Learning is harder in the digital world

Snowboard lesson Most of us have crappy theories of learning. The better you were at school the more likely your theories about learning are distorted. I ran into this phenomenon while I was the Chief Learning Officer at Diamond Technology Partners in the 1990s. My partners were full of well intentioned advice about how they thought I should do my job based on their school experiences years or decades earlier in their lives. I had my own, somewhat less ill-informed, theories based on my more recent school experiences convincing my thesis committee to let me loose on the world.

Fortunately, I also made the smart decision to go find several people smarter than I was and hung around with them long enough to soak up some useful insight. Two in particular, Alan Kay and Roger Schank, were instrumental in shaking me free from my poor theories. Very different in temperament, they did agree on fundamental insights about how learning worked.

Learning is what happens when our expectations about the world collide with experience. As we adjust our expectations to be better aligned with reality we learn.

Schools are dangerous places for learning because they are too isolated from the real world. On the other hand, the real world can be straight up dangerous if we haven’t learned how to behave correctly in the situation at hand. All learning is learning by doing, whether we’re learning to turn on a snowboard or solve a differential equation. If we had unlimited time and were invulnerable, we could figure anything out on our own. As it is, it helps to have someone who knows more than we do to arrange the experiences we can learn from in a reasonable and safe sequence.

The name for this strategy is “apprenticeship” and remains the most effective from a learner-centered perspective. All other approaches are compromises to make the economics work or to solve scale mismatches between the number of those needing to learn and those with mastery to pass along. Anthropologist Lucy Suchman showed how to extend this notion of apprenticeship to all kinds of learning beyond the trade/craft connotations we attach to the word. She talked of learning and apprenticeship as a process of “legitimate peripheral participation.” You learned how to repair copiers by handing tools to the senior repair technician and carrying their bags. You learned how to handle the cash register by watching someone who already had it figured out. You learned how to put a budget together by doing the junior-level scut work of helping your boss transform a handwritten budget into a typewritten one.

It’s become a cliche that learning has become an ongoing requirement in all kinds of work. The problem isn’t simply that work demands more learning more often. The changing nature of work also makes learning qualitatively harder as well. This was never a problem for physical work and for much of the knowledge work of the 20th century. Nearly everything you might want to observe was in sight. You could watch how a repair technician selected and handled tools. You could see an editor’s corrections and notes in the margins of your manuscript.

As work has evolved to be more abstract and more mediated by technology, the task of learning has gotten harder. Whether we call it apprenticeship or legitimate peripheral participation it becomes difficult, if not impossible, in environments where you can’t see what others are doing. Previously, the learning called for within organizations occurred as a byproduct of doing work. It now takes conscious and deliberate effort to design work so that it is, in fact, learnable.

Smart people doing smarter work

For today’s organizations, success depends on the effective care, feeding, and management of smart people. This is not the same thing as managing the ideas these smart people produce, which is where too many organizations get stuck. Ideas may be the basic output of the knowledge economy but you can’t manage by focusing on these outputs.

In an industrial economy, you focus on outputs and the game is to optimize the faithful replication of outputs. Organizations lavish attention on standardization, process, uniformity, and predictability to produce identical outputs. It is tempting to equate ideas with products because we know how to do and manage replication. Software behemoths like Microsoft were built on taking one expensive first copy and figuring out how to distribute that copy as far and wide as possible. There was so much money to be made in the replication and distribution of the copies that there was little need to think, much less worry, about the economics of creating copy number one.

Professional service firms, advertising agencies, and other knowledge intensive organizations pay more attention to the economic importance of ideas. But their management focus and attention ignores the hard problem of the gestation and delivery of new ideas. Instead they apply the techniques and mindsets of industrial models to standardizing the irrelevant. They industrialize support processes and functions. In the best cases, they make an effort to streamline and support the work of the creative core. But their principal managerial strategy is what Tom Davenport accurately characterized as “hire smart people and leave them alone.”

How do we systematically enable smart people to do smarter work? Where are the effective leverage points if industrial models aren’t the answer? First, it helps to look at individual knowledge workers and groups separately. Second, we need to focus on effectiveness over efficiency.

I’ve written elsewhere about the challenges of looking at knowledge work–Managing the visibility of knowledge work – McGee’s Musings. An excellent recent example of this kind of observation of work practice and its value comes in a recent blog post by author Steven Johnson, “Why Writing Books Is More Than Processing Words – Workflow – Medium,” where Johnson reflects on how he approaches his work. To improve the effectiveness of knowledge work we have to go into the wild and study what practitioners are actually doing.

What I’m suggesting is the value of “reflective practice.” Donald Schön, late of MIT. argued that management–and knowledge work–is characterized by the need for practitioners to formulate and build theories of their work and their environment as an ongoing component of doing their work. “Practice” and “reflection” are both necessary to becoming effective in complex knowledge work settings.

This is more demanding than simply thinking about knowledge work in terms of productivity and efficiency. It asks you to think at multiple levels of analysis in parallel; to be adept at both cognition and meta-cognition. Most damning, perhaps, is that this course of inquiry appears to be overly abstract and academic to most managers.

We need to build better insight into how knowledge work gets done and how smart people are attempting to systematically improve their practices. That means going into the wild and studying what practitioners–effective and ineffective–are actually doing. For knowledge intensive organizations, this is an effort that can potentially yield substantial gains in knowledge work effectiveness.

The costs of context switching

Multiple ScreensMulti-tasking doesn’t work but our lives demand it anyway. This leaves us with the problem of how to compensate for the productivity and quality losses generated by work environments that demand parallel processing our brains can’t handle.

Why can’t our brains multi-task and what happens when we try? Left brain/right brain discussions aside, we only have one brain and that brain is single-threaded; it’s built to work on one cognitive problem at a time. Most of us can manage to walk and chew gum at the same time, but we can’t read the paper and discuss changes in the day’s schedule with our spouse simultaneously.

The bottleneck is attention. When we pretend to multi-task, what we are doing is cycling focus among the tasks competing for our attention. Each time we switch focus, we have to re-establish where we were in our work when we left off before we can begin moving forward. We also have to set aside the work we were doing along with whatever supporting materials we were using.

This process of redirecting focus is a context switch. Context switching is expensive because complex tasks—writing a blog post, debugging code, analyzing sales data—depend on equally complex mental scaffolding. When writing a blog post, for example, that scaffolding can include notes on the points to be made, memory of relevant previous posts ideas about upcoming blog posts, links and open browser tabs to supporting research, and so on. That scaffolding might be spread across multiple computer screens and program windows. It might also handwritten notes or paper copies of relevant supporting articles. All of that supporting scaffolding, along with the current draft of the blog post, helps you build up the mental structures that eventually lead to a finished draft of your post.

Suppose now that I need to put aside the blog post in progress to take an incoming phone call from my boss. It’s a call about a proposal we are putting together for a client. It might be just a simple call to confirm a detail in the proposal document, or it might be a more complex discussion about whether to rethink and reorganize the entire proposal. Regardless, I need to set aside the work on the blog post and flush my mind of all the details. I then need to call to mind the salient details of the client and the draft proposal as the call unfolds. In the first moments of this call, I’m not likely to be terribly articulate or smart. As the call progresses, I may need to call up various supporting materials and gradually fill in an entirely new context to contribute to the conversation.

Switching tasks means that you have to also break down one context and stand up a new context before you can actually begin to do any meaningful work. When the call is complete, you need to reverse the process to resume work on your blog post. Will you recall the insight that was just coming into focus when you were interrupted by that call from your boss? Or is it lost forever?

How expensive is this context switch? Research from the world of software development (see Jeff Sutherland’s Scrum: The Art of Doing Twice the Work in Half the Time) suggests that switching between two projects can result in productivity losses of 20%. Add a third project to your list and the costs rise to 40%. This means that each project gets no more than 20% of your attention and focus. Is it any wonder then that professionals work the hours that they do?

Step one in solving any problem is recognizing it. Limiting the number of projects you are working on and carving out big blocks of time to focus exclusively on each project helps. This is the core advice of most time management gurus. Few of us, however, have that much control over our responsibilities. A more attractive target then is to think about ways to lower the costs of context switching. We’ll come back to that in the next post.

Knowledge management matters more to you than to your organization

I gave a talk on Saturday for ChicagoLand PMI about why knowledge workers needed to develop strategies and the supporting habits and practices to manage and develop their know how across organizations and across time. If you’re interested you can find a copy of my slides on Slideshare.

Knowledge management as buzzword and practice originated in solving organizational problems. That’s where the big, obvious, problems are as well as the budgets. But the roots of the problem lie in the changing nature of work and careers at the individual level.

My father worked for three organizations in his career; I’ve worked for twenty so far and the number is likely to climb. Some might argue that this reflects either a severe case of ADD or a general inability to hold a job. Regardless, the trend is real; knowledge workers will work for more organizations and have shorter tenures at each. Organizations worry about the knowledge retention problems this creates; I’m more interested in the knowledge management problems it creates for individuals. I am aware of a handful of people who are also thinking about this; Harold Jarche, Luis Suarez. If you know of others, I would love to hear about it. 

The nub of my concern is this. You cannot rely on your memory and the experience it encodes. You also can no longer rely on having access to the institutional memory and artifacts of any one organization to supplement your limited human capabilities. You ought to be thinking about and planning for how you will accumulate knowledge and expertise over time. What personal infrastructure should you be building that can travel with you? How should you adapt your work habits and practices to simultaneously deliver value to your organization and enhance the value of your personal knowledge base? What new practices and skills do you need to add to your repertoire?

Entropy and knowledge management

What does the second law of thermodynamics tell us about knowledge management? There’s some pretty complex mathematics around the laws of thermodynamics, but the poet’s version will do for our purposes:

  1. You can’t win
  2. You’re going to lose
  3. You can’t get out of the game

Life is a constant battle against entropy or disorder. Cars break down; they don’t repair themselves. Left to themselves, files, books, and ideas become disorganized. Organizations and the knowledge workers inside them are engaged in a constant, but doomed, fight against entropy; the order they bring is always temporary.

Knowledge management is one of many disciplines engaged in that fight. If entropy is destined to win, what does that tell us about how to carry on the fight?

It reminds us that perfection is the wrong goal. You can’t define a perfect taxonomy; 100% compliance with the documentation standards is wasted effort; there will always be something more pressing than the paperwork. This matters because the personalities attracted to the apparent orderliness of knowledge management tend to be seekers of this impossible perfection. You want to temper that predisposition, not feed it.

Surrendering to disorder isn’t a good strategy either. Let the reality of entropy shape our strategies and practices. There are things we should worry less about and things we might better do differently.

We should worry a lot less about perfection and completeness and strive instead for a standard of “good enough”. That requires more judgment and sensitivity to unique circumstance than most organizations—and many individuals—are comfortable with. Black and white makes for easier, albeit impossible, compliance standards and management.

If you are in a position to shift an organization in the direction of more gray, encourage that. If you are enmeshed in unrealistic organizational expectations, strive for only as much compliance as will keep the auditors and censors at bay. I’m not advocating open rebellion, or even mild “civil disobedience”; simply be comfortable that you have the laws of physics on your side while you quietly ignore stupider requirements.

If entropy is the law, how might you operate differently?

Learn where small efforts now postpone or eliminate major remediation efforts. Whatever you opt to do now, you are going to live with that choice later. Make your choices with that appreciation of a disorderly later. You are never going to go back later to add the appropriate tag, improve the name of the file, or reorganize the project team’s directories. Recognize the places and moments where a tiny injection of order now will pay lasting dividends. Don’t pretend that you can get organized after the press of the immediate has passed.

Entropy is inevitable. As a knowledge worker, your task is to create pockets of order out of the noise. As you create those pockets, don’t increase the noise everywhere else.

EntropyAndKidsHMP Comics

Carve out time and space for deep thinking

 

Deep Work: Rules for Focused Success in a Distracted World, Cal Newport

If your value to an organization depends on the quality and insight of your thinking, Cal Newport’s latest book, Deep Work, offers important insights about how to think about your thinking. The forces at work in our environment and in our organizations favor quick, shallow, and social over other forms of thought. That is generally adequate for much of the activity that fills our days.

Exceptional value, however, finds its roots in sustained, focused, individual thinking and reflection. Deep Work builds the case for this mode of thinking and offers paths to carve out the necessary time and develop the necessary mental muscles to engage in deep work more intentionally and predictably.

Newport defines deep work as

Professional activities performed in a state of distraction-free concentration that push your cognitive capabilities to their limit. These efforts create new value, improve your skill, and are hard to replicate.

The ability to think deeply is a skill, so it can be developed. Much of the book offers counsel on techniques and practices that can help you develop your skills at deep work.

Newport is an academic computer scientist. In his world the contrasts between deep and shallow work can be stark. His binary distinction doesn’t transfer neatly to organizational settings where it is more useful to think in terms of a spectrum of work with shallow and deep anchoring the extremes. With the pressure toward superficial and shallow, there is great opportunity for individual knowledge workers to become more proficient at going deep.

Newport does offer practical advice for how to make deep thinking more possible. That advice needs tuning and refinement to work well in most complex organizations. Newport sums up why that effort matters thusly;

A commitment to deep work is not a moral stance and it’s not a philosophical statement—it is instead a pragmatic recognition that the ability to concentrate is a skill that gets valuable things done. Deep work is important, in other words, not because distraction is evil, but because it enabled Bill Gates to start a billion-dollar industry in less than a semester.

Practice and Performance

Cpl. Derek McGee, USMC MEU15 TRAP 2013

How do you strike an effective balance between practice and performance? In many realms we draw a distinction between performing and preparing to perform. Actors and musicians rehearse. Athletes practice. Soldiers train before they fight.

In other, equally demanding, realms the boundary is fuzzy; at times non-existent. Where does a sales rep or project manager practice? Where does a brand manager practice market segmentation? When does an investment banker practice designing a deal?

The notion of an apprentice observing and mimicking a master is one proven model that blends practice and performance. What troubles me is that this model works best when it is explicit. There needs to be some recognition that some performance settings are about both performance and practice; some fraction of your focus and attention needs to be tuned to learning.

My sense is that we have abandoned the notion of practice built into apprenticeship and favor performance exclusively. If we substitute performance only in place of practice and performance, do we abandon the possibility of achieving peak performance? How do we recognize situations that call for effective apprenticeship models? How do we design organizations so that they meet their performance goals and provide the necessary practice opportunities so that tomorrow’s organization can perform as well or better than today’s?

Connected Courses Course – An Experiment in Collaboration – #CCourses

I’m carving out time to participate in what I see as a worthy experiment in collaboration. It’s been organized by some of the most interesting people working on online learning and seems to be attracting an equally interesting collection of people interesting in participating.

Here’s what they have to say:

We invite you to participate in a free open online learning experience designed to get you ready to teach open, connected courses no matter what kind of institution you’re working in. We’ll explore how openness and collaboration can improve your practice and help you develop new, open approaches.

You can mix and match — take one unit or take them all, and go at your own pace. You’ll be joined by other participants from around the world who are looking to:

  • get hands-on with the tools of openness;
  • create open educational resources, curriculum and teaching activities and get feedback from a community of your peers; and
  • connect with and learn alongside other faculty, educators and technologists.

Sign up and receive updates from the organizers. Everyone is welcome, and no experience is required. We will all learn together in this free and fun opportunity to start planning your own connected course. The instructors, award-winning university professors from around the globe, are the innovative educators behind successful connected courses such as FemTechNetds106phonar, and the National Writing Project CLMOOC.

An orientation starts Sept. 2 and the first unit starts Sept. 15, 2014 and you can sign up and find more details about the topics we’ll be exploring at connectedcourses.net.

[Connected Courses Sign Up]

This is being billed as “a collaborative community of faculty in higher education developing networked, open courses that embody the principles of connected learning and the values of the open web.” I think it is something richer than that.

Paying attention is the least that you should do if you are interested in issues of collaboration, learning, and new organizational forms. Jumping into the pool with the rest of the crowd is a better idea.

Collaboration, games, and the real world

I’ve been thinking a lot about hard problems that need multiple people collaborating to solve. There’s no shortage of them to choose from.

This TED video from Jane McGonigal makes a persuasive case that I need to invest some more time looking at the world of online gaming for insight. Watch the video  and see if you don’t come to a similar conclusion.

 

Doing and Managing Knowledge Work: TUG2010 Keynote Reflections

I’m back from last week’s Traction User’s Group meeting, TUG2010, where Greg Lloyd graciously asked me to do the opening keynote. I’ve posted the slides on Slideshare and wanted to add some further commentary here.

 

First, one caution; when I do use slides I don’t design them to be standalone documents. There are too many bullet points in the world as it is. What I’d like to do here is highlight and elaborate on some of the key points I was trying to make.

Peter Drucker first called our attention to the importance of knowledge workers decades ago. The rest of us are slowly catching up to his ideas. One shift in focus that I’ve begun to emphasize is toward the knowledge work itself and away from the notion of knowledge worker as somehow distinct from other kinds of workers. Trying to distinguish who may or may not be a knowledge worker as opposed to some other kind of worker simply perpetuates pecking order games that do little to further the mission of an enterprise. We all do knowledge work  to some degree or another, we are all doing more knowledge work than before, and the important question is how to do that work more effectively.

The notions of visibility and observability have been central to my thinking for some time now. The evidence is clear that dealing with complex problems and thinking requires a certain amount of corresponding complexity and mess in our working environments. To those whose focus is on stability and operational control, mess, of course, is disturbing. So disturbing that we ridicule those who deviate from the presumed ideal. We do so at a greater organizational cost than we realize, however, when we ignore the complexity in the environment that is driving the mess.

I introduced the following simple map to suggest just how unavoidably messy the real world of knowledge work can be. The x-axis maps the inherent structure of the knowledge “stuff” we encounter; the y-axis maps the degree to which knowledge stuff is individual or social. It didn’t take long to identify a multitude of items and objects that you might routinely encounter as you go about your work.

KnowledgeStuffMap-2010-10-19-1045

It’s tempting to simplify this reality in some way. Many years ago P&G was famed for teaching its managers to distill their arguments into one-page memos. Too many consultants and speakers opt to squeeze all of their output into slideuments; which merely transfers the problem somewhere else. Senior executives rely on staffs to filter the stream at the risk of filtering out the essential insight or data point that truly informs.

The strategy I prefer is to accept the fundamental messiness and seek ways to tame it enough to make it manageable. Part of that relies on exploiting the natural pattern-seeking, pattern-matching capabilities of the human mind. Part relies on enlisting the pattern management capabilities of the other human minds in the system to supplement your own capacity. Both of which also need to be tempered by appreciation for the limits of those same capabilities.

Taming the mess breaks into three layers of practices:

  1. Hygiene. The proliferation of objects in a physical office offer a host of clues about their contents and relative importance; size, shape, color, location on a shelf or desktop, position in a stack, etc. In a digital environment you need to provide the equivalent of those clues explicitly and consciously. Seemingly mundane decisions about the file names you choose, for example, can make large differences when you are later scanning through a page of search results. Most of today’s systems provide little real assistance in this arena; you and your teams need to develop their own standards for naming files, managing versions, and other details of the knowledge stuff they work with.
  2. Metadata. i wish there were a more homespun term for this layer. One of the central tricks to taming the flood of data and information that constitute your digital world is to add more data to the flood. The ability to tag the items you create or encounter with labels that are meaningful to you greatly leverages the other tools at your disposal. Merge those tags with the tags of those in your social network and you shorten the path to finding what you are searching for still further, either on your own or through your network’s help.
  3. Context. One of the least appreciated aspects of messiness in the physical world is the context it provides. There’s a story attached to each pile and object; a story that can be triggered by its context. The power of this context is why students do better on tests when they take them in the same classroom they took the course in than in a random room. A filename in a directory listing or a document displayed on  a monitor lack this ready context and are poorer for it. The alternative is to become more mindful of the importance of context and make an effort to capture it explicitly and contemporaneously. This is rationale behind such notions as narrating your work, and developing a digital portfolio.

There is a payoff to all of this for both individuals doing knowledge work and the organizations they contribute to. Once again Peter Drucker said it first; “the most valuable asset of a 21st century institution will be its knowledge workers and their productivity.”  Economic growth in this century depends on our ability to improve knowledge work productivity; until you can see it, you can’t improve it.