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Clark Quinn’s Learnings about Learning

Archives for August 2020

Authentic Marketing

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I’m not a marketing expert, or even a marketer, so take the following with the proverbial boulder of salt. Still, I have to market Quinnovation, and I’ve advised orgs on marketing (learning) products, and I’ve taken down a lot of bogus marketing. So when something prompted me to reflect, I realized I had some thoughts on authentic marketing.

First, I’ve argued that good marketing is really good customer education. That is, you should be helping your customers understand why your product is the right thing for their needs. Of course, you should be first designing to ensure that it  is the answer. Or, perhaps,  an answer, and then helping your customers to understand if they’re the right customer for this solution.

And, when I’ve studied marketing for services, there are several steps that make sense. First, the clear thing is knowing your customer’s pain, and being able to articulate how you solve that pain. You want to help articulate clearly what the problem is, and what’s it’s costing, so that then you can suggest a solution and the benefits.

At core it’s about building up a solid, scrutable, case. Which is, in essence, building up trust that you know what you’re talking about, and that you can truly meet the need.

And that may not be the quick easy way. It appears to be the case that some folks would rather use clickbait-style advertising. Perhaps to cover up from not having a defensibly different product or solution? When there are hundreds of LMSs around, how do you differentiate yourself?   And I guess it works, because I keep finding new examples of marketing that goes for the cheap ploy rather than authentic education.

So I guess this is a plea for being an aware consumer. It’d be great if orgs started building products that really do make their customers awesome, and then use authentic marketing to sell them. In lieu of that, be wary. Look for unsubstantiated hype, buzzword bandwagon behavior, and style over substance. Know what you need, take your time to do due diligence, and spend wisely. Caveat emptor, after all.

The plusses and minuses of learning science research

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A person who I find quite insightful (and occasionally inciteful ;) is Donald Clark. He built and sold Epic, an elearning company, and now he leads a learning AI company, Wildfire. He’s knowledgeable (for instance, having read up and summarized centuries of learning theorists), willing to call out bad learning, and he’s funny. And so, when he reported on a new study, I of course looked into it. And I find that it points out the plusses  and  minuses of learning science research.

To be clear, this is about his product, so there’s a vested interest. However, he’s got integrity; he’s not going to sully his reputation with a bad study. And, it’s a good study. It rightly demonstrates an important point. It’s just that it stops short of what we need for full  learning.

So, his product does something pretty amazing. You give it content, and it can not only answer questions about the content (as, for instance, some chat tools do), it can turn the tables and ask  you questions about the content. That is, it can serve as a sort of tutor. Which is all to the good.

What it can’t do, of course, is design meaningful practice. As Van Merriënboer’s Four Component Instructional Design (4C/ID) points out, you need to know the information, and you also need practice applying it. And I reckon we’re still far from that. So, while this is part of a whole solution (and Donald knows this), it’s not the full solution. He’s subsequently let me know it can do language tasks, which is impressive. I’m thinking more of contextualized scenarios, however.

The study demonstrates, as you might expect, that breaking up a video into reasonable chunks, and having system-generated questions asked in-between, led to 61% better retrieval, going from getting 8 to 14 questions right. That is a big improvement. it’s also impressive, since it’s generating those questions from video! That is, it parses the video, establishes a transcript, and then uses that to generate a knowledge base. Very cool.

And it’s a well-designed study. It’s got a control group, and a  reasonable number of subjects. It uses the same test material, for an AB comparison. Presumably, the video chunking was done by hand, into four pieces. The chunking and break might account for the difference, which wasn’t controlled for, but it’s still a big improvement. Granted, we know that watching a video alone isn’t necessarily going to improve retention (except, perhaps, over some other non-interactive way of dumping content). But still, this is good as it’s an improvement and a lot of work was saved.

What I quibble about, however, is the nature of the retrieval. The types of questions liable to be asked (and it’s not indicated), are knowledge questions. As suggested above, knowledge is a necessary component. But using that knowledge to make decisions in context is typically what our goals are. And to achieve such goals, you basically have to practice making decisions in context. (Interestingly, the topic here was equality and diversity, a topic he has complained about!)

Knowledge about a topic isn’t likely to impact your ability to apply it. What will  make a difference are actually doing things about it, like calling it out, having consequences, and actively working to remedy imbalances. And that requires separate practice. Which he’s acknowledged in the past, and rightly points out that his solution means you can devote more resources to that end.

Thus, the plusses of learning science research are we nibble away at the questions we need to answer, and find answers about the questions we ask. The minus, of course, is not necessarily asking the most important questions. It’d be easy to see this and say: “we’ve improved retention, and we’re done”. However, it won’t necessarily lead to reducing the behaviors being learned about, or building ability to deal with it.  There are plusses and minuses of learning science research, and we need to know the strengths, and limitations, of it when we hear it.

The case for learning science

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In a perfect world, we’d spend all the time we want on learning. However, we don’t live in that world, we live in the real world. Which means our decisions are about tradeoffs. Which means we have to evaluate the case for paying attention to research. So here’s a stab at the case for learning science.

Learning is a probabilistic game. That is, there’s a probability that anything we’ve invested in learning will arise at the appropriate time. I’ll suggest that our brains have some randomness built into them, so there’s always a chance we’ll do things differently. Thus, in a sense, learning design is about increasing the probability that the right thing will occur.

And there are consequences. Say, for instance, that we want people to wash hands sufficiently. Then we might rightly work to increase awareness (along with making sure there’s soap, water, sinks, towels, etc) and the proper procedure. To do that sufficiently, say, takes X minutes of instruction.

Yet we may not have X minutes. Given the drain on resources, allocating that much time means the cost of the washing may be more than the cost of not. People not on the job for that time are an expensive resource. What’s a manager to do?

What we do all the time is make a probabilistic decision. We provide the rules in places where people might get their hands dirty, and we provide support materials (e.g. signs on the walls) in the places you wash your hands.

Most importantly, however, we make a determination of what’s a level of time that is going to likely do ‘good enough’. We’ll spend X-Y minutes, and make essentially a gamble that we’ll get 80% there, and the support materials will do the rest.

What this means is that since we’re not allocating sufficient time, we should be optimizing the quality of the learning design we apply. If they’re only getting X-Y minutes, that time should be as effective as possible. Which means we practice serious learning design, reflecting the best practices.

Quite simply, if you do less than use the best learning design principles derived from research, you’re decreasing the value of your investment in design time and learner time. And there are lots of ways we go wrong, whether it’s myths or just underinformed design. It’s a matter of professionalism as well. So let’s be smart and design smart. That’s the case for learning science. We owe it to our learners and our organizations.

Top 10 Tools for Learning 2020

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It’s time, once again, for Jane Hart’s excellent Top 10 Tools for Learning survey. And, so, it’s time once again for my reflections. Here are my take on the top 10 tools that support my learning.

The first way I learn is to process what I’ve seen. That, toolwise, is largely about representing and communicating.

Processing Tools

1-2. Writing is arguably the top way I reflect. And, so that’d put Microsoft Word at the top of my list. That’s where I write books and articles first. And, of course WordPress is how I write my blog (e.g. here!).   Writing is a way to sort out how I think about things. As I say, things that end up in presentations and books tend to show up on blog first. Well, one of the main ways.

3-4. Besides writing, two ways I sort out my understandings are to diagram and to outline. I use OmniGraffle as a general purpose diagramming tool because, well, it largely works the way I want to think about it. Diagrams, mind maps, even recently as sort of posterboard. And I use OmniOutliner to do, well, outlines. Another way to map out structures. I’d use a less costly tool, but…the columns feature is really helpful for annotation. Both, unfortunately, are Mac only (and sadly quite dear).

5. Keynote is how I create presentations, another way I do, and then share, my thinking. Diagrams are a big part of my talks, punctuated with stock photos to represent concepts (from Pixabay and occasionally Unsplash). I believe (and don’t have evidence for) that using an image that relates to the concept but doesn’t exactly communicate it leaves open some curiosity that then gets connected. And that this leads to better comprehension (I avoid bullet points in live presos, and save them for handouts). Anyone got that data?

The second thing I do is see what other people are pointing to and have to say, and ask them questions   as well. So the second category is about interacting with others.

Social tools

6. Twitter is a regular feature of how I see what people are pointing to, as well as pointing to things I’ve found as well. Chats there are fun, too. Like Jane, Tweetdeck is my tool of course on my Mac. I have to use the Twitter client on iPad/iOS, since they’ve taken away Tweetdeck on the iPad (grr).

7. I like FeedBlitz as a way to sign up for blogs, as it brings them into my inbox, instead of me needing a separate app. Reading a select list of blogs is one of my tactics. That’s how people can sign up to get my blog in email, too.

8. Slack has also been a major component of getting things done, mostly with IBSTPI. It’s a handy way to get things done with others.

9-10. Social networks are a big part of my learning, which means that Facebook and LinkedIn also play big roles. Facebook’s more personal, ie less about work, but I learn about   many societal things there. And LinkedIn is a place for learning as well, professionally as opposed to personally.

And…

Honorable Mention: to round out the picture (10 is such an arbitrary number ;), sharing collaborative documents, e.g. Google Docs, is a major way to collaboratively process and learn together. Also socially, Zoom and BlueJeans (the latter’s almost the same, and what ISBTPI uses) are used a lot to discuss and negotiate understandings. And email, of course (using the Mac Mail client) is a major way I learn, e.g. blogs appear there, and it’s a major way I interact.

DuckDuckGo has become my goto search engine (and Brave as my browser,  at least on my Mac, awaiting cross-device sync), because I don’t need to spread my data any further than necessary. And searching is a big part of my learning.

As an aside, owing to the pandemic, like everyone else I’ve been doing much more with Zoom to interact with colleagues than I had in the past. And I find, interestingly, that the ways I reach out are more opportunistic: I’ll use FB Messenger, or a Twitter DM, or a LinkedIn message, or an email depending on who, why, and what tool I’m in at the time. There may be some method to the madness, but I’m not confident on that point ;).

So, there’re my Top 10 Tools for Learning. I hope you’ll post or send your list to Jane too, so we can continue to see what emerges.

 

Tips to Avoid Millennials Marketing Hype

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I received, in my email, a solicitation for a webinar titled 5 Tips to Engage Gen Z and Millennial eLearners in 2020 and Beyond.  And, as you might imagine, it tweaked my sensibilities for the worse. My initial reaction is to provide, as a palliative, tips to avoid millennials marketing hype.

The content starts off with this scintillating line: “if you‘re searching for current, new ways to engage people online and keep your business thriving, look to your youngest learners.” What? Why do you want current and new ways to engage people? How about the evidence-based ways instead? Tested and validated ones. And why your youngest learners? Organizations need to be continually learning across all employees. Why not just your  newest employees (regardless of age)?

So, your first tip is to look for phrases like ‘new’ as warnings, and look for “research-based” or “evidence-based” instead. “Science-based” is likely okay, as long as it’s not neuroscience-based (wrong level) or brain-based (which is like saying ‘leg-based walking’ as someone aptly put it.)

Second tip: don’t be ageist. Why focus on their age at all? Deal with people by their knowledge and background. It’s discriminatory, really.

The ad goes on: “To future-proof your learning program, make sure your content is designed with these young professional learners in mind.”   What’s different for these learners? Their cognitive architecture isn’t fundamentally different; evolution doesn’t work that fast. So why would you do something just for them (and discriminate against others) instead of doing what’s right for the topic?

Next tip: avoid any easy and inappropriate categorizations. Don’t try to divide content or experiences in trendy ways instead of meaningful ways.

You should already be leery. But wait, there’s more! “On one hand, they can be distracted, overwhelmed, and impatient. On the other, they are highly collaborative, technically-savvy, and driven by fairness and storytelling.”This is like a horoscope; it fits most everyone, not just young people. We all have distractions and increasingly feel overwhelmed. And our brains are wired for storytelling.   These describe human nature! And that ‘tech savvy’ bit is a clear pointer to the digital native myth. Doh!

They then go on. “With this in mind, how can you effectively engage this digitally dependent group to attract, train, and retain them?” Um, with what attracts, trains, and retains humans in general?  That would be helpful!

Thus, another tip: let’s not make facile attributions that falsely try to portray a meaningful difference. Let’s focus on design that addresses capturing and maintaining attention and motivation, and communicating in clear and compelling ways. And skip mashing up myths, ok?

We’re not  quite done with the pitch: “how to level up your existing learning strategy to meaningfully engage your Millennial and Gen Z learners.” This is just a rehash of the tips above. Meaningfully engage  all your learners!

There’s also this bullet list of attractions:

  • What motivates Millennials and Gen Z and how to tailor your learning strategy to keep them engaged
  • Ways in which traditional learning programs fail younger learners and how you can prevent these common mistakes
  • A step-by-step process for evaluating your instructional content, providing you with an actionable blueprint on transforming your content

This could easily be rewritten as:

  • What motivates Millennials and Gen Z learners and how to tailor your learning strategy to keep them engaged
  • Ways in which traditional learning programs fail younger learners and how you can prevent these common mistakes
  • A step-by-step process for evaluating your instructional content, providing you with an actionable blueprint on transforming your content

And, for all I know, that’s what they’re really doing. That would be actually useful, if they avoid perpetuating the myths about generational differences. But, as you can tell, they’re certainly trying to hit buzzword bingo in drawing you in with trendy and empty concepts. Whether they actually deliver is another issue.

Please, avoid the marketing maelstrom. Follow these tips to avoid millennials marketing hype, and focus on real outcomes. Thanks!

Thinking Transformation

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This pandemic has led to everyone scrambling to work digitally. And it‘s not really a transformation (which shouldn‘t be ‘digital first‘), but rather just ‘move what we do online‘. And that‘s understandable. Over time, however, I think we want to shift our mindset. And, I think a previous exercise in thinking transformation is valuable here. I‘m talking mobile.

When I originally was talking about mobile, I was doing so from a perspective of augmenting our brains. The 4C‘s framework was a way to think about core mobile affordance from a point of view of what mobile offers. Then I moved on to the role the devices play in our (working) life. It‘s about not just courses on a phone, but:

  • Augmenting formal learning: extending it
  • Performance support: cognitive augmentation
  • Social: tapping into the power of social and informal learning
  • Contextual: mobile‘s unique opportunity

And, I suggest, these are valuable ways to think about using technology in general to support us. On principle, I like to think about how technology supports our thinking (not the other way around ;). To future-proof what I propose is one driver, so tech changes don’t undermine relevance. Further, since mobile is a platform – a strategy not just a tactic – focusing on fundamentals makes sense.

For instance, elearning shouldn‘t look like just a classroom online. That can and arguably should be part of it, but there‘s more. It‘s about extending formal learning, not just delivering it. And aligning with how we really learn, because it really does have to be effective.

Similarly, with folks working from wherever, thinking about the support they need is important. What tools, aids, guides, etc., will help them work more effectively without their prior context? Let’s change workflows to align better with what‘s known about how we work.

And making people available in useful ways for communication and collaboration is important. The demands of online meetings are becoming more prevalent and onerous. Zoom fatigue is a thing!   How can we optimize the experience?

Contextual is more uniquely mobile, taking advantage of where and when you are (and other contextual factors), but we probably do need to account for them more astutely. If your kids are in the other room, what does that do to your ability to work? Here, of course, is the greatest difference from mobile, but the mindset is still relevant.

So, for instance, when I ran a mobile course for the Allen Academy, we had a week dedicated to each of these elements (as well as kicking off a mobile mindset and closing on strategy). Given that it was still early in this new world, I didn‘t really push the thought of how this is a more general tactic. Of course, I now would.

And, given that I‘ll be running the course again, I definitely will! Look, mobile hasn‘t gone away, and we‘re possibly using mobile tools more now even though we‘re not on the road! So mobile‘s still relevant, and the mindset behind ‘thinking mobile‘ is even more relevant. I’ll be talking with Christopher Allen of Allen Interactions about it tomorrow (12 Aug) at 11AM PT, 2 Eastern (see below). And, if you‘re interested in the course, check it out!

Curious about Curiosity

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Looking into motivation, particularly for learning, certain elements appear again and again.   So I’ve heard ‘relevance’, ‘meaningfulness‘, consequences, and more. Friston suggests that we learn to minimize surprise. One I’ve heard, and wrestled with, is curiosity. It’s certainly aligned with surprise. So I’ve been curious about curiosity.

Tom Malone, in his Ph.D. thesis, talked about intrinsically motivating instruction, and had curiosity along with fantasy and challenge. Here he was talking about helping learners see that their understanding is incomplete. This is in line with the Free Energy Principle suggesting that we learn to do better at matching our expectations to real outcomes.

Yet, to me, curiosity doesn’t seem enough. Ok, for education, particularly young kids, I see it. You may want to set up some mismatch of expectations to drive them to want to learn something. But I believe we need more.

Matt Richter, in his well-done L&D conference presentation on motivation, discussed self-determination theory. He had a nice diagram (my revision here) that distinguished various forms of motivation. From amotivated, that is, not, there were levels of external motivation and then internal motivation. The ultimate is what he termed intrinsic motivation, but that’s someone wanting it of their own interest. Short of that, of course, you have incentive-driven behavior (gamification), and then what you’re guilted into (technically termed Introjection), to where you see value in it for yourself (e.g. WIIFM).

While intrinsic motivation, passion, sounds good, I think having someone be passionate about something is a goal too far. Instead, I see our goal as helping people realize that they need it, even if not ‘want’ it. That, to me, is where consequences kick in. If we can show them the consequence of having, or not, the skills, and do this for the right audience and skills, we can at least ensure that they’re in the ‘value’ dimension.

So, my take is that while we should value curiosity, we may not be able to ensure it. And we can ensure that, with good analysis and design, we can at least get them to see the value. That’s my current take after being curious about curiosity. I’d like to hear yours!