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Post popularity?

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My colleague, Will Thalheimer, asked what posts were most popular (if you blog, you can participate too).  For complicated reasons, I don’t have Google Analytics running.  However, I found I have a WordPress plugin called Page Views. It helpfully can list my posts by number of guest views.  I was surprised by the winner (and less so by the runner up). So it makes me wonder what leads to post popularity.

The winner was a post titled New Curricula?  In it, I quote a message from a discussion that called for meta-cognitive and leadership skills, and briefly made the case to support the idea.  I certainly don’t think it was one of my most eloquent calls for this. Though, of course, I do believe in it.  So why?  I have to admit I’m inclined to believe that folks, searching on the term, came to this post rather than it was so important on it’s own merits.

Which isn’t the case with the post that had the second most views.  This one, titled Stop creating, selling, and buying garbage!, was a rant about our industry. And this one, I believe, was popular because it could be viewed as controversial, or at least, a strong opinion.  I was trying to explain why we have so much bad elearning (c.f. the Serious eLearning Manifesto), and talking about various stakeholders and their hand in perpetuating the sorry state of affairs.

Interestingly, I won an award last year for my post on AR (yes, I was on the committee, but we didn’t review our own).  And, I was somewhat flummoxed on that one too. Not that there weren’t good thoughts in it, but it was pretty simple in the mechanism: I (digitally) drew on some photos!  Yet clearly that made something concrete that folks had wondered about.

Of course, I think there’s also some luck or fate in it as well. Certainly, the posts I think are most interesting aren’t the ones others perceive.  But then, I’m biased. And perhaps some are used in a class so you get a number of people pointed to it or something. I really have no way to know.  I note that the posts here at Learnlets are more unformed thoughts, and my attempts at more definitive thoughts appear at the Litmos blog and now at my Quinnsights columns at Learning Solutions.

I’ll be interested in Will’s results (regardless of whether my data makes it in, because without analytics I couldn’t answer some of his questions).  And, of course, I welcome any thoughts you have about what makes a post popular (beyond SEO :), and/or what you’d like to read!

Revisiting personal learning

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A number of years ago, I tried a stab at an innovation process. And I was reminded of it thinking about personal learning, and looked at it again. And it doesn’t seem to aged well. So I thought I’d revisit the model, and see what emerged. So here’s a mindmap of personal learning, and the associated thinking.

The earlier 5R model was based on Harold Jarche’s Seek-Sense-Share model, a deep model that has many rich aspects. I had reservations about the labels, and I think it’s sparse at either end.  (And, I worked to hard to try to keep it to ‘R’s, and Reify just doesn’t work for me. ;)

Personal learning

In this new approach, I have a richer representation at either end. My notion of ‘seek’ (yes, I’m still using Harold’s framework, more at the end) has three different aspects. First is ‘info flows’. This is setting up the streams you will monitor. They’re filters on the overwhelming overload of info available. They’re your antenna for resonating with interesting new bits. You can also search for information, using DuckDuckGo or Google, or going straight to Wikipedia or other appropriate information resources you know. And, of course, you can ask, using your network, or Quora, or in any social media platform like LinkedIn or Facebook.  And there’re are different details in each.

To make sense of the information, you can do either or both of representing your understanding and experimenting. Representing is a valuable way to process what you’re hearing, to make it concrete. Experimenting is putting it to the test. And you naturally do both; for instance read a web page telling you how to do something that’s new, and you put it into practice and see if it works. Both require reflection, but getting concrete in trying it out or rendering it is valuable. Again, representing and experimenting break down into further details.

What you learn can (and often should) be shared. At whatever stage you’re at, there’s probably someone who would benefit from what you’ve learned.  You can post it publicly (like this blog), or circulate it to a well-selected set of individuals (and that can range from one other person to a small group or some channel that’s limited).  Or you can merely have it in readiness so that if someone asks, you can point them to your thoughts. Which is different than pointing them to some other resource, which is useful, but not necessarily learning. The point is to have others providing feedback on where you’re at.

I looked at Harold’s model more deeply after I did this exercise (a meta-learning on it’s own; take your own stab and then see what others have done).  I realize mine is done on sort of a first-principles basis from a cognitive perspective, while his is richer, being grounded in others’ frameworks. Harold’s is also more tested, having been used extensively in his well-regarded workshop.

I note that part of the meta-learning here is the ongoing monitoring of your own processes (the starred grey clouds). This is a key part of Harold’s workshop, by the way. Looking at your processes and evaluating them. An early exercise where you evaluate your own network systematically, for instance, struck me as really insightful. I’m grateful he was willing to share his materials with me.

So, this has been my sensing and sharing, so I hope you’ll take the opportunity to provide feedback!  What am I missing?

 

 

Are Decisions the Key?

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A number of years ago, now, Brenda Sugrue posited that Bloom’s Taxonomy was wrong. And, she proposed a simpler framework. I’ve never been a fan of Bloom’s; folks have trouble applying it systematically (reliably discriminating between various levels). And, while it pushed for higher levels, it left people off the hook if they decided a lower level would do it. Sugrue first proposed a simpler taxonomy, and also an alternative that was just performance. In her later version, she’s aligned the former to the work of the Science of Learning Center’s KLI (knowledge-learning-instruction) framework. But I want to go back to her ‘pure performance’ model, and make a the case that decisions are key, that they are necessary but also sufficient.

So her lates model discriminates between concept, process, fact, principle, etc.  And, I would agree, there are likely different pedagogies applied to each.  Is that a basis enough?  Let me suggest a different approach, because I don’t see how they differ in one meaningful way. For each, you need to take some action, whether it’s to:

  • classify as a fact (is it a this or a that)
  • perform the steps (which action to take now)
  • trouble shoot the process (what experiment now)
  • predict an outcome (what will happen)

Note, however, that for each, there’s an associated decision. And that, to me, is core.  Now, I’m not claiming that they all require the same approach.  For instance, to help people deal with ambiguous decisions, I suggested a collaborative approach to discuss the parameters and unpack the thinking. To teach trouble-shooting, I would give some practice making conceptual decisions about the systems that could cause the observed symptoms. In internal combustion engines (read: cars), if it’s not running, is the air/fuel system or the electricity? How could you narrow that down?  In a diesel, you could eliminate the electrical ;).

Van Merriënboer, in his Four Component Instructional Design, talks about the knowledge you need and the complex decisions you apply that to. I agree, and so it’s not just about decisions. However, even the knowledge needs to be applied to stick.  To test that learners have acquired the underpinning knowledge, you can hav them exercising the models in decisions.

Ok, so you might want to short-circuit the mapping from decision to practice. I think a good heuristic (ala Cathy Moore’s Action Mapping) is just to have them do what they need to do, and give them the necessary information. However, if you want to create a ‘cheat sheet’ to accelerate performance and success, with learning goals and associated pedagogies, I won’t quibble.

Now, you can’t provide all the situations, you you need to choose the right ones that will help facilitate abstraction and transfer. You may need to also ensure that they know the requisite information, so you may need to determine that, but I think exercising the models in simpler situations helps develop them more than just a presentation.

I’m suggesting that focusing radically on decisions is the best way to work with SMEs, and is the best guide for designing practice (e.g. put learners in situations to make decisions). Everything else revolves around that. Now, are these categories reliable types of decisions?  Will ponder. Your thoughts?

Transparency isn’t enough

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wet foggy windowOf late, there has been a number of articles talking about thinking and mental models (e.g. this one). One of the outcomes is that we have a lot of stories about how the world works.  Some of them are accurate. Others, not. And pondering this when I should’ve been sleeping, I realized that there was a likelihood that our misinterpretations could cause problems. It made me think that maybe transparency isn’t enough. What does that mean?

We build models, period. We create explanations about how the world works. And they may not be right.  If we aren’t given good ones up front, it’s likely. It’s also the case that they seem to come from previous models we’ve seen. (And diagrams. ;)

Now, it’s easy to misattribute an outcome to the wrong model if we don’t have better explanations. And this comes into play when we’re trying to figure out what has happened, or why something happened. This includes decisions made by others that may affect us, or even just lead to outcomes such as product designs, policies, or more.

Where I’m going is this: if we don’t see the thinking that explains how we got there, not just the process followed, we can infer wrongly about why it happened. And this is important in the ‘show your work’ sense.

I’m a fan of transparency. I like it when politics and other decisions are scrutable; we can see who’s making the decision, what influences they’ve had, what steps they took to get there. That’s not enough, however. Particularly when you disagree or have a problem. Take LinkedIn, for example; when I connect to someone using the app on the qPad, I can then send them a message, but when I do it through the web interface on my computer, it wants to use one of those precious ‘InMail’s.  It’s inconsistent (read: frustrating). Is there a rationale?

So I’m going to suggest that just transparency is necessary, but not sufficient. You can’t just show your work, you need to show your thinking. You need to see the rationale!  Two reasons: you can learn more when you see the associated cogitation, and you can provide better feedback as well.  In short, we want to see why they believe this is the right solution. Otherwise, we could question their decision because we misattribute the reasoning.

Transparency is great, but if you can’t see the thinking behind it, you can make wrong inferences.  It’s better if you can see the thinking and the result. Is this transparent enough on both?

Realities: Why AR over VR

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In the past, I’ve alluded to why I like Augmented Reality (AR) over Virtual Reality. And in a conversation this past week, I talked about realities a bit more, and I thought I’d share. Don’t get me wrong, I like VR alot, but I think AR has the bigger potential impact.  You may or may not agree, but here’s my thinking.

In VR, you create a completely artificial context (maybe mimicking a real one).  And you can explore or act on these worlds. And the immersiveness has demonstrably improved outcomes over a non-immersive experience.  Put to uses for learning, where the affordances are leveraged appropriately, they can support deep practice. That is, you can minimize transfer to the real world, particularly where 3D is natural. For situations where the costs of failure are high (e.g. lives), this is the best practice before mentored live performance. And, we can do it for scales that are hard to do in flat screens: navigating molecules or microchips at one end, or large physical plants or astronomical scales at the other. And, of course, they can be completely fantastic, as well.

AR, on the other hand, layers additional information on top of our existing reality. Whether with special glasses, or just through our mobile devices, we can elaborate on top of our visual and auditory world.  The context exists, so it’s a matter of extrapolating on it, rather than creating it whole. On the other hand, recognizing and aligning with existing context is hard.  Yet, being able to make the invisible visible where you already are, and presumably are for a reason that makes it intrinsically motivating, strikes me as a big win.

First, I think that the learning outcomes from VR are great, and I don’t mean to diminish them. However, I wonder how general they are, versus being specific to inherently spatial, and potentially social, learning.  Instead, I think there’s a longer term value proposition for AR. There’s less physical overhead in having your world annotated versus having to enter another one. While I’m not sure which will end up having greater technical overhead, the ability to add information to a setting to make it a learning one strikes me as a more generalizable capability.  And I could be wrong.

Another aspect is of interest to me, too. So my colleague was talking about mixed reality, and I honestly wondered what that was. His definition sounded like alternate reality, as in alternate reality games. And that, to me, is also a potentially powerful learning opportunity. You can create a separate, fake but appearing real, set of experiences that are bound by story and consequences of action that can facilitate learning. We did it once with a sales training game that intruded into your world with email and voicemail. Or other situations where you have situations and consequences that intrude into your world and require decisions and actions. They don’t have real consequences, but they do impact the outcomes. And these could be learning experiences too.

At core, to me, it’s about providing either deep practice or information at the ‘teachable moment’. Both are doable and valuable. Maybe it’s my own curiosity that wants to have information on tap, and that’s increasingly possible. Of course, I love a good experience, too. Maybe what’s really driving me is that if we facilitate meta-learning so people are good self-learners, having an annotated world will spark more ubiquitous learning. Regardless, both realities are good, and are either at the cusp or already doable.  So here’s to real learning!

Question: values?

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So, I’m wrestling with how to characterize useful changes in an organization. I’ve been compiling a list of different tactics (e.g. implement coaching, show-your-work, support curation, etc), and want to map them to the changes you’ll get in the organization. I’ve wanted to tie them to another set of various outcomes: improved participation, innovation, etc. But, while I have the strategies, I’m looking at what breakdowns of outcomes are some minimal useful set. I’ll lay out my very preliminary set of thoughts around the values we’re trying to develop/influence, and I welcome input, pointers, what have you.

My goal, I should be clear, is to try to take specific changes we want in an organization, and have them linked to specific tactics.  And, of course, a new school approach.  That is, tactics that move organizations into directions that create learning organizations.

I start with the three elements Dan Pink talks about in his book Drive.  In it, he lists three core motivators of employees: Purpose, Autonomy, and Mastery (this is my order, not his).  Purpose is why what you’re doing matters.  What does this do for the org, and that what the org is doing also matters. Then, autonomy is when you’re given the freedom to pursue your purposes.  Now, you may not be completely capable of that, so there’s support for mastery, to develop the capabilities to succeed. I think these are all great, but are they sufficient in and of themselves? Are these the right things to want to impact?

I’m also a fan of Amy Edmondson’s quadrant model of psychological safety and accountability. Without either, you’re loafing. With just safety, you’re happy. With just accountability, you’re fearful. But if you’ve accountability and safety, you get results.  This draws upon the richer work of Garvin, Gino, and Edmondson on the components of innovation.  That model adds time for reflection, diversity, and openness to new ideas. Is this a better way to think about it?

There’re also personal values (which might be organizational, too).  Barack Obama, in his keynote to ATD 2018, had two very simple ones: be kind, and be useful.  I’ve extended that out one notch, to include three: responsibility (do the right thing, and do something [useful]), integrity (honesty, do what you promise), and compassion (respect, helping, etc [kind]).  Is that a full set? Or is responsibility derivable from integrity? I’ve a collection of a suite of value proposals (five, with entries ranging from 5 – 8 core values).  Can you derive some of the others from the three I have? E.g. does courage come from integrity and responsibility? Does fairness come from compassion and integrity?  I don’t know.

And so, I’m not sure what the right core set is.  Trust has to be in there somehow, but is that derivative from integrity?  And do I frame it from the change we want in the org, or the change in the people?  I’m inclined to the former.  And are they unitary, or can the tactics impact more than one? (Preliminary: more than one.)

Obviously, I’m at an early stage in formulating this.  I can beaver away on it on my own, but I’m happy to hear pointers, thoughts, etc.  Yes, I’m trying to diagram it too, but nothing coherent has yet emerged.  So, once again, this is me ‘thinking out loud’.  Care to do similarly and share?

Complex thinking

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An interesting article I came across brings up an interesting issue: how do we do complex thinking?  Are some people just better at it?  The short answer appears to be ‘no’.  Instead, a couple of tools play a role, and I think it’s an interesting excursion.

The article says that our brains are limited in thinking about complex situations. Yet, experts can do this.  How? The article cites metaphors as the key, grounding our thinking in models that we’ve developed from our experiences. They draw upon George Lakoff’s work on metaphor (a core aspect of my grad school experience) to explain how our understanding advances.  At core, there’s a fundamental requirement that our knowledge builds upon previous knowledge, which ultimately is grounded in our physical activities.

My PhD thesis topic was thinking with analogy, which shares much with this model. The point being that we use familiar frameworks to make inferences in new areas. We map the familiar to the points in the new that match, and then we extrapolate from the familiar to explain things in the new. And using familiar models as explanatory frameworks are essentially the same process as metaphors. Metaphors tend to be more literal, with a shared point, while analogies go further, and share structure. The latter is, I’ll suggest, more useful.

Note that the frameworks are built of conceptually-related causal relationships, e.g. models. Thus, when we want to communicate models, we can detail them, but using metaphors or analogies are short-cuts.  When we want someone to be able to understand, particularly to be able to use the reference as a tool to support doing, we can use them to facilitate comprehension. We want to leverage, as much as possible, pre-existing knowledge. And people aren’t necessarily great at coming up with analogies (research shows), but they’re good at using them.

Another short-cut that the article cites is diagrams.  Here, we’re making visible the relationships, supporting the understanding. Equations can get specific, but conceptual understanding is facilitated by seeing the connections.

The important outcome is that we all have our cognitive limitations to overcome, but we’ve also developed powerful tools to support these limitations. To the extent we understand how these tools support learning, we can use them to help achieve the outcomes we need.  We can do complex thinking, with the right tools. Are you facilitating success by leveraging these tools?

Telling stories

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I’ve talked about examples before, and have long suggested that examples for learning should be in the form of stories. Today I thought a little deeper about how those stories can be told. And I thought of a particular entailment about video. But let me go through the whole story.

So, first, an example needs to do several things.  Foremost, examples show how a concept is used in context to guide performance and achieve a solution. To put it another way, examples illustrate how a model is used to make decisions in a specific situation.  It could be situational coaching, a sales process, how to run a brainstorming session, or the basis of a good presentation. Importantly, examples need to show the underlying thinking behind what aspects of the situation made this model relevant, how it was mapped to the situation, how  it stipulated as actions to take, and what the outcomes were. It can also show mistakes, backtracking, and repair.

So how does this map to media?  There are (at least) three good ways to tell such a story. One, obviously, is prose. It’s easy to show the underlying thinking or discussions as “dialog” (internal or otherwise).  You can even illustrate model with a diagram, and convey context with a picture.  And this works.  However, for variety, you could use more visual treatments.

I’ve argued in the past that graphic novel formats also make sense. They strike a balance between conceptual and contextual, and tell a story nicely. You can use thought bubbles to show the underlying thinking, include diagrams as a separate panel. And you can include necessary context but simplify the unnecessary context.

Finally, there’s video. This can be an animated cartoon (e.g. dynamic graphic novel), a narrated slideshow (think: Ken Burns), or a documentary style.  The question: how do you represent the model? Is it a separate narrated animation, or an overlay on the video (or both)?  I don’t think it matters as long as it’s explicit, and the underlying thinking is shown linking the concept to the context.

The point is that the nuances matter. It’s not just ‘content’; the elements that we provide have specific roles and we need to understand those.

Social Cognition

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In the two preceding posts, I discussed situated and distributed cognition. In this closing post of the series, I want to talk about social cognition. They’re related, and yet each needs explicit consideration. If we don’t know how we think, work, and learn, we can’t optimally support both performance in the moment and continual innovation over time.

The traditional definition of social cognition is how we think about social interactions.  But here I’m emphasizing instead the fact that our thinking isn’t just in our heads or our tools, but also across our partners. That’s partly distributed cognition, but I want to emphasize it.  And this is true for formal and informal learning as well as performing.

There are two ways to think about this. For one, we benefit from formal social interactions as ways to get richer interpretations. It works the same way when we are problem-solving: working together (under constraints) increases the likelihood of the best outcome. As I like to say, the room is smarter than the smartest person in the room if you manage the process right. The implications of this are several.

First, we need to make sure we have the right constraints. When we have people working together, it helps if it’s the right people and the right environment.  We know that diversity helps, as long as there is overlap in values. Similarly, it needs to be psychologically safe to contribute, the environment helps to be open, and there needs to be time for reflection.

There’re also benefits to mentoring and coaching, helping people in the moment. We want to succeed, and we like to be challenged, and we learn when we are, so having scaffolding helps. Developing coaching and mentoring skills is a good investment in the workplace.

There’re also times when we want help, or someone else does and we can help. That is, we need to support serendipitous inquiry. It helps, by the way, to assist people in learning how to ask questions or answer them in useful ways. There also needs to be the channels to accomplish these goals.

Recognize that there are times when the answer can come from the network, not our own efforts. Particularly if things are changing fast, or the situation’s unique or hard to anticipate. In fact, it frees us up to do more if we take advantage of that as often as possible!  It takes nurturing the networks to become a community so that the answer’s likely to be right.

The point being, there are lots of considerations to making the ecosystem sociable as well as effectively distributed and situated.  If you want to optimize the environment, it helps to have the latest understanding of the users of that environment. Hope this makes sense, and in the spirit of social, I welcome your thoughts and comments!

Distributed Cognition

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In my last post, I talked about situated cognition.  A second, and related, cognitive revelation is that thinking is distributed between our heads and the world. That is, the model that it all occurs between the ears doesn’t recognize that we incorporate external representations are part of our processing. Hutchins, in his Cognition in the Wild, documented a variety of ways that our thinking is an artefact of our tools and our models.

So, for example, navigation typically involves maps as well as thinking. Business reasoning is typically accompanied by tools like a spreadsheet. We use diagrams, tables, graphs, charts, and more to help us understand situations better. And we are unlikely to be able to do things like long division without paper and pencil or a calculator. This means that putting everything in the head isn’t necessary. And this is just what we  should be doing!  Designing for the right distribution of tasks between world and mind(s) is the optimal solution.

We know that it’s difficult to get things in the head (how hard is it to learn, say, to drive), and therefore undesirable anyway.  It’s about designing solutions that put into the world what can be in the world, and then putting into the head what has to be in the head. This includes performance support in a variety of ways. It also should address what we consider to be worth training.

When we want to optimize performance, we should recognize that we need a bigger picture. We need to consider the person & tools, or people & tools, as a whole entity when it comes to achieving the end goal.  This is also true for learning. Our reflective representations are part of our thinking process. So, too, our collaborative representations.

We are better thinkers and learners when we consciously consider tools, and their availability in the ecosystem. In fact, our ecosystem is the tools and people we have ‘to hand’, accessible in or from the workflow. And elsewhere, in our times for reflection, and discussion. So, have you optimized your, and your organization’s thinking and learning toolset?