s a question that learners respond to. Ideally, paragraph size, though their response is likely to be longer than the model one; I tend to write densely (because I am). Then, they see their answer, a model answer, and a self-evaluation rubric.  

I‘ll suggest that there‘s a particular benefit to learners‘ self-evaluating. In the process (particularly with specific support in terms of a mnemonic or graphic model), learners can internalize the framework to guide their performance. Further, they can internalize using the framework and monitoring their application to become self-improving learners.

This is on top of providing the ability to respond in richer ways that picking an option out of those provided. It requires a freeform response, closer to what likely will be required after the learning experience. That‘s similar to what I‘m looking for from the gated response, but the latter expects peers and/or instructors to weigh in with feedback, where as here the learner is responsible for evaluating. That‘s a more complex task, but also very worthwhile if carefully scaffolded.  

Of course, it‘d also be ideal if an instructor is monitoring the response to look for any patterns, but that‘s outside the learners‘ response. So that‘s the case for model answers. So, what say you? And is that supported anywhere or in any way you know?

How to be an elearning expert

1 June 2021 by Clark 3 Comments

I was asked (and have been a time or two before): “What’s the one most important thing you’d like to tell to be successful Ed Tech industry leader” Of course there wasn‘t just one ;). Still, looking at colleagues who I think fit that characterization, I find some commonalities that are worth sharing. So here‘s one take on how to be an elearning expert.

Let‘s start with that ‘one thing‘.   Which is challenging, since it‘s more than one thing! Still, I boiled it down into two components: know your stuff, and let people know.   That really is the core. So let‘s unpack that some more.   The first thing is to establish credibility. Which means demonstrating that you track and promote the right stuff.  

Some folks have created a model that they tout. Cathy Moore has Action Mapping, Harold Jarche has PKM, Con Gottfredson has the 5 moments of need, and so on.   It‘s good having a model, if it‘s a good, useful one (there are people who push models that are hype or ill-conceived at best). Note that it‘s not necessarily the case that these folks are just known for this model, and most of these folks can talk knowledgeably about much more, but ‘owning‘ a model that is useful is a great place to be. (I occasionally regret that I haven‘t done a good job of branding my models.) They understand their model and its contribution, it‘s a useful one, and therefore they contribute validly that way and are rightly recognized.

Another approach like this is owning a particular domain. Whether gaming (e.g. Karl Kapp), visuals (Connie Malamed), design (Michael Allen), mixed realities (Ann Rollins), AI (Donald Clark), informal (Jane Hart), evaluation (Will Thalheimer), management (Matt Richter), and so on, they have deep experience and a great conceptual grasp in a particular area. Again, they can and do speak outside this area, but when they talk about these topics in particular, what they say is worthy of your attention!

Then there are other folks who don‘t necessarily have a single model, but instead reliably represent good science. Julie Dirksen, Patti Shank, Jane Bozarth, Mirjam Neelen, and others  have established a reputation for knowing the learning science and interpreting it in accurate, comprehensible, and useful ways.  

The second point is that these folks write and talk about their models and/or approaches. They‘re out there, communicating. It‘s about reliably saying the important things again and again (always with a new twist). A reputation doesn‘t just emerge whole-cloth, it‘s built step by step. They also practice what they preach, and have done the work so they can talk about it. They talk the talk and walk the walk. Further, you can check what they say.  

So how to start? There are two clear implications. Obviously, you have to Know. Your. Stuff! Know learning, know design, know engagement, know tech. Further, know what it means in practice!   You can focus deeply in one area, or generate one useful and new model, or have a broad background, but it can‘t just be in one thing. It‘s not just all your health content for one provider. What you‘re presenting needs to be representative and transferable.  Further, you need to keep up to date, so that means continually learning: reading, watching, listening.

Second, it‘s about sharing. Writing and speaking are the two obvious ways. Sure, you can host a channel: podcast, vlog, blog, but if you‘re hosting other folks, you‘re seen as well connected but not necessarily as the expert. Further, I reckon you have to be able to write and speak (and pretty much all of these folks do both well).   So, start by speaking at small events, and get feedback to improve. Study good presentation style. Then start submitting for events like the Learning Guild, ATD, or LDA (caveats on all of these owing to various relationships, but I think they‘re all scrutable). I once wrote about how to read and write proposals, and I think my guidance is still valid.

Similarly, write. Learning Solutions or eLearn Mag are two places to put stuff that‘s sensibly rigorous but written for practitioners.   Take feedback to heart, and deliberately improve. Make sure you‘re presenting value, not pitching anything. What conferences and magazines say about not selling, that your clear approach is what sells, is absolutely true.  

Also, make sure that you have a unique ‘voice’. No one needs the same things others are saying, at least in the same way. Have a perspective, your own take. Your brand is not only what you say, but how you say it.

A related comment: track some related fields. Most of the folks I think of as experts have some other area they draw inspiration from. UX/UI, anthropology, software engineering, there are many fields and finding useful insight from a related one is useful to the field and keeps you fresh.

Oh, one other thing. You have to have integrity. People have to be able to trust what you say. If you push something for which you have a private benefit, or something that‘s trendy but not real, you will lose whatever careful credibility you‘ve built up. Don‘t squander it!  

So that‘s my take on how to be an elearning expert. So, what have I missed?

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