Learnlets

L&D and working out loud #wolweek

18 November 2014 by Clark 1 Comment

This week is Working Out Loud week, and I can’t but come out in support of a principle that I think is going to be key to organizational success. And, I think, L&D has a key role to play.

The benefits from working out loud are many. Personally, documenting what you’re doing serves as a reminder to yourself and awareness for others. The real power comes, however, from taking that next level: documenting not just what you’re doing, but why. This helps you in reflecting on your own work, and being clear in your thinking. Moreover, sharing your thinking gives you a second benefit in getting others’ input which can really improve the outcome.

In addition, it gives others a couple of benefits. They get to know what you’re up to, so it’s easier to align, but if your thinking is any good, it gives them the chance to learn from how you think.

So what is the role of L&D here? I’ll suggest there are two major roles: facilitating the skills and enabling the culture.

First, don’t assume folks know what working out loud means. And even if they do, they may not be good at it in terms of knowing how to indicate the underlying thinking. And they likely will want feedback and encouragement. First, L&D needs to model it, practicing what they preach. They need to make sure the tools are easily available and awareness is shared. Execs need to be shown the benefit and encouraged to model the behavior too. And L&D will have to trumpet the benefits, accomplishments, and encourage the behavior.

None of this is really likely to succeed if you don’t have a supportive culture. In a Miranda organization, no one is going to share. Instead, you need the elements of a learning organization: the environment has to value diversity, be open to new ideas, provide time for reflection, and most of all be safe. And L&D has to understand the benefits and continue to promote them, identify problems, and work to resolve them.

Note that this is not something you manage or control. The attitude here has to be one of nourishing aka (seed, feed, and weed). You may track it, and you want to be looking for things to support or behaviors to improve, but the goal is to develop a vibrant community of sharing, not squelching anything that violates the hierarchy.

Working out loud benefits the individual and the organization in a healthy environment. Getting the environment right, and facilitating the practice, are valuable contributions, and ones that L&D can, and should, contribute to.

#itashare

Learning Problem-solving

11 November 2014 by Clark Leave a Comment

While I loved his presentation, his advocacy for science, and his style, I had a problem with one thing Neil deGrasse Tyson said during his talk. Now, he’s working on getting deeper into learning, but this wasn’t off the cuff, this was his presentation (and he says he doesn’t say things publicly until he’s ready). So while it may be that he skipped the details, I can’t. (He’s an astrophysicist, I’m the cognitive engineer ;)

His statement, as I recall and mapped, said that math wires brains to solve problems. And yes, with two caveats. There’s an old canard that they used to teach Latin because it taught you how to think, and it actually didn’t work that way. The ability to learn Latin taught you Latin, but not how to think or learn, unless something else happened. Having Latin isn’t a bad thing, but it’s not obviously a part of a modern curriculum.

Similarly, doing math problems isn’t necessarily going to teach you how to do more general problem-solving. Particularly doing the type of abstract math problems that are the basis of No Child Left Untested, er Behind. What you’ll learn is how to do abstract math problems, which isn’t part of most job descriptions these days. Now, if you want to learn to solve meaningful math problems, you have to be given meaningful math problems, as the late David Jonassen told us. And the feedback has to include the problem-solving process, not just the math!

Moreover, if you want to generalize to other problem-solving, like science or engineering, you need explicit scaffolding to reflect on the process and the generality across domains. So you need some problem-solving in other domains to abstract and generalize across. Otherwise, you’ll get good at solving real world math problems, which is necessary but not sufficient. I remember my child’s 2nd grade teacher who was talking about the process they emphasized for writing – draft, get feedback, review, refine – and I pointed out that was good for other domains as well: math, drawing, etc. I saw the light go on. And that’s the point, generalizing is valuable in learning, and facilitating that generalization is valuable in teaching.

I laud the efforts to help folks understand why math and science are important, but you can’t let people go away thinking that doing abstract math problems is a valuable activity. Let’s get the details right, and really accelerate our outcomes.

Taking note

6 November 2014 by Clark Leave a Comment

A colleague pointed me to this article that posited the benefits of digital note-taking. While I agree, I want to take it further. There are some non-0bvious factors in note taking.

As the article points out, there are numerous benefits possible by taking notes digitally. They can be saved and reviewed, have text and/or sketches and/or images (even video too), be shared, revised, elaborated with audio both to add to notes and to read back the prose, and more. Auto-correct is also valuable. And I absolutely believe all this is valuable. But there’s more.

One thing the article touched on is the value of structure. Whether outlining, where indents capture relationships, or networks similarly, capturing that structure means valuable processing by the note-taker. Interestingly, graphical frameworks can support cycles or cross references in the structure better than outlines can (I once was called out that there was no additional value to mindmaps over outlines, and this is one area where they are superior).

However, as the article noted, research has shown that taking verbatim notes doesn’t help. You have to actively reprocess the information, extracting structure through outlines or networks, and paraphrasing what you hear instead of parroting it. This is the real value of note taking. You need to be actively engaged.

Note-taking also helps keep that engagement. The mindmaps that I frequently post started as a way for me to listen better. My brain can be somewhat lateral (an understatement; a benefit for Quinnovating, but a problem for listening to presentations), and if someone says something interesting, by the time I’ve explored the thought and returned, I’ve lost the plot. Mindmapping was a way to occupy enough extra cognitive overhead to keep my mind from sparking off. It just so happens that when I posted one, it drew significant interest (read: hits), and so I’ve continued it for me, the audience, and the events.

Interestingly, the benefit of the note taking can persist even if the notes aren’t reviewed; the act of note-taking with the extra processing in paraphrasing is valuable in itself. I once asked an audience how many took notes, and many hands went up. I then asked how many read the notes afterwards, and the result was significantly less. Yet that’s not a bad thing!

So, take notes that reprocess the information presented. Then, review them if useful. But give yourself the benefit of the processing, if nothing else.

#DevLearn 14 Reflections

5 November 2014 by Clark 1 Comment

This past week I was at the always great DevLearn conference, the biggest and arguably best yet. There were some hiccups in my attendance, as several blocks of time were taken up with various commitments both work and personal, so for instance I didn’t really get a chance to peruse the expo at all. Yet I attended keynotes and sessions, as well as presenting, and hobnobbed with folks both familiar and new.

The keynotes were arguably even better than before, and a high bar had already been set.

Neil deGrasse Tyson was eloquent and passionate about the need for science and the lack of match between school and life. I had a quibble about his statement that doing math teaches problem-solving, as it takes the right type of problems (and Common Core is a step in the right direction) and it takes explicit scaffolding. Still, his message was powerful and well-communicated. He also made an unexpected connection between Women’s Liberation and the decline of school quality that I hadn’t considered.

Beau Lotto also spoke, linking how our past experience alters our perception to necessary changes in learning. While I was familiar with the beginning point of perception (a fundamental part of cognitive science, my doctoral field), he took it in very interesting and useful direction in an engaging and inspiring way. His take-home message: teach not how to see but how to look, was succinct and apt.

Finally, Belinda Parmar took on the challenge of women in technology, and documented how small changes can make a big difference. Given the madness of #gamergate, the discussion was a useful reminder of inequity in many fields and for many. She left lots of time to have a meaningful discussion about the issues, a nice touch.

Owing to the commitments both personal and speaking, I didn’t get to see many sessions. I had the usual situation of good ones, and a not-so-good one (though I admit my criteria is kind of high). I like that the Guild balances known speakers and topics with taking some chances on both. I also note that most of the known speakers are those folks I respect that continue to think ahead and bring new perspectives, even if in a track representing their work. As a consequence, the overall quality is always very high.

And the associated events continue to improve. The DemoFest was almost too big this year, so many examples that it’s hard to start looking at them as you want to be fair and see all but it’s just too monumental. Of course, the Guild had a guide that grouped them, so you could drill down into the ones you wanted to see. The expo reception was a success as well, and the various snack breaks suited the opportunity to mingle. I kept missing the ice cream, but perhaps that’s for the best.

I was pleased to have the biggest turnout yet for a workshop, and take the interest in elearning strategy as an indicator that the revolution is taking hold. The attendees were faced with the breadth of things to consider across advanced ID, performance support, eCommunity, backend integration, decoupled delivery, and then were led through the process of identifying elements and steps in the strategy. The informal feedback was that, while daunted by the scope, they were excited by the potential and recognizing the need to begin. The fact that the Guild is holding the Learning Ecosystem conference and their release of a new and quite good white paper by Marc Rosenberg and Steve Foreman are further evidence that awareness is growing. Marc and Steve carve up the world a little differently than I do, but we say similar things about what’s important.

I am also pleased that Mobile interest continues to grow, as evidenced by the large audience at our mobile panel, where I was joined by other mLearnCon advisory board members Robert Gadd, Sarah Gilbert, and Chad Udell. They provide nicely differing viewpoints, with Sarah representing the irreverent designer, Robert the pragmatic systems perspective, and Chad the advanced technology view, to complement my more conceptual approach. We largely agree, but represent different ways of communicating and thinking about the topic. (Sarah and I will be joined by Nick Floro for ATD’s mLearnNow event in New Orleans next week).

I also talked about trying to change the pedagogy of elearning in the Wadhwani Foundation, the approach we’re taking and the challenges we face. The goal I’m involved in is job skilling, and consequently there’s a real need and a real opportunity. What I’m fighting for is to make meaningful practice as a way to achieve real outcomes. We have some positive steps and some missteps, but I think we have the chance to have a real impact. It’s a work in progress, and fingers crossed.

So what did I learn? The good news is that the audience is getting smarter, wanting more depth in their approaches and breadth in what they address. The bad news appears to be that the view of ‘information dump & knowledge test = learning’ is still all too prevalent. We’re making progress, but too slowly (ok, so perhaps patience isn’t my strong suit ;). If you haven’t, please do check out the Serious eLearning Manifesto to get some guidance about what I’m talking about (with my colleagues Michael Allen, Julie Dirksen, and Will Thalheimer). And now there’s an app for that!

If you want to get your mind around the forefront of learning technology, at least in the organizational space, DevLearn is the place to be.

Neil deGrasse Tyson #DevLearn Keynote Mindmap

29 October 2014 by Clark 1 Comment

Neil deGrasse Tyson opened this year’s DevLearn conference. A clear crowd favorite, folks lined up to get in (despite the huge room). In a engaging, funny, and poignant talk, he made a great case for science and learning.

Cognitive prostheses

28 October 2014 by Clark 2 Comments

While our cognitive architecture has incredible capabilities (how else could we come up with advances such as Mystery Science Theater 3000?), it also has limitations. The same adaptive capabilities that let us cope with information overload in both familiar and new ways also lead to some systematic flaws. And it led me to think about the ways in which we support these limitations, as they have implications for designing solutions for our organizations.

The first limit is at the sensory level. Our mind actually processes pretty much all the visual and auditory sensory data that arrives, but it disappears pretty quickly (within milliseconds) except for what we attend to. Basically, your brain fills in the rest (which leaves open the opportunity to make mistakes). What do we do? We’ve created tools that allow us to capture things accurately: cameras and microphones with audio recording. This allows us to capture the context exactly, not as our memory reconstructs it.

A second limitation is our ‘working’ memory. We can’t hold too much in mind at one time. We ‘chunk’ information together as we learn it, and can then hold more total information at one time. Also, the format of working memory largely is ‘verbal’. Consequently, using tools like diagramming, outlines, or mindmaps add structure to our knowledge and support our ability to work on it.

Another limitation to our working memory is that it doesn’t support complex calculations, with many intermediate steps. Consequently we need ways to deal with this. External representations (as above), such as recording intermediate steps, works, but we can also build tools that offload that process, such as calculators. Wizards, or interactive dialog tools, are another form of a calculator.

Processing information in short term memory can lead to it being retained in long term memory. Here the storage is almost unlimited in time and scope, but it is hard to get in there, and isn’t remembered exactly, but instead by meaning. Consequently, models are a better learning strategy than rote learning. But external sources like the ability to look up or search for information is far better than trying to get it in the head.

Similarly, external support for when we do have to do things by rote is a good idea. So, support for process is useful and the reason why checklists have been a ubiquitous and useful way to get more accurate execution.

In execution, we have a few flaws too. We’re heavily biased to solve new problems in the ways we’ve solved previous problems (even if that’s not the best approach. We’re also likely to use tools in familiar ways and miss new ways to use tools to solve problems. There are ways to prompt lateral thinking at appropriate times, and we can both make access to such support available, and even trigger same if we’ve contextual clues.

We’re also biased to prematurely converge on an answer (intuition) rather than seek to challenge our findings. Access to data and support for capturing and invoking alternative ways of thinking are more likely to prevent such mistakes.

Overall, our use of more formal logical thinking fatigues quickly. Scaffolding help like the above decreases the likelihood of a mistake and increases the likelihood of an optimal outcome.

When you look at performance gaps, you should look to such approaches first, and look to putting information in the head last. This more closely aligns our support efforts with how our brains really think, work, and learn. This isn’t a complete list, I’m sure, but it’s a useful beginning.

Extending Mobile Models

21 October 2014 by Clark Leave a Comment

In preparation for a presentation, I was reviewing my mobile models. You may recall I started with my 4C‘s model (Content, Compute, Communicate, & Capture), and have mapped that further onto Augmenting Formal, Performance Support, Social, & Contextual. I’ve refined it as well, separating out contextual and social as different ways of looking at formal and performance support. And, of course, I’ve elaborated it again, and wonder whether you think this more detailed conceptualization makes sense.

self and social mlearning content So, my starting point was realizing that it wasn’t just content. That is, there’s a difference between compute and content where the interactivity was an important part of the 4C’s, so that the characteristics in the content box weren’t discriminated enough. So the new two initial sections are mlearning content and mlearning compute, by self or social. So, we can be getting things for an individual, or it can be something that’s socially generated or socially enabled.

mLearningCompute The point is that content is prepared media, whether text, audio, or video. It can be delivered or accessed as needed. Compute, interactive capability, is harder, but potentially more valuable. Here, an individual might actively practice, have mixed initiative dialogs, or even work with others or tools to develop an outcome or update some existing shared resources.

Things get more complex when we go beyond these elements. So I had capture as one thing, and I’m beginning to think it’s two: one is the capture of current context and keeping sharing that for various purposes, and the other is the system using that context to do something unique.

To be clear here, capture is where you use the text insertion, microphone, or camera to catch unique contextual data (or user input). It could also be other such data, such as a location, time, barometric pressure, temperature, or more. This data, then, is available to review, reflect on, or more. It can be combinations, of course, e.g. a picture at this time and this location.

Now, if the system uses this information to do something different than under other circumstances, we’re contextualizing what we do. Whether it’s because of when you are, providing specific information, or where you are, using location characteristics, this is likely to be the most valuable opportunity. Here I’m thinking alternate reality games or augmented reality (whether it’s voiceover, visual overlays, what have you).

And I think this is device independent, e.g. it could apply to watches or glasses or..as well as phones and tablets. It means my 4 C’s become: content, compute, capture, and contextualize. To ponder.

So, this is a more nuanced look at the mobile opportunities, and certainly more complex as well. Does the greater detail provide greater benefit?

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