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Clark Quinn’s Learnings about Learning

Concept Maps and Learning

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Once again, someone notified me of something they wanted me to look at. In this case, a suite of concept maps, with a claim that this could be the future of education. And while I’m a fan of concept maps, I was suspicious of the claim, So, while I’ve written on mindmaps before, it’s time to dig into concept maps and learning.

To start, the main separation between mindmaps and concept maps is labels. Specifically, concept maps have labels that indicate the meaning of  connections between concepts. At least, that’s my distinction. So while I’ve done (a lot of) mindmaps of keynotes, they’re mostly of use to those who also saw the same presentation. Otherwise, the terms and connections don’t necessarily make sense. (Which doesn’t mean a suite of connections can’t be valuable, c.f. Jerry’s Brain, where Jerry Michalski has been tracking his explorations for over two decades!) However, a concept map does a better job of indicating the total knowledge representation.

I know a wee bit about this, because while writing up my dissertation, I had a part-time job working with Professor  Kathy Fisher and SemNet. Kathy Fisher is a biologist and teacher who worked with Joe Novak (who can be considered the originator of concept mapping). SemNet is a Macintosh concept mapping tool (Semantic Network) that Kathy created and used in teaching biology. It allows students to represent their understanding, which instructors can use to diagnose misconceptions.

I also later volunteered for a while with the K-Web project. This was a project with James Burke (of Connections fame) creating maps of the interesting historical linkages his show and books documented. Here again, navigating linkages can be used for educational purposes.

With this background, I looked at this project. The underlying notion is to create a comprehensive suite of multimedia mindmaps of history and the humanities. This, to me, isn’t a bad thing! It provides a navigable knowledge resource that could be a valuable adjunct to teaching. Students can be given tasks to find the relationships between two things, or asked to extend the concept maps, or… Several things, however, are curious at least.

The project claims to be a key to the future of global education. However, as an educational innovation, the intended pedagogical design is worrisome. The approach claims that “They have complete freedom to focus on and develop whichever interests capture their fancy.” and “…the class is exposed to a large range of topics that together provide a comprehensive and lively view of the subject…”  This is problematic for two reasons. First, there appears to be no guarantee that this indeed will provide comprehensive coverage. It’s possible, but not likely.

As a personal example, when I was in high school, our school district decided that the American Civil War would be taught as modules. Teachers chose to offer whatever facets they wanted, and students could take any two modules they wanted. Let me assure you that my knowledge of the Civil War did not include a systematic view of the causes, occurrences, and outcomes, even in ideologically distorted versions. Anything I now know about the Civil War comes from my own curiosity.

Even with the social sharing, a valuable component, there appears to be no guidance to ensure that all topics are covered. Fun, yes. Curricularly thorough, no.

Second, presenting on content doesn’t necessarily mean you’ve truly comprehended it. As my late friend, historian Joseph Cotter, once told me, history isn’t about learning facts, it’s about learning to think like a historian. You may need the cultural literacy first, but then you need to be able to use those elements to make comparisons, criticisms, and more.  Students should be able to  think with these facts.

Another concerning issue in the presentation about this initiative is this claim: “reading long passages of text no longer works very well for the present generation of learners. More than ever, learners are visual learner [sic].” This confounds two myths, the digital native myth with the learning styles myth. Both have been investigated and found to be lacking in empirical support. No one likes to read long passages of text without some intrinsic interest (but we can do that).

In short, while I laud the collection, the surrounding discussion is flawed. Once again, there’s a lack of awareness of learning science being applied. While that’s understandable, it’s not sufficient.  My $0.05.

2021 Top 10 Tools for Learning

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As always, I like to participate in my Internet Time Alliance colleague Jane Hart’s Top 10 Tools for Learning survey. However, in reviewing last year’s list, things haven’t changed much. Still, it’s worth getting out there. So there’re my selections for 2021  top 10 tools for learning.

One of the major things I do is write: book, blog posts, articles, and more. So the first two tools I use are for writing:

1. Word. Yes, not totally pleased about the provider, but I have yet to find a tool with better industrial strength outlining. And, well, I’ve been using it since around 1989, so…there’s some familiarity…

2. WordPress. Of course, that’s how I’m writing here. I also use it for writing for the HPT Treasures blog (I post once a month, third Friday. Also I can occasionally use it for managing other sites (e.g. IBSTPI).

Another way I get my mind around new understandings is by representing information structure. So:

3. OmniGraffle. While this is Apple only, and dear, it so far is the best tool I’ve found to make diagrams. It’s got more capability than I need, but it also works the way I think, so…all told it’s still the winner.

4. OmniOutliner. Outlining is another way I think. While for writing it can be in Word, for other things: checklists, presentations, etc, it helps to have a dedicated tool. Again, Apple only, dear, and overkill, but their cheaper version doesn’t include columns, and that can be helpful!

I also do a fair bit of presentations: keynotes, webinars, and more. While I’m often forced to end up using Powerpoint…

5. Keynote. My native presentation tool.  (yes, I’m an Apple person, what can I say?). It’s just cleaner for me than alternatives.

From there, we get to social tools:

6. Twitter has been a long-standing tool. Tracking other folks, participating in dialogs, and even asking questions, Twitter’s an ongoing contributor to my learning.

7. LinkedIn is where I professionally socialize, and it’s becoming more prominent in my interactions. People reach out to me there, and I also track some folks, and there are occasionally interesting discussions.

8. Zoom has, well, zoomed up to the top of my interaction tool suite.  I’ve used it for chats, meetings, and webinars.

Then, of course, there’s searching for answers.

9. DuckDuckGo. I’ve switched to use this as my search engine, as it’s less-tracking, and provides good results.

10. Safari is still my browser of choice. I’ve experimented with Brave, but it hadn’t synched bookmarks across my devices. Now it does, but it’s hard to switch again.

So that’s my 2021 top 10 learning tools list. (Not really in any order, but I’ve numbered anyway. ;) It’s a personal list, since I’m not formally in education nor part of a workplace. I’ve been using Teams  more, but I still find it has silly limitations, so my preference is Slack.

 

 

A message to CxOs 2: about org learning myths

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When I wrote my last post on a message to CxOs about L&D myths, I got some pushback. Which, for the record, is a good thing; one of us will learn something. As a counter to my claim that L&D often was it’s own worst enemy, there was a counter. The claim was that there are folks in L&D who get it, but fight upward against wrong beliefs. Which absolutely is true as well. So, let‘s also talk about what CxOs need to know about the org learning myths they may believe.  

First, however, I do want to say that there is evidence that L&D isn‘t doing as well as it could and should. This comes from a variety of sources. However, the question is where does the blame lie. My previous post talked about how L&D deludes itself, but there are reasons to also believe in unfair expectations. So here‘s the other side.  

  1. If it looks like schooling… I used this same one against L&D, but it‘s also the case that CxOs may believe this. Further, they could be happy if that‘s the case. Which would be a shame just as I pointed out in the other case. Lectures, information dump & knowledge test, in general content presentation doesn‘t lead to meaningful change in behavior in the absence of activity. Designed action and guided reflection, which looks a lot more like a lab or studio than a classroom, is what we want.
  2. SMEs know what needs to be learned. Research tells us to the contrary; experts don’t have conscious access to around 70% of what they  do (tho’ they do have access to what they know). Just accepting what a SME says and making content around that is likely to lead to a content dump and lack of behavior change. Instead, trust (and ensure) that your designers know more about learning than the SME, and have practices to help ameliorate the problem.
  3. The only thing that matters is keeping costs low.  This might seem to be the case, but it reflects a view that org learning is a necessary evil, not an investment. If we’re facing increasing change, as the pundits would have it, we need to adapt. That means reskilling. And effective reskilling isn’t about the cheapest approach, but the most effective for the money. Lots of things done in the name of learning (see above) are a waste of time and money. Look for impact first.
  4. Courses are the answer to performance issues.  I was regaled with a tale about how sales folks and execs were  insisting that customers wanted training. Without evaluating that claim. I’ll state a different claim: customers want solutions. If it’s persistent skills, yes, training’s the answer. However, a client found that customers were much happier with how-to videos than training for most of the situations. It’s a much more complex story.
  5. Learning stops at the classroom. As is this story. One of the reasons Charles Jennings was touting 70:20:10 was not because of the numbers, but because it was a way to get execs to realize that only the bare beginning came from courses, if at all. There’s ongoing coaching with stretch assignments and feedback, and interacting with other practitioners…don’t assume a course solves a problem. A colleague mentioned how her org realized that it couldn’t create a course without also creating manager training, otherwise they’d undermine the outcomes instead of reinforcing them.
  6. We‘ve invested in an LMS, that‘s all we need. That’s what the LMS vendors want you to believe ;)!  Seriously, if all you’re doing is courses, this could be true, but I’m hoping the above
  7. Customers want training.  Back to an earlier statement, customers want solutions. It is cool to go away to training and get smothered in good food and perks. However, it’s  also known that sometimes that  goes to the manager, not  the person who’ll actually be doing the work! Also, training can’t solve certain types of problems.  There are many types of problems customers encounter, and they have different types of solutions. Videos may be better for things that occur infrequently, onboard help or job aids may meet other needs to unusual to be able to predict for training, etc. We don’t want to make customers happy, we want  to make them successful!
  8. We need ways to categorize people. It’s a natural human thing to categorize, including people. So if someone creates an appealing categorization that promises utility, hey that sounds like a good investment. Except, there are many problems! People aren’t easy to categorize, instruments struggle to be reliable, and vested interests will prey upon the unwary.  Anyone can create a categorization scheme, but validating it, and having it be useful, are both surprisingly big hurdles. Asking people questions about their behavior tends to be flawed for complex reasons. Using such tools for important decisions like hiring and tracking have proven to be unethical. Caveat emptor.
  9. Bandwagons are made to be jumped on. Face it, we’re always looking for new and better solutions. When someone links some new research to a better outcome, it’s exciting. There’s a problem, however. We often fall prey to arguments that appear to be new, but really aren’t. For instance, all the ‘neuro’ stuff unpacks to some pretty ordinary predictions we’ve had for yonks. Further, there are real benefits to machine learning and even artificial intelligence. Yet there’s also a lot of smoke to complement the sizzle. Don’t get misled. Do a skeptical analysis.  This holds doubly true for technology objects. It’s like a cargo cult, what’s has come down the pike must be a new gift from those magic technologists! Yet, this is really just another bandwagon. Sure, Augmented Reality and Virtual Reality have some real potential. They’re also being way overused. This is predictable, c.f. Powerpoint presentations in Second Life, but ideally is avoided. Instead, find the key affordances – what the technology uniquely provides – and match the capability to the need. Again, be skeptical.

My point here is that there can be misconceptions about learning  within  L&D, but it can also be outside perspectives that are flawed. So hopefully, I’ve now addressed both. I don’t claim that this is a necessary and complete set, just certain things that are worth noting. These are org learning myths that are worth trying to overcome, or so I think. I welcome your thoughts!

A message to CxOs: about L&D myths

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If you’re a CEO, COO, CFO, and the like, are you holding L&D to account? Because much of what I see coming out of L&D doesn’t stand up to scrutiny. As I’ve cited in books and presentations, there’s evidence that L&D isn’t up to scratch. And I think you should know a few things that may be of interest to you. So here’re some L&D myths you might want to watch out for.

  1. If it looks like school, it must be learning. We’ve all been to school, so we know what learning looks like, right? Except, do you remember how effective school actually was? Did it give you many of the skills you apply in your job now?  Maybe reading and writing, but beyond that, what did you learn about business, leadership, etc? And how  did you learn those things? I’ll bet not by sitting and listening to lectures presented via bulletpoints. If it looks like schooling, it’s probably a waste of time and money. It should look more like lab, or studio.
  2. If we’re keeping our  efficiency in line with others, we’re doing good. This is a common belief amongst L&D: well, our [fill in the blank: employees served per L&D staff member | costs per hour of training | courses run per year | etc.] is the same or better than the industry average, so we’re doing good. No, this is all about efficiency, not effectiveness. If they’re not reporting on measurable changes in the improvement of business metrics, like sales, customer service, operations,e tc, they’re not demonstrating their worth. It’s a waste of money.
  3. We produce the courses our customers need. Can they justify that? It’s a frequent symptom that the courses that are asked for have little relation to the actual problem. There are many reasons for performance problems, and a reliable solution is to throw a course at it. Without knowing whether it’s truly a function of lack of skill. Courses can’t address problems like the wrong incentives, or a lack of resources. If you’re not ensuring that you’re only using courses when they make sense, you’re throwing away money.
  4. Job aids aren’t our job.  Performance should be the job, not just courses. As Joe Harless famously said: “Inside every fat course there‘s a thin job aid crying to get out.” There are many times when a job aid is a better solution than a course. To believe otherwise is one of the classic L&D myths. If they’re avoiding taking that on, they’re avoiding a cheaper and more effective solution.
  5. Informal learning isn’t our job. Well, it might not be if L&D truly doesn’t understand learning, but they should. When you’re doing trouble-shooting, research, design, etc., you don’t know the answer when you start. That’s learning too, and there is a role for active facilitation of best principles. Assuming people know how to do it isn’t justifiable. Informal learning is the key to innovation, and innovation is a necessary differentiation.
  6. Our LMS is all we need. Learning management systems (which is a misnomer, they’re course management systems) manage courses well. However, if they’re trying to also be resource portals, and social media systems, and collaboration tools, they’re unlikely to be good at all that. Yet those are also functions that affect optimal performance and continual innovation (the two things I argue  should be the remit of L&D). Further, you want the right tool for the job. One all-singing, all-dancing solution isn’t the way to bet for IT in general, and that holds true for L&D as well.
  7. Our investment in evaluation instruments is valuable. If you’re using some proprietary tools that purport to help you identify and characterize individuals, you’re probably being had. If you’re using it for hiring and promotion, you’re also probably violating ethical guidelines. Whether personality, or behavior, or any other criteria, most of these are methodologically and psychometrically flawed. You’re throwing away money. We have a natural instinct to categorize, but do it on individual performance, not on some flawed instrument.
  8. We have to jump on this latest concept.  There’re a slew of myths and misconceptions running around that are appealing and yet flawed. Generations, learning styles, attention spans, neuro-<whatever> and more are all appealing, and also misguided. Don’t spend resources on investing in them without knowing the real tradeoffs and outcomes.These are classic L&D myths.
  9. We  have to have this latest technology. Hopefully you’re resistant to new technologies unless you know what they truly will do for your organization. This holds true for L&D as well. They’re as prone to lust after VR and AR and AI as the rest of the organization. They’re also as likely to spend the money without knowing the real costs and consequences. Make sure they’re coming from a place where they know the unique value the technology brings!

There’s more, but that’s enough for now. Please, dig in. Ask the hard questions. Get L&D to be scrutable for real results, not platitudes. Ensure that you’re not succumbing to L&D myths. Your organization needs it, and it’s time to hold them to account as you do the rest of your organization. Thanks, and wishing you all the best.

Something that emerged from a walk, and, well, I had to get it off my chest. I welcome your thoughts.

ID Support Thyself

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Want to dig a bit deeper into improving design processes. Here, I look at tools,  asking IDs to ‘support thyself’.

As usual, the transcript:

One of the things I do is help organizations improve their design processes. Last week, I talked about when to team up in the process of learning design. Another component of good design, besides knowing when and how to draw in more minds, is baking learning science into your processes. That‘s where tools help. I expect that most orgs do have process support, but…baking in learning science seems not to be there. So here I‘m exhorting IDs to ‘Support Thyself’.  

As I discuss in my forthcoming book, there are nuances to each of the elements of learning design (as I also talked about for Learnnovators). That includes meaningful practice, useful models, motivating intros, and more. The question is how to help ensure that as you develop them, you make sure to address all the elements.

One approach, of course, is to use checklists. Atul Gawande has made the case for checklists in his The Checklist Manifesto.  In this great book, he talks about his own inspiring efforts in the context of other high-risk/high-value endeavors such as flight and construction.   There are clear benefits.

The point is that checklists externalize the important elements, supporting us in not forgetting them. It‘s easy when you do yet another task, to think you‘ve completed a component because you‘ve done it so many times before. Yet this can lead to errors. So having an external framework is useful. That‘s part of the rationale behind the Serious eLearning Manifesto!

I had originally been thinking about templates, and that‘s another way. And here, I‘m not talking about tarted-up quiz show templates. Instead, I mean a tool that leaves stubs for the important things that should be included. In examples, for instance, you could leave a placeholder for referencing the model, and for the underlying thinking. Really, these are checklists in another format.  All in all, these are ways that you can  Support Thyself!

What you don‘t want to do is make it too constraining. You want to create a minimum floor of quality, without enforcing a ceiling. At least other than the ones your own schedule and budget will import. But you want to be creative while also maintaining effectiveness.

And you can do this in your authoring tool. Just as you may have a template you reuse to maintain look and feel, you can have placeholders for the elements. You can also provide guidance for the elements, in a variety of ways.

There are lots of forms of performance support. And, just as we should be using them to assist our performers (even doing backwards design to design the tools first then any learning), we should be using them to overcome our own cognitive limitations. Our cognitive architecture is amazing, but it‘s prone to all sorts of limitations (there‘s no perfect answer). We can suffer from functional fixedness, set effects, confirmation bias, and more.  

I‘ll admit that I created an ID checklist. The only problem was it had 178 elements, which might be unwieldy (though it did go through the whole process). But you should make sure that whatever tools you do have cover the necessary elements you need. I did create a more reasonable one to accompany my ‘Make it Meaningful‘ initiative (coming soon to a theater or drive-in near you).  

Our brains have limitations that influence our ability to design. Fortunately, we can use technology as support to minimize the impact of those limitations and maximize the contributions of our outcomes. And we should. Thus, my encouragement for IDs to Support Thyself!

Separate content from description

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Once again facing folks who aren’t using styles, I was triggered to think more deeply about the underlying principle. That is, to separate content from description. It’s a step forward in what we can do with systems to bring about a more powerful human-aligned system.

And, as always, here’s the text, in case you (like me) prefer to read ;).

I‘ve ranted before about styles, but I want to make a slightly different pitch today. It‘s not just about styles, it‘s about the thinking behind it. The point is to separate content from description.

So, the point about styles is that they‘re a definition of formatting. You have elements of documents like headings at various levels, and body text, and special paragraphs like quotes, and so on. Then you have features, like font size, bolding and italics, color, etc. And what you see, too often, is people hand-formatting documents, choosing to do headers by increasing the font size, bolding, etc. And, importantly, having to go through and change them all manually if there‘s a desire for a change in look.  

The point of styles is instead merely to say this is a heading 1, this is a figure, this is a caption, and so on. Then, you separately say: heading 1s will be font size 16 bold and left-justified. Figures and captions will be centered, in font size 12. And so on. Then, should someone want to change how the document‘s formatted, you just change the definition of heading 1, and all the heading ones change.  

It goes further. You can define that all heading ones have a page break before (e.g. a new chapter in a book). And you can define new styles, like for a callout box (e.g. colored background), etc. You can have different heading ones for a book than for a white paper. And some styles can be based on others. So your headings can use the same font as your body text, and if you want it all to change, you change the source and the rest will change.  

Which is wonderful for writing, but the concept behind this is what‘s really important to get your head around. That is, separating out role from description. That‘s what‘s led me to be keen on content systems. The notion of pulling up content by description instead of hardwiring together content into an experience is the dream.  

It‘s all about beginning to use semantics, that is the meaning of things, as a manipulable tool. Many years ago, I led a project creating an adaptive learning system. We were going to have content objects defined by topic, and learning role, and tagged in terms of media, difficulty, and more. So you could   say: “pull a video on an example of diversity set in a sales office”. Our goal, with a suite of rules about what when to move up or back in difficulty, was to specify what learning content the learner should see next, etc.  

This is how adaptive platforms work. When Amazon or Netflix make a recommendation for you, there‘s not someone watching your behavior, instead it‘s a set of rules matching your particular actions to content recommendations. If you‘ve ordered a lot of British mysteries, and you haven‘t seen a particular series that lots of other people like, it‘ll be likely to be offered to you.  

This is the opportunity of the future. We can start doing this with learning (and coaching)!   We can start pulling together your learning goals, job role, current progress, current location (in time and space), etc, and offer you particular things that are appropriate for you. And, like our learning system, it might be recommendations of content, but you can choose others, or ignore, or…As Wayne Hodgins used to say, present the ‘right stuff‘: the right content to the right person at the right time in the right way….

The point being, just like styles, if we stop hardwiring things together, hand-formatting learning experiences, we can start offering personalized and even adaptive learning. Yes, there are technical backend issues, and more rigor in development, but this is the direction we can, and should, go. At least, that‘s my proposal, what say you?

Personalized and adaptive learning

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For reasons that are unclear even to me, I was thinking about personalized versus adaptive learning. They’re similar in some ways, but also different. And a way to distinguish them occurred to me. It’s kinda simplistic, but I think it may help to differentiate personalized and adaptive learning.

As background, I led a project to build an intelligently adaptive learning platform. We were going to profile learners, but then also track their ongoing behavior. And, on this basis, we’d serve up something appropriate for learner X versus learner Y. (We’d actually recommend something, and they could make other choices.)

It was quite the research endeavor, actually, as the CEO had been inspired by Guilford’s learning model. I dug into that and all the learning styles literature, and cognitive factor analysis, and content models around learning objectives, and revisited my interest in intelligent tutoring, and more. I was able to hire a stellar team, and create an approach that was scientifically scrutable (e.g. no learning ‘styles’ :). We got it up and running before, well, 2001 happened and the Internet bubble burst and…

In some sense, the system was really both, in the way I’m thinking about it. I’ve seen different definitions, and one has adaptive as a subset of personalized, but I’m going a different way. I think of personalized as pre-planned alternatives for different groups, whereas adaptive reacts to the learner’s behavior.

Our use of initial profiling, if we only used that, would be personalized. The ongoing adaptation is what made it adaptive. We had rules that would prioritize preferences, but we’d also use behavior to update the learner model. It’s something they’re doing now, but we had it a couple of decades ago.

So, my simple way of thinking about personalized versus adaptive is that personalized is based upon who you are: your role, largely. We’d swap out examples on marketing for people selling services versus those selling products, for instance. Or if we’re talking negotiation, a vendor might get a different model than a lawyer.

Adaptive, on the other hand, is based upon what you  do. So, for instance, if you did poorly on the last problem, we might not give you a more difficult one, but give you another at the same level. Twice in a row doing badly, we might bring you another example, or even revisit the concept. This is what intelligent tutoring systems do, they just tend to require a rigorous model of expertise.

Of course, you could get more complicated. Personalization might have a more and less supportive path, depending on your anxiety and confidence. Similarly, adaptive might throw in an encouraging remark while showing some remedial materials.

At any rate, that’s how I differentiate personalized and adaptive learning. Personalized is pre-set based upon some determined differences that suggest different learning paths. Adaptive calculates on the fly and changes what the learner sees next.  How do you see it?

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.

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.

 

Experimenting with conference design

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As part of coping in this time of upheaval, I’m trying different things. Which isn’t new, but there seem to be more innovations to tap into. In addition to teaching a course on mobile learning, I’m one of the speakers at a new online event. And, what’s nice, is that they’re experimenting with conference design, not just moving straight online.

To be fair, the Learning Guild has had a continual practice of trying different things at their conferences, and it’s been good. And, so too, was the most recent TK by ATD.   But this is different. Two of my colleagues organized it as a response to our ‘new normal’, Will Thalheimer and Matt Richter. And their stated goal is changing the way we conference.

The key, of course, is to leverage what’s different, and possible, online. It’s running from June 22 – July 31. That’s not a typo, it’s all of July and the tail end of June. That’s a long time!   They’ve recruited a suite of experts from around the world (they’re really trying to do this across boundaries include time and geography). And, to let you know, I’m one (so take my comments with the appropriate caveats ;).

They’re also tossing out traditional ideas and open to new ones. Speakers are expected to build an experience that’s spread out over the time. Yet also designed so that you can come in late, or early, and drill into what you want when you want. They’re also planing on having synchronous events – debates, panels, socializing – again using technology.

Note that it’s not free. There are some free conferences being put on, mostly webinars. And those are good. This is different. It’s deeper. It’s a stab at looking afresh. And I’m not sure it could even have come from any existing framework.

And, we won’t know if it all will work. We’re designing this in the time between now and launch. There’re bound to be hiccups. Which, of course, means there’re bound to be learnings. I know I want to talk about Learning Science 101. And something else. Lots I could (I welcome suggestions). I’m inclined to think it might be Emotion and Learning. But it could also be LXD. (There are all linked, of course.)

But it’s a high quality group (er, mostly…they did let me in). AND, importantly, it’s focused on evidence-based content. There may be sponsors, or even an exhibit hall, but every presenter is honor-bound not to push anything that’s not legit. Most importantly, there’s enough quality that overall it’s bound to be worth it.

I’m excited, frankly. I have to come up with some different ideas. And I like that. I’m glad that they’re experimenting with conference design. We all win, regardless! It’s part of learning, challenging yourself. So, do yourself a favor. Check it out. It may not be for you, but keep an open mind!