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Clark Quinn’s Learnings about Learning

Theory or Research?

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There’s a lot of call for evidence-based methods (as mentioned yesterday): L&D, learning design, and more. And this is a good thing. But…do you want to be basing your steps on a particular empirical study, or the framework within which that study emerged? Let me make the case for one approach. My answer to theory or research is theory. Here’s why.

Most research experiments are done in the context of a theoretical framework. For instance, the work on worked examples comes from John Sweller’s Cognitive Load theory. Ann Brown & Ann-Marie Palincsar’s experiments on reading were framed within Reciprocal Teaching, etc. Theory generates experiments which refine theory.

The individual experiments illuminate aspects of the broader perspective. Researchers tend to run experiments driven by a theory. The theory leads to a hypothesis, and then that hypothesis is testable. There  are some exploratory studies done, but typically a theoretical explanation is generated to explain the results. That explanation is then subject to further testing.

Some theories are even meta-theories! Collins & Brown’s Cognitive Apprenticeship  (a favorite) is based upon integrating several different theories, including the Reciprocal Teaching, Alan Schoenfeld’s work on examples in math, and the work of Scardemalia & Bereiter on scaffolding writing. And, of course, most theories have to account for others’ results from other frameworks if they’re empirically sound.

The approach I discuss in things like my Learning Experience Design workshops is a synthesis of theories as well. It’s an eclectic mix including the above mentioned, Cognitive Flexibility, Elaboration, ARCS, and more. If I were in a research setting, I’d be conducting experiments on engagement (pushing beyond ARCS) to test my own theories of what makes experiences as engaging and effective. Which, not coincidentally, was the research I was doing when I  was  an academic (and led to  Engaging Learning). (As well as integration of systems for a ubiquitous coaching environment, which generates many related topics.)

While individual results, such as the benefits of relearning, are valuable and easy to point to, it’s the extended body of work on topics that provides for longevity and applicability. Any one study may or may not be directly applicable to your work, but the theoretical implications give you a basis to make decisions even in situations that don’t directly map. There’s the possibility to extend to far, but it’s better than having no guidance at all.

Having theories to hand that complement each other is a principled way to design individual solutions  and design processes. Similarly for strategic work as well (Revolutionize L&D) is a similar integration of diverse elements to make a coherent whole. Knowing, and mastering, the valid and useful theories is a good basis for making organizational learning decisions. And avoiding myths!  Being able to apply them, of course, is also critical ;).

So, while they’re complementary, in the choice between theory or research I’ll point to one having more utility. Here’s to theories and those who develop and advance them!

Working virtually

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Of late, I’ve been involved in two separate initiatives that are distributed, one nationally, one internationally. And, as with some other endeavors, I’ve been using some tools to make this work. And, finally, it really really is. I’m finding it extraordinarily productive to be working virtually.

In both endeavors, there’s trust. One’s with folks I know, which makes it easy. The other’s with folks who have an international reputation for scholarly work, and that generates an initial acceptance. Working together quickly generates that.

Working

The work itself, as with most things,  comes down to communication, collaboration, and cooperation. We’ve got initiatives to plan, draft, review, and execute. And we need to make decisions.

We’re using one social media tool to coordinate. In both cases, we’re using Slack as the primary tool for asynchronous communications. We’re setting up meetings (sometimes with the help of Doodle), asking questions, updating on occurrences, and sharing thoughts.

We’re using different tools for synchronous sessions. In one, we’re using Zoom, Blue Jeans in the other. I like Zoom a bit better because when you open the chat or the list of participants, it expands the window. In Blue Jeans, it covers a bit of the screen. Both, however, handle video streams without a problem.

And, for both, we’re using Google tools to create shared representations. Documents, and occasionally spreadsheets, mostly. I’m experimenting with their draw tools; while they’re not as smooth as OmniGraffle, they’re quite robust. It’s even fun to be working together watching several of us editing a doc at the same time!

There are always the hiccups; sometimes one or another can’t attend a meeting, or we lose track of files, but nothing that doesn’t plague co-located work. One problem that’s unique is those folks who aren’t regular users of one or the other tools. But we’ve enough peer pressure to remedy that. And, of course, these are folks who are in tech…

Reflecting

One key element, I think, is the ‘working out loud’. It’s pretty easy to share, and people do. Thinking is largely out in the open. There’re subcommittees, for instance, that may work on specific issues, and some executive discussions, but  little you  can’t see.

And we’re unconsciously working in, and consciously working on, a desirable learning culture. We’re sharing safely, considering ideas fairly, taking time to reflect, and actively seeking diversity. We experiment, and we do serendipitously review our practices (particularly when we onboard new folks).

Most importantly, this is beginning to not only feel natural, but productive.  This  is the new world of work. Using tools to handle collaboration, coordination, and cooperation (the 3 c’s?).  We’re working, and evolving too!

And, a key learning for me, is that this doesn’t preclude being co-located. Though I wonder if that would actually hurt, since hallway conversations can progress things but there’re no trails. Unless, I suppose, if you commit to immediately capture whatever emerges. That’s a cultural thing.

This working virtually is a direction I think will be productive for organizations going forward. It’s social, it’s augmented, and it’s culturally sound. It’s not to say that I won’t welcome the chance to be co-located with these folks at some point. There might even be hugs between folks who’ve never met before (that happens when you interact in a safe space online). But the important thing is that it works, well. And what else needs to be said, after all?

 

Cognition external

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reading outsideI was thinking a bit about distributed cognition, and recognized that there as a potentially important way to tease that apart. And I’ll talk it out first here, and maybe a diagram will emerge. Or not. The point is to think about how external tools can augment our thinking. Or, really, a way that at least partly, we have cognition external.

The evidence says that our thinking isn’t completely in our head. And I’ve suggested that that makes a good case for performance support. But I realize it goes further in ways I’ve thought about it elsewhere. So I want to pull those together.

The alternative to performance support, a sort of cognitive scaffolding, is to think about representation. Here we’re not necessarily supporting any particular performance, but instead supporting developing thinking. I shared Jane Hart’s diagram yesterday, and I know that it’s a revision of a prior one. And that’s important!

The diagram is capturing her framework, and such externalizations are a way to share; they’re a social as well as artifactual sharing. It’s part of a ‘show your work‘ approach to continuing to think. Of course, it doesn’t have to be social, it can be personal.

So both of these forms of distributed cognition are externalizing our thinking in ways that our minds have trouble comprehending. We can play around with relationships by spatially representing them. We can augment our cognitive gaps both formally through performance support, and informally by supporting externalizing our thinking.  Spreadsheets are another tool to externalize our thinking. So, too, for that matter, is text.

So we can augment our performance, and scaffold our thinking. Both can be social or solitary, but they both qualify as forms of distributed cognition (beyond social). And, importantly, both then should be consciously considered in thinking about revolutionizing L&D. We should be designing for cognition external.  The tools should be there, and the facilitation, to use either when appropriate. So, think distributed, as well as situated, and social. It’s how our brains work, we ought to use that as a guide. You think?

A very insightful framework

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Jane Hart has just come up with something new and, to me, intriguing. Ok, so she’s a colleague from the Internet Time Alliance, and I’ve been a fan of her work for a while, but I think this is particularly good.  If you’ve read here before, you’ll know I love a good model (Harold Jarche’s Seek>Sense>Share comes to mind). So when I parsed her “from training to modern workplace learning”, it resonated in many ways.  So here’s her framework with some comments.

First, some context. If you’ve known my work at all, you know that I’ve been pushing a L&D revolution. And that’s about rethinking training to be about transformative experience design, performance support to be included, and informal learning to be also addressed. That’s  intellectricity! And it’s sometimes hard to tie them together coherently.

Jane’s always had a talent for drilling down into the practicalities in sensible ways. Her books, continually updated, have great specifics about things to do. This is a framework that ties it together nicely.

The thing I like is the way she’s characterized different activities. The categories of Discovery (informal learning), Discourse (social learning), and Doing (experiential learning) provides a nice handle around which to talk about elements, roles, and tasks. And, importantly, prescriptions.  And I really like the ‘meta’ layer, where she suggests skills for each vertical.

I’m not without quibbles, however small. For instance, with her use of microlearning, because of my concerns about the label rather than her specific intention. She told me personally that she means “short daily learning”, and I think that’s great. I just think of that as spaced learning ;). And I might label ‘discovery’ to be ‘develop’, because it’s about the individual’s continual learning. And I’m not sure there’s what I call ‘slow’ innovation there, creating a culture and practices about experimentation and exposure to the ‘adjacent possible’. But it’s hard for one diagram to capture everything, and this does a great job.

I admit that I haven’t parsed all the nuances yet. But as an advocate of diagrams  and frameworks, I think this is truly insightful  and  useful. (And she’s updated it so I’ve grabbed this copy which appears to have lost microlearning.)   I’m sure she, as well as I, welcome your thoughts!

Social Silliness

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It’s that time again. Someone pointed me to a post that touted the benefits of social learning. And I’m a fan!  However, as I perused it, I saw that was a bit of social silliness. So, let me be clear about why.

It starts off mostly on the right foot, saying “playing off of the theory that people learn better when they learn collectively…” I’m a proponent of that theory. There are times when that’s not the most effective nor efficient approach, but there are times when it’s really valuable.

What follows in the article are a series of five tips about applying social learning. And here we go off the rails!  Let’s go through them:

  1. A Facebook Group Or A Forum, or both.  Well, yes, a group is a good idea. But Facebook is  not! Expecting everyone to have to be open to being on Facebook isn’t a good policy. While I’m on Facebook (and no, don’t connect to me there, that’s for personal relationships, not professional ones; go see me on LinkedIn ;), I know folks who aren’t and won’t be. Create your own group in your own tool, so folks know what’s being done with their data!
  2. Leaderboards. What? NOOOO!  That’s so  extrinsic  ;). Seriously, that’s the second most important tip?  Er, not. If you’re not making sure folks are finding intrinsic value in the community, go back and fix it. People (should) come because it’s worth it. Work to make it so. That’s hard, but in the end if you want to build community, start modeling and encouraging sharing, and make it safe.  Don’t do it on points.
  3. Surveys or polls. Ok, let’s put this in context. Yes, getting people to participate and collecting their opinions is good. Is this the third most important tip? No, but no points lost for this suggestion. However, let’s do it right. You can really decrease participation when you’re allowing ‘drive-by’ surveys. Have a policy, be clear, and do it  when it makes sense. This would be a subset of a more general principle about stimulating and leveraging the community, I reckon.
  4. Interactions between the L&D  Team and Employees.  This requires nuance. Not just any interactions. In a sense, L&D should be invisible, the hidden hand that keeps things moving. Facilitating, yes, where someone needs a nudge to contribute, someone else needs a nudge to  not contribute (in that way, or that often, or…), some statement needs some nuance, etc. But ultimately, the community should be interacting with each other, not L&D.
  5. elearning Courses that Require Teamwork. Back to my point above, yes,  sometimes. This is a good idea. And it can build the community skills that will carry over. You want a smooth segue from courses to community. The suggestion included, however, “only that employee can access that particular phase or section” is a lot of extra design. Why not just group assignments with facilitation to participate? It’s not a horrible idea, but not a general one.

Overall, this is nowhere near the first five tips  I would suggest about building community. I agree community’s big, but I’d be pushing:

Start small: get it working somewhere (particularly within L&D), then spread slowly to other groups.

Make it safe: ensure that there’re principles in place about what’s acceptable behavior, and that the relevant leader is sharing. If they don’t, will anyone really believe it’s safe?

Ensure value: make sure that people coming to the community will find reasons to return. To get it to critical mass, you need to nurture it. Start by seeding valuable information over time, and inviting (or incepting) some respected folk to contribute. And the surveys and polls are ways to find out what’s going on and reflect that back.  It takes effort to kick start it, but it’s critical to get people to stay engaged. As part of this:

Enable sharing: the ‘show your work‘ mentality should be encouraged. Get people showing what they’re doing (once it’s safe) enables long term benefits. This will start providing valuable content, and support the organization beginning to learn together.

Persist: success will depend on maintaining the support until the community reaches critical mass. That means a continual effort to make value, surface value until the community is doing this itself.

I’m not saying this is my official list, this is off the top of my head. However, when I look at these two lists, the problem for me is that the top list is tactical, but creating community is really a strategic initiative. Which means, it needs to be treated as such. No social silliness, it needs to be seriously addressed. So, what am I missing?

Quinnovations

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I was talking with my lass, and reminiscing about a few things. And, it occurs to me, that I may not have mentioned them all. Worse, I confess, I’m still somewhat proud of them. So, at the risk of self-aggrandizement, I thought I’d share a few of my Quinnovations. There’s a bigger list here, but this is the ‘greatest hits’ list, with some annotation. (Note, I’ve already discussed the game Quest for Independence, one of my most rewarding works.)

One project was a game based upon my PhD topic. I proposed a series of steps involved in analogical reasoning, and tested them both alone and then after some training. I found some improvement (arguing for the value of meta-learning instruction). During my post-doc, a side project was developing a game that embedded analogical reasoning in a story setting. I created a (non-existent) island, and set the story in the myths of the voodoo culture on it. The goal was a research environment for analogical reasoning; the puzzles in the game required making inferences from the culture. Most players were random, interestingly, at a test, but a couple were systematic.

With a colleague, Anne Forster, we came up with an idea for an online conference to preface a face-to-face event. This was back circa 1996, so there weren’t platforms for such. I secured the programming assistance of a couple of the techs in the office I was working for (Open Net), and we developed the environment. In it, six folks reknown in their area conducted overlapping conversations around their topic. This set up the event, and saw vibrant discussions.

A colleague at an organization I was working for, Access Australia CMC, had come up with the idea of competition for school kids to create websites about a topic. With another colleague, we brainstormed a topic for the first running of the event. In it, we had kids report on innovations in their towns that they could share with other towns (anywhere). I led the design and implementation of the competition: site and announcements, getting it up and running. It ended up generating vibrant participation and winning awards.

Upon my return to the US, I led a team to generate a learning system that developed learners’ understanding of themselves as learners. Ultimately, I conceived of a model whereby we profiled learners as to their learning characteristics (NB:  not learning styles) and adapted learning on that basis. There was a lot to it: a content model, rules for adaptation, machine learning for continuing improvement, and more. We got it up and running, and while it evaporated in 2001 (as did the organization we worked for), it’s legacy served me in several other projects. (And, while they didn’t base it on our system, to my knowledge, it’s roughly the same architecture being seen in Newton.)

Using the concept of that adaptive system, with one of my clients we pitched and won the right to develop an electronic performance support system. It ended up being a context-sensitive help system (which is what an EPSS really is ;).  I created the initial framework which the team executed against (replacing a help system created by the system engineers, not the right team to do it). The design wrote content into a framework that populated the manual (as prescribed by law)  and the help system. The client ended up getting a patent on it (with my name on too ;).

Last one I’ll mention for now, a content system for a publisher. They were going to the next generation of their online tool, and were looking for a framework to: incorporate their existing texts, guide the next generation of texts, and support multiple business models. Again pulling on that content structure experience, I gave them a structured content model that met their needs. The model was supposed to be coupled with a tech platform, and that project collapsed, meaning my model didn’t see the light of day. However, I was pleased to find out subsequently that it had a lasting impact on their subsequent works!

The point being that, in conjunction with clients and partners, I have been consistently generating innovations thru the years. I’m not an academic, tho’ I have been and know the research and theories. Instead, I’m a consultant who comes in early, applies the frameworks to come up with ideas that are both good and unique (I capitalize a lot on models I’ve collected over the years), and gets out quickly when I’m no longer adding value. Clients get an outcome that is uniquely appropriate, innovative, and effective. Ideas they likely wouldn’t have come up with on their own!  If you’d like to Quinnovate, get in touch!

Learning from Experimentation

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At the recent LearnTec conference, I was on a panel with my ITA colleagues, Jane Hart, Harold Jarche, and Charles Jennings. We were talking about how to lift the game of Modern Workplace Learning, and each had staked out a position, from human performance consulting to social/informal. Mine (of course :) was at the far end, innovation.  Jane talked about how you had to walk the walk: working out loud, personal learning, coaching, etc.  It triggered a thought for me about innovating, and that meant experimentation. And it also occurred to me that it led to learning as well, and drove you to find new content. Of course I diagrammed the relationship in a quick sketch. I’ve re-rendered it here to talk about how learning from experimentation is also a critical component of workplace learning.

Increasing experimentation and even more learnings based upon contentThe starting point is experimentation.  I put in ‘now’, because that’s of course when you start. Experimentation means deciding to try new things, but not just  any things.  They should be things that would have a likelihood of improving outcomes if they work. The goal is ‘smart’ experiments, ones that are appropriate for the audience, build upon existing work, and are buttressed by principle. They may or may not be things that have worked elsewhere, but if so, they should have good outcomes (or, more unlikely, didn’t but have a environmentally-sound reason to work for you).

Failure  has to be ok.  Some experiments should not work. In fact, a failure rate above zero is important, perhaps as much as 60%!  If you can’t fail, you’re not really experimenting, and the psychological safety isn’t there along with the accountability.  You learn from failures as well as from successes, so it’s important to expect them. In fact, celebrate the lesson learned, regardless of success!

The reflections from this experimentation take some thought as well. You should have designed the experiments to answer a question, and the experimental design should have been appropriate (an A-B study, or comparing to baseline, or…).  Thus, the lesson extracted from learning from experimentation is quickly discerned. You also need to have time to extract the lesson! The learnings here move the organization forward. Experimentation is the bedrock of a learning organization,  if you consolidate the learnings. One of the key elements of Jane’s point, and others, was that you need to develop this practice of experimentation for your team. Then, when understood and underway, you can start expanding. First with willing (ideally, eager) partners, and then more broadly.

Not wanting to minimize, nor overly emphasize, the role of ‘content’, I put it in as well. The point is that in doing the experimentation, you’re likely to be driven to do some research. It could be papers, articles, blog posts, videos, podcasts, webinars, what have you. Your circumstances and interests and… who knows, maybe even courses!  It includes social interactions as well. The point is that it’s part of the learning.

What’s  not in the diagram, but is important, is sharing the learnings. First, of course, is sharing within the organization. You may have a community of practice or a mailing list that is appropriate.  That builds the culture. After that, there’s beyond the org.  If they’re proprietary, naturally you can’t. However, consider sharing an anonymized version in a local chapter meeting and/or if it’s significant enough or you get good enough feedback, go out to the field. Present at a conference, for instance!

Experimentation is critical to innovation. And innovation takes a learning organization. This includes a culture where mistakes are expected, there’s time for reflection, practices for experimentation are developed, and more.  Yet the benefits to create an agile organization are essential.  Experimentation needs to be part of your toolkit.  So get to it!

 

Locus of learning: community, AI, or org?

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A recent article caused me to think. Always a great thing!  It led to some reflections that I want to share. The article is about a (hypothetical) learning journey, and talks about how learning objects are part of that learning process. My issue is with the locus of the curation of those objects; should it be the organization, an AI, or the community?  I think it’s worth exploring.

The first sentence that stood out for me made a strong statement. “Choice is most productive when it is scaffolded by an organizationally-curated framework.” Curation of resources for quality and relevance is a good thing, but is the organization is the best arbiter? I’ve argued that the community of practice should determine the curriculum to be a member of that community. Similarly, the resources to support progression in the community should come from the community, both within  and outside the organization.

Relatedly, the sentence before this one states “learner choice can be a dangerous thing if left unchecked”.  And this really strikes me as the wrong model.  It’s inherently saying we don’t trust our learners to be good at learning.  I don’t  expect  learners (or SMEs for that matter) to know learning. But then, we shouldn’t leave that to chance. We should be facilitating the development of learning to learn skills explicitly, having L&D model and guide it, and more.  It’s rather an  old school approach to think that the org (through the agency of L&D) needs to control the learning.

A second line that caught my eye was that the protagonist “and his colleagues  create and share additional AI-curated briefings with each other.”  Is that AI curation, or community curation? And note that there’s ‘creation’, not just sharing.  I’m thinking that the human agency is more critical than the AI curation. AI curation has gotten good, but when a community is working, the collective intelligence is better. Or, if we’re talking IA (and we should be), we should explicitly looking to couple AI and community curation.

Another line is also curious.  “However, learning leaders must balance the popularity of informal learning with the formal, centralized needs of the organization. This can be achieved using AI-curated real-time briefings.” Count me skeptical. I believe that if you address the important issues – purpose via meaningful work and autonomy to pursue, communities of practice, and learning to learn skills – you can trust informal learning more than AI or a central view of what learning can and should be.

Most of the article was quite good, even if things like “psychological safety” are being attributed to McKenzie instead of Amy Edmondson.  I like folks looking to the future, and I understand that aligning with the status quo is a good business move. It’s just that when you get disconnects such as these, it’s an opportunity to reflect.  And wondering about the locus of responsibility for learning is a valuable exercise.  Can the locus be the individual and community, not the org or AI? Of course, better yet if we get the synergy between them.  But let’s think seriously about how to empower learners and community, ok?

 

Another Day Another Myth-Ridden Hype Piece

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Some days, it feels like I’m playing whack-a-mole. I got an email blast from an org (need to unsubscribe) that included a link that just reeked of being a myth-ridden piece of hype.  So I clicked, and sure enough!  And, as part of my commitment to showing my thinking, I’m taking it down. I reckon it’s important to take these myths apart, to show the type of thinking we should avoid if not actively attack.  Let me know if you don’t think this is helpful.

The article starts by talking about millennials. That’s a problem right away, as millennials is an arbitrary grouping by birthdate, and therefore is inherently discriminatory. The boundaries are blurry, and most of the differences can be attributed to age, not generation. And that’s a continuum, not a group. As the data shows.  Millennials is a myth.

Ok, so they go on to say: “Changing the approach from adapting to Millennials to leveraging Millennials is the key…”  Ouch!  Maybe it’s just me, but while I like to leverage assets, I think saying that about people seems a bit rude.  Look, people are people!  You work with them, develop them, etc. Leverage them?  That sounds like you’re using them (in the derogatory sense).

They go on to talk about Learning Organizations, which I’m obviously a fan of.  And so the ability to continue to learn is important.  No argument. But why would that be specific to ‘millennials’?  Er…

Here’s another winner: “They natively understand the imperative of change and their clockspeed is already set for the accelerated learning this requires.”  This smacks of the ‘digital native’ myth.  Young people’s wetware isn’t any different than anyone else’s. They may be more comfortable with the technology, but making assumptions such as this undermines the fact that any one individual may not fit the group mean. And it’s demonstrable that their information skills aren’t any better because of their age.

We move on to 3 ways to leverage millennials:

  1. Create Cross-pollination through greater teamwork.  Yeah, this is a good strategy.  FOR EVERYONE. Why attribute it just to millennials?  Making diverse teams is just good strategy, period. Including diversity by age? Sure. By generation?  Hype. You see this  also with the ‘use games for learning’ argument for millennials. No, they’re just better learning designs! (Ok, with the caveat: if done well.)
  2. Establish a Feedback-Driven Culture to Learn and Grow Together. That’s a fabulous idea; we’re finding that moving to a coaching culture with meaningful assignments and quick feedback (not the quarterly or yearly) is valuable. We can correct course earlier, and people feel more enagaged. Again,  for everyone.
  3. Embrace a Trial-and-Error Approach to Learning to Drive Innovation. Ok, now here I think it’s going off the rails. I’m a fan of experimentation, but trial and error can be smart or random. Only one of those two makes sense. And, to be fair, they do argue for good experimentation in terms of rigor in capturing data and sharing lessons learned. It’s valuable, but again, why is this unique to millennials? It’s just a good practice for innovation.

They let us know there are 3 more ways they’ll share in their next post.  You can imagine my anticipation.  Hey, we can read  two  posts with myths, instead of just one.  Happy days!

Yes, do the right things (please), but  for the right reasons. You could be generous and suggest that they’re using millennials as a stealth tactic to sneak in messages about modern workplace learning.  I’m not, as they seem to suggest doing this largely with millennials. This sounds like hype written by a marketing person. And so, while I advocate the policies, I eschew the motivation, and therefore advise you to find better sources for your innovation practices. Let me know if this is helpful (or not ;).

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!