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Wise organizations

10 October 2012 by Clark Leave a Comment

My ITA colleague Jay (always a spark igniter) has been thinking about well-being in organizations, and it activated my thinking on wisdom. My interest in wisdom continues to ferment, slowly but surely, as a personal commitment. My question was what would business wisdom look like, and what would be the benefits?

One preliminary issue is definitional: when I google the term, I mostly see good business practices wrapped up and trumped as business wisdom. That’s not quite what I mean. We’ve seen examples of people doing things that were smart in the moment, but not very smart over time (*cough* Enron *cough*). Yes, there are some business principles that really do stand the test of time and could be considered business wisdom, but I’m thinking more about wise decisions, not wise principles. Other folks tend to treat wisdom as ineffable or only obvious in situ, you know it when you see it but you can’t analyze it. That doesn’t leave me much traction, so I focus on frameworks that give me some possibility for doing things differently.

So the definition I like for wisdom comes from Robert Sternberg, where he talks about making decisions that are not just smart in the short term, but in the long term. Decisions that consider not just me and mine, but society in general. And decisions that are based on values that are articulated and examined, not implicit and potentially less then optimal. I suggest that this sort of approach would lead to better decisions.

One of the things would be just to get people to start making decisions with this approach. If you accept the view that for situations where we’re experts, we can trust our gut, this means more to slow down when we’re making decisions out of our comfort zone. It’s harder work, to be very conscious in our decision making process, but I hope it’s implicitly obvious that making better decisions is the best solution.

And this segues into the broader topic of the organizational culture. I’m not immune to the view that there’s a certain personal attitude to wisdom. The wisest people I know are also the most unflappable, thoughtful and warm. And I think that’s hard to accomplish in an organization where everything you say can and will be held against you. You’ve got to have the appropriate culture for such an approach to flourish. Which ties to Jay’s interest in well-being, bring me full circle.

So, I think there’s an argument to be made for consider wisdom in business, as part of a longer term shift from short term returns to a sustainable differentiator. Coupled with appropriation of collaboration and cooperation, I suggest organizations can and should be working wiser and more coherently.

Emergent & Semantic Learning

10 July 2012 by Clark 2 Comments

The last of the thoughts still percolating in my brain from #mlearncon finally emerged when I sat down to create a diagram to capture my thinking (one way I try to understand things is to write about them, but I also frequently diagram them to help me map the emerging conceptual relationships into spatial relationships).

What I was thinking about was how to distinguish between emergent opportunities for driving learning experiences, and semantic ones. When we built the Intellectricity© system, we had a batch of rules that guided how we were sequencing the content, based upon research on learning (rather than hardwiring paths, which is what we mostly do now). We didn’t prescribe, we recommended, so learners could choose something else, e.g. the next best, or browse to what they wanted. As a consequence, we also could have a machine learning component that would troll the outcomes, and improve the system over time.

And that’s the principle here, where mainstream systems are now capable of doing similar things. What you see here are semantic rules (made up ones), explicitly making recommendations, ideally grounded in what’s empirically demonstrated in research. In places where research doesn’t stipulate, you could also make principled recommendations based upon the best theory. These would recommend objects to be pulled from a pool or cloud of available content.

However, as you track outcomes, e.g. success on practice, and start looking at the results by doing data analytics, you can start trolling for emergent patterns (again, made up). Here we might find confirmation (or the converse!) of the empirical rules, as well as potentially new patterns that we may be able to label semantically, and even perhaps some that would be new. Which helps explain the growing interest in analytics. And, if you’re doing this across massive populations of learners, as is possible across institutions, or with really big organizations, you’re talking the ‘big data’ phenomena that will provide the necessary quantities to start generating lots of these outcomes.

Another possibility is to specifically set up situations where you randomly trial a couple alternatives that are known research questions, and use this data opportunity to conduct your experiments. This way we can advance our learning more quickly using our own hypotheses, while we look for emergent information as well.

Until the new patterns emerge, I recommend adapting on the basis of what we know, but simultaneously you should be trolling for opportunities to answer questions that emerge as you design, and look for emergent patterns as well. We have the capability (ok, so we had it over a decade ago, but now the capability is on tap in mainstream solutions, not just bespoke systems), so now we need the will. This is the benefit of thinking about content as systems – models and architectures – not just as unitary files. Are you ready?

Stealth mentoring

2 July 2012 by Clark 3 Comments

I was looking for any previous post I’d made about stealth mentoring, so I could refer to it in a post I was writing, and I couldn’t find it. It’s a concept I refer to often (and have to give credit to my colleague Jay Cross who inspired the thought), so here’s my obligatory place holder.

When someone is thinking and learning ‘out loud’, e.g. putting their deeper reflections on line via, say, a blog (er, like this one, recursively), they’re allowing you to look at where and how their thinking is going. When they also are leaving a trail of what they think is interesting (e.g. by pointing to things on Twitter or leaving bookmarks at a social bookmarking site), you can put together what’s interesting to them and what their resulting thoughts are, and start seeing the trajectory of their thinking and learning.

In formal learning, we can think of modeling behavior and cognitive annotation, the processes covered in Cognitive Apprenticeship as a development process. In a more informal sense, if you had a leader who shared discussions of their thinking with you, you’d consider that mentoring.

Similarly, here, with a difference. If they’re blogging and tweeting, or otherwise leaving tracks of their thinking, they can be mentoring you and not even know it. You’re being a stealth mentee! So, if you can find interesting people who blog and tweet a lot, and you follow their blogs and tweets, they can be mentors to you!

I strongly recommend this path to self-development. One of the ways to accelerate your own growth, part of your personal knowledge management path, is to mentor folks who represent the type of thinking you believe is interesting and important. By the way, don’t just consume, interact. If they say something you don’t understand or disagree with, engage: either you’ll learn, or they will.

And, as an associated caveat, I strongly recommend that you also similarly share your thinking. You can be not only stealth mentored, but folks who read and comment become actual real mentors for you, shaping your thinking. The feedback I’ve gotten through comments on my blog has been extremely beneficial to improving my own thinking, and I’m very grateful.

I really do think this is an important opportunity for personal self-development, and it’s a benefit of the increasing use of social media. I hope you are practicing learning out loud and leaving traces of what’s interesting you as you wander hither and yon. I think it’s something an app like Tappestry could provide as well, leveraging the Tin Can API, where you might more explicitly see a richer picture of what someone’s doing. But I’m getting into the weeds here, so I’ll simply point out that there’s an opportunity here. You owe it to others to think and learn out loud, and then can take advantage of others who do so with a clear conscience.

BJ Fogg #mLearnCon Keynote Mindmap

19 June 2012 by Clark Leave a Comment

BJ Fogg, known from his work on persuasive technology, talked about making persistent behavior change via tiny habits. Very interesting research with important implications both personally and commercially.

George Siemens #iel12 Keynote Mindmap

6 June 2012 by Clark Leave a Comment

George Siemens delivered an enlightening talk contextualizing analytics, tying the need for more effective coupling in decision making with new types of data.

Flipping assessment

30 May 2012 by Clark 3 Comments

Inspired by Dave Cormier’s learning contract, and previous work at learner-defined syllabi and assessment, I had a thought about learner-created project evaluation rubrics. I’m sure this isn’t new, but I haven’t been tracking this space (so many interests, so little time), so it’s a new thought for me at any rate ;).

It occurred to me that, at least for somewhat advanced learners (middle school and beyond?), I’d like to start having the learners propose evaluation criteria for rubrics. Why? Because, in the course of investigating what should be important, they’re beginning to learn about what is important. Say, for instance, they’re designing a better services model for a not-for-profit (one of the really interesting ways to make problems interesting is to make them real, e.g. service learning). They should create the criteria for success of the project, and consequently the criteria for the evaluation of the project. I wouldn’t assume that they’re going to get it right initially, and provide scaffolding, but eventually more and more responsibility devolves to the learner.

This is part of good design; you should be developing your assessment criteria as part of the analysis phase, e.g. before you start specifying a solution. This helps learners get a better grasp on the design process as well as the learning process, and helps them internalize the need to have quality criteria in mind. We’ve got to get away from a vision where the answers are ‘out there’, because increasingly they’re not.

This also ties into the activity model I’ve been talking about, in that the rationale for the assessment is discussed explicitly, make the process of learning and thinking transparent and ‘out loud’. This develops both domain skills and meta-learning skills.

It is also another ‘flip’ of the classroom to accompany the other ways we’re rethinking education. Viva La Revolucion!

Making rationale explicit

29 May 2012 by Clark Leave a Comment

In discussing the activity-based learning model the other day, I realized that there had to be another layer to it. Just as a reflection by the learner on the product they produce as the outcome of an activity should be developed, there’s another way in which reflection should come into play.

What I mean here is that there should be a reflection layer on top of the curricula and the content as well, this time by the instructor and administration. In fact, there may need to be several layers.

For one, the choice of activities should be made explicit in terms of why they’re chosen and how they instantiate the curricula goals. This includes the choice of products and guidance for reflection activities. This is for a wide audience, including fellow teachers, administrators, parents, and legislators. Whoever is creating the series of activities should be providing a design rationale for their choice of activities.

Second, the choice of content materials associated with the activities should have a rationale. Again, for fellow teachers, administrators, parents, and legislators. Again, a design rationale makes a plausible framework for dialog and improvement.

In both cases, however, they’re also for the learners. As I subsequently indicated, I gradually expect learners to take responsibility for setting their own activities, as part of the process of becoming self learners. Similarly, the choice of products, content materials, and reflections will become the learners to improve their meta-learning skills.

All together, this is creating a system that is focused on developing meaningful content and meta-learning skills that develops learners into productive members of the society we’re transitioning into.

(And as a meta-note, I can’t figure out how to graft this onto the original diagram, without over-crowding the diagram, moving somehow to 3D, or animating the elements, or… Help!)

Applying Expertise

17 May 2012 by Clark Leave a Comment

I’m trying to get my mind around how the information we’re finding out about expertise matches to the types of problems people face. Clearly, you want to align your investments appropriately to situations you face. If you look across the literature on expertise, and the recent writing on how our brains work (c.f. Kahneman’s Thinking Fast and Slow), you see an emerging picture of expertise. When you combine this with the situations organizations are increasingly facing, you recognize that we need to get more granular about the types of problems we’re facing and the solutions we have on tap.

Starting with the types of problems, there are more than just the problems we know and the ones we don’t. When you look at the Cynefin model, which characterizes the types of problems we face, we see what types of expertise are helpful. Beyond work that should be automated, there are formulaic types of complicated problems that can be outsourced or accomplished by skilled or well-supported practitioners. Then there are the complex problems that require deeper expertise. Beyond that is the chaotic state where you have to try something to move it into one of the other states, and there are certainly reasons to believe that deep expertise .

So now we look at what’s known about our knowledge. We’ve known for a while that expertise is slowly accumulated, and becomes deeper in ways that are hard to unpack (hence why you need some detailed approaches to get at their understanding). What’s also becoming clear is that this ability to make expert judgements, once compiled away, is most effective in quick (not laborious) application, with a caveat. As Kahneman tells us, this expertise needs to be developed in a field that is “sufficiently regular to be predictableâ€, and in which the expert gets quick and decisive feedback on whether he did the right or the wrong thing. Otherwise, you need to do the hard yards, the slow thinking that’s effortful and systematic. Now, if it’s out of your area of expertise but a known problem, you have two choices: either take a well-known (and appropriate) but laborious approach, or hire the appropriate expertise. If it’s a relatively novel situation, either unique or new, you’ll need a different type of expertise.

We can infer that having a rich suite of models and frameworks helps in circumstances where the right solution isn’t obvious. The conclusion is clear: advanced experts may not immediately know the solution if the problem is reasonably complex (if so, you can get by with a practitioner), but their deeply developed intuition, based upon experience, and associated approaches to those types of problems will have a higher likelihood of finding a solution. Particularly if their expertise spans problem-solving in general, and specific expertise in at least some of the involved domains. Experience solving complex problems, and having a deep and broad conceptual background increases the likelihood of a systemic and comprehensive solution.

To think about it another way, this article makes a distinction between puzzles and mysteries. Puzzles have an answer, once you identify the information needed, collect it, and execute against it. This is the ‘Complicated’ part of the Cynefin situation. Mysteries are where you can’t know what will happen, and you have to experiment. This is the ‘Complex’ or ‘Chaotic’ parts of the model. You’re better off in the latter two if you’ve got good systems thinking, and a suite of useful models in your quiver.

So, when facing a problem, you have to characterize it: is this a puzzle where someone has an off-the-shelf solution “ah, we know that pain, and we solve it this way”, versus the mystery situation where it’s not clear how things will sort out, and you need a much richer conceptual background to address it. In the former case, you can find vendors or consultants with specific expertise. In the latter, the implication clearly is that you want someone who’s been thinking and doing this stuff as long as possible. You want someone who can guide some experiments.

The risk of trying to solve the complex problems with off-the-shelf solutions or DIY is that your answer is likely to be missing a significant component of the situation, and consequently the solution will be partial. You need the right type of expertise for the right type of problem.

Mentoring

9 May 2012 by Clark 2 Comments

I was talking today with an organization that has mentoring as a very core feature of their culture, and it got me reflecting on the fantastic luck I’ve had in my career. Even before working, I had some great teachers, and then many folks have helped shape me through my job experience.

Dick Bergeron was a 12th grade teacher at SPHS who really helped me understand a different path to learning and thinking. He did what I now know to be reciprocal teaching, had us take turns talking about what we were learning from our reading, and discussing it, with him facilitating our reflection.

I got a job while I was in college at UCSD, maintaining the computer records for the office that did tutoring on campus, and Carmel Myers and Ken Majer helped me learn how to be professional (particularly when I screwed up).

Seeing the connection between computers and learning via that job, I designed my own major in college, and with the guidance of Hugh Mehan and Jim Levin learned a lot about what constituted good research. They also lived what great student development was.

In my first job, designing and programming educational computer games for DesignWare, Jim Schuyler and Lesley Czechowicz helped me learn quite a bit about how organizations run and what good management is.

I returned to grad school at UCSD after a summer working with Ken Majer again, and Don Norman patiently helped me rediscover and expand my understanding of research and cognition, particularly the application thereof. Don is also a great role model for top-notch critical and innovative thinking. At the Learning Research & Development Center at the University of Pittsburgh, Leona Schauble furthered my understanding of detailed research and development while guiding my post-doctoral fellowship.

At the University of New South Wales, where I took up an academic position, Paul Compton served to model what a wise leader really looks like. When I stepped away from the University, first Ron Watts at Open Net and then Rim Keris at Access CMC helped me take steps in understanding a strategic approach to business.

Jim Schuyler brought me back to the US, where he again mentored me on team leadership. He also introduced me to Joe Miller, who has served to really help me understand the next level of organizational strategy.

Since then, my business partners, Charlie Gillette at Knowledge Anywhere and Mohit Bhargava at LearningMate, have helped me learn much about business models and the art of the deal. And my colleagues in the Internet Time Alliance – Jay Cross, Jane Hart, Harold Jarche, and Charles Jennings – have taught me a lot about working as an independent and together.

These are the ones where I had an extended period of time with, but many other folks have served as models and provided assistance: Ellen Wagner and Marcia Conner are two that come to mind, and many more I’m forgetting, as well as many friends who’ve shared good times (and bad). Naturally, my parents had a wee bit of influence as well. And I’ve been doubly fortunate that many of these folks have remained in touch.

There certainly is an art to mentoring, though it can be developed, and I reckon there’re also some skills to being a good mentee. I hope I’ve been able to do some of the former, and not do the latter too badly.

I certainly still have lots to learn, but I’m exceptionally grateful to these folks. Many kudos to them, but of course all faults of the end product remain with the author ;). I think increasingly we can and should be continually mentoring those we care about, and mutually mentoring each other. Here’s to learning!

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