Work is learning. Learning is the work.
Networked learning
Sharing complex knowledge requires trusted professional relationships. You cannot just throw people together and hope they will work effectively on difficult problems.
“strong interpersonal relationships that allowed discussion, questions, and feedback were an essential aspect of the transfer of complex knowledge” —Hinds & Pfeffer (2003)
Workplace training and education too often resemble modern playgrounds:
“safe, repeatable, easily constructed from component parts, requiring that the child bring little of their own to the experience” – Johnnie Moore
When adults design for children they have a tendency to dumb things down. Perhaps the notion that there is no such thing as writing for children should be extended to workplace training and education design. In the workplace, thinking of co-workers as “learners” actually may be a barrier to learning.
The real value of the MOOC (massively open online course/content) could be its potential to remove the barrier between learners, designers, and instructors. Its workplace learning potential may be greater than its academic value. But if one thinks of the MOOC as a course, designed by one party for another party, then it really is nothing new.
“Indeed, I was struck by a recent comment from someone with 15 years of experience in designing face-to-face, blended and online credit programs: I am trying to understand what MOOCs can offer that my understanding of educational design, learning design and online and distance education does not include. I’m afraid that the answer continues to be: ‘Nothing’, at least for the moment.” – Tony Bates
But the MOOC can foster emergent learning, which makes it an optimal form for understanding complex issues. This is something that a curriculum-based, graded, course is not well suited to support. With the MOOC, especially one focused on being massive and open, there is a greater possibility for serendipitous connections, such as what happened with participants becoming instructors in the early MOOC we conducted in 2008.
If we think of the MOOC as a vehicle for shared understanding, and not content delivery, it becomes the collective equivalent to personal knowledge mastery. It is group learning, with some structured content, and good facilitation; but most importantly, space for sense-making. In the complex domain, combining PKM with more structure for social learning, using the MOOC format, can be an important addition to how workplace learning is supported.
Update: several possibilities for corporate MOOC’s from Donald Clark.
But neither the flat organization nor empowered employees have been fully realized. The reason is that most of us have been working over the years to solve problems by creating new and improved companies, rather than by equipping individuals with their own empowering tools. What we still need are tools that make individuals both independent of companies and better able to engage with companies (or with organizations of any kind). Social tools alone won’t do it — especially ones that are still corporate silos. (And, forgive me, even Quora is an example of that.) – Doc Searls: Answering “Why has the empowered employee predicted in the Cluetrain Manifesto not emerged?” in Quora
You cannot read the rest of Doc’s answer unless you log into Quora, which is a pretty good example that most social media companies are just as control-oriented as any industrial organization was. If you have not read The Cluetrain, you should at least peruse some of its 95 theses. The initial thesis of The Cluetrain Manifesto is that markets are conversations, but I think that theses 10 through 12 describe the big potential change in relationships brought on by the Internet.
#10. As a result, markets are getting smarter, more informed, more organized. Participation in a networked market changes people fundamentally.
#11. People in networked markets have figured out that they get far better information and support from one another than from vendors. So much for corporate rhetoric about adding value to commoditized products.
#12. There are no secrets. The networked market knows more than companies do about their own products. And whether the news is good or bad, they tell everyone.
As Doc mentions, the big challenge is equipping individuals with their own empowering tools. These tools are hardware, software, and most importantly, skills and attitudes. Taking control of our learning is a challenge for individuals used to working inside hierarchies that demand conformity and compliance. Note that without compliance training there would be almost no e-learning industry. The deck is still stacked against networked individuals.
So if you read the Cluetrain back in 1999, or have since quoted it, then it’s time to think about how to implement it. I have written about hierarchies and connected organizations for the past few weeks here. I have no doubt that major systemic change is necessary to deal with the wicked problems that face society today. Critical components that need to change are how we work and how we learn in organizations. That change has to start with people. Individuals need to build their own interdependent learning networks.
This is not a leadership or a management responsibility. This is a people issue. Each one of us should start seeking knowledge, building upon it, and sharing it, all in public. In this way we can develop an aggressively intelligent and engaged citizenry.
For the first time in history we have the means to learn together without any institutional or organizational intermediaries. We don’t need schools, or even corporate MOOC’s. It is not easy, but it is possible to create a global group of co-learners around almost any problem or subject. What’s holding us back? I think we are holding ourselves back.
If participation in a networked market changes people fundamentally, then it’s time to make some fundamental changes. Here is an example of re-thinking market relationships. Doc Searls is working with the Vendor Relationship Management project, which is “based on the belief that free customers are more valuable than captive ones — to themselves, to vendors, and to the larger economy. To be free —”
Similar changes can be made in education and employment.
Thanks to Jon Husband for inspiring this little manifesto.
Stowe Boyd and I had an email conversation a few weeks ago, which is now posted on his Socialogy site:
[Stowe] The thesis of Socialogy is that scientific findings about sociality, social networks, and human cognition are only slowly becoming part of management thinking, and as a result, much of what goes on as established practice in business is actually folklore dressed up as policy. Where do you see the greatest point of leverage in the application of scientific understanding of social connection in business?
…
[Harold] Cognitive science, anthropology, bioeconomics and other sciences may be the long lever, but visualization [with tools like social network analysis] is the fulcrum to widespread understanding of social connection in business.
As any marketing professional knows, ideas don’t spread themselves, they need to be in a form that first gets the recipient’s attention. Dan Pink talks about this with his six types of sales pitches, giving the same message in different ways. I have found that the visualization that social network analysis provides can be very powerful, and network thinking can fundamentally change our view of social connection in business. Seeing is believing. Visualizing network relationships can give the initial leverage of getting complex new ideas accepted into general management thinking.
For example, I once used value network analysis to help a steering group see their internal community of practice in a new light. For the first time, they saw it mapped as a value network, not a hierarchy. They immediately realized that they were pushing solutions instead of listening to their community. This was obvious when all arrows pointed toward the user community, but no tangible or intangible value arrows pointed out. As a result, they decided to change their Charter and develop more network-centric practices. Thinking in terms of networks enabled them to see with new eyes.
Map of my LinkedIn connections, by LinkedIn Labs
It’s Learn @ Work week in Canada. A related article in the HuffPo states that, “Simply put, a culture of learning is nothing more than workplace leaders providing opportunities for learning in a supportive environment.” Is that really it?
For me, it’s never “Learn @ Work” week. It’s always, “Learning is the Work” week.
Thinking of learning as something additional to work is plain wrong in a knowledge-based, creative, networked society and economy.
It is not enough for workplace leaders to merely “provide opportunities for learning”. They need to model learning themselves. But it’s not just about those in leadership positions, as networked organizations need everyone to think and learn for themselves.
Organizational resilience is strengthened when those in leadership roles let go of control, because leadership in networks does not come from above, as there is no top. Leadership is an emergent property of a network in balance and not some special property available to only the select few. As networks become the dominant organizational principle, networked learning is essential to do any work of value. A real learning organization requires leadership from everyone – an aggressively intelligent and engaged workforce, understanding that:
It can sometimes be difficult to see oneself as a node in multiple networks, as opposed to a more conventional position within an organizational hierarchy. We have become used to titles, job descriptions, and other institutional trappings. But network thinking can fundamentally change our view of hierarchical relationships.
For example, I once used value network analysis to help a steering group see their community of practice in a new light. For the first time, they saw it mapped as a network. They immediately realized that they were pushing solutions instead of listening to their community. As a result, they decided to change their Charter and develop more network-centric practices. Thinking in terms of networks can enable us see with new eyes.
Managing in Networks:
Here are some recommendations for organizations moving to more networked and creative work.
Learning in Networks:
As we learn in digital networks, stock (content) loses significance, while flow (conversation) becomes more important – the challenge becomes how to continuously weave the many bits of information and knowledge that pass by us each day. Conversations help us make sense. But we need diversity in our conversations or we become insular. We cannot predict what will emerge from continuous learning, co-creating & sharing at the individual, organizational and market level, but we do know it will make for more resilient organizations.
Networked Professional Development:
A professional learning network, with its redundant connections, repetition of information and indirect communications, is a much more resilient system than any designed development program can be. Redundancy is also a good principal for supporting social learning diffusion. There is always more than one way to communicate or find something and just because something was blogged, tweeted or posted does not mean it will be understood and eventually internalized as actionable knowledge. The more complex or novel the idea, the more time it will take to be understood.
Programmers often say that you are only as good as your code. Credentials and certifications often act as blinders and stop us from recognizing the complexity of a situation. As Henry Mencken wrote, “For every complex problem there is an answer that is clear, simple, and wrong.”
One approach to working smarter starts by organizing to embrace diversity and manage complexity. Diversity is a key factor in innovation and there are few organizations that do not want to improve innovation.
At the Connected Knowledge Lab, we offer a place and time to develop network skills. Our next event will focus on building a professional network, providing resources and feedback for anyone interested in getting started. Our workshops are designed to give just enough structure, without constraining personal and social learning, all at a reasonable price.
There is a lot of knowledge in an organization, some of it easy to codify (capture), and much (most) of it difficult to do so. Understanding how best to commit resources for knowledge-sharing should be in some kind of a decision-making framework that is easy for anyone to understand. This is a first attempt to do that.
[This post is a follow-up from my building institutional memory post].
Brian Gongol made an interesting observation on three categories of institutional memory. Decision memories are probably the most important, and likely the most open to rationalization in hindsight. The good decisions always seem obvious after the fact.
event memories, which are things like the construction of new facilities or the arrival of new employees
process memories, which note how things are done in order to save time and ensure their reliable repetition in the future
decision memories, which explain how the institution chose one path or policy or course of action over another
We can expand these three categories with Ewen La Borgne’s observation on the types of artifacts left by work projects. Outputs are quite explicit, while expertise is mostly implicit knowledge. Networks can be mapped, and are therefore explicit, but interpreting them requires implicit knowledge.
Information and outputs produced
Expertise (knowledge and know-how)
A network of connections
Put all of these together in order of difficulty in codifying memories/artifacts and the following graphic is my working interpretation. Explicit knowledge is easier to codify and more suitable for enterprise-wide initiatives, while implicit knowledge requires personal interpretation and engagement to make sense of it. Note that these six categories only serve as examples and are not a complete spectrum of knowledge representations.
So what types of knowledge management (KM) frameworks could help us support the codification of these knowledge artifacts? One way to look at it would be from a perspective discussed by Patti Anklam a few years back. Patti explained the differences between Big KM, Little KM and Personal KM and this distinction could be useful. Big KM is good for knowledge that can be easily codified, and Little KM can provide a structure for teams & groups to try out new things (in a Probe-Sense-Respond way). PKM puts individuals in control of their sense-making, but the organization can benefit from this by making it easier for workers to share knowledge.
Finally, there are certain types of tools and and platforms that would be more suitable for sharing of each type of knowledge artifact. I describe only a few in this image, but it gives an idea of how one could structure a full spectrum of knowledge-sharing in order to support institutional memory.
From here, one can now ask what types of platforms would help to codify and share the knowledge that is important to any organization. For larger organizations, all three types of KM are most likely necessary. Too often, Big KM is seen as sufficient, but in complex work environments, Little KM and Personal KM are also needed and should work in conjunction with Big KM. These are three important pieces, that should remain loosely joined in order for each to do what it does best.
Institutional memory, which I wrote about recently, is a mixture of explicit and implicit knowledge sharing. It can be as explicit as Harvard Business School’s Institutional Memory site, or as implicit as the feeling one gets from a well-known local legend. A lot depends on what the organization wants to preserve. Is it how-to knowledge, like a trade secret formula, or is it certain practices and norms that define the culture? Or is it both? Each institution has to define this for itself.
Implicit knowledge is difficult to share and is usually complex. We know that this type of knowledge cannot easily be codified. However, it’s often what gives institutions sustainability and even competitive advantage. Finding ways to collect and share both types of knowledge is important for institutional memory. Stories can be an effective medium for these exchanges. The Ritz-Carlton provides an excellent example with Stories that Stay with You. Stories do not have to be exceptional to be effective, and simple anecdotes may be better on a large scale, rather than sweeping epics, or one can wind up in the uncanny valley of business storytelling.
Institutional memory is a close cousin of knowledge management. Both can be strengthened with a firm foundation of personal knowledge management (Seek-Sense-Share). While seeking and sense-making are mostly individual activities and people should be allowed to use what’s best for them, the organization can overtly support knowledge sharing. One suggestion is to create more opportunities for “people to have coffee together”. Though it’s not the coffee that’s important, the act of gathering, combined with an environment that encourages capturing and sharing knowledge artifacts, serves to build institutional memory.
In The Storytelling Animal, Jonathan Gottschall tells us how stories make us human. The book looks at gender differences in weaving our own stories, the cultural significance of stories, and some of the science and pseudo-science on story, narration and memory. It boils down to a simple formula, says Gottschall.
Story = Character + Predicament + Attempted Extrication
This made me consider how this could be important for institutional memory. Would this be a good formula to try to capture past events from those who have experienced them? It could be, but it might be highly dependent on how much time has passed and how important accuracy is, as we are not very good at remembering, especially critical, or ‘flashbulb’, events. “Memory isn’t an outright fiction; it is merely a fictionalization“, says Gottschall.
“The signature flashbulb event of our age is 9/11, which led to a bonanza of false-memory research. The research shows two things: that people are extremely sure of their 9/11 memories and that upward of 70% of us misremember key aspects of the attacks … In one study, 73 percent of research subjects misremembered watching, horrified, as the first plane plowed into the North Tower on the morning of September 11.
The research shows that our memories get worse over time, but our stories, as we remember them, become much clearer. We have a propensity for self-delusion, something every jury member should always keep in mind. But fiction (story) is much more powerful than non-fiction. Gottschall discusses the power of Wagner’s mythology on Hitler, as well as how the book, Uncle Tom’s Cabin, influenced the 19th century anti-slavery movement.
“When we read nonfiction, we read with our shields up. We are critical and skeptical. But when we are absorbed in a story, we drop our intellectual guard. We are moved emotionally, and this seems to leave us defenseless.”
Consider the above statement and think about training. Would it not be more effective if content was developed as stories? How about knowledge management? I think stories would be most effective for new hire training. Perhaps we should focus less on instructional design or knowledge repositories. Instead, organizations could engage good story tellers. We hear a lot about the importance of curation in the digital workplace today. The best curators are also story tellers.
I enjoyed this book and learned a fair bit from it, but it is not a book that deals much with how stories can be used for KM or other organizational purposes.
Roger Schank has several interesting articles posted on his site in the Corporate Memory section, which I decided to dive into recently.
In The Future of Knowledge Management, he says that the main problem with KM systems is that they do not copy how real people think and that unlike a person, a “KM system simply gets slower as a result of more information”. He proposes creating software scripts to organize information, but these must be capable of self-modification. I have not seen any systems that really do this well, yet. Schank concludes:
There is a lot of knowledge in an enterprise that can be used to organize new knowledge that is coming in. People understand new knowledge in terms of what they already know. A smart KM system must know a lot of about an industry and a particular enterprise before it starts up. This is hard but by no means impossible. And it is the future of software – namely software that really knows a great deal about your business.
Until these types of systems are available though, I would encourage individuals to practice personal knowledge management and use enterprise social networks to share within the organization. It may not be as elegant, but I know it can be implemented today, with existing technologies and skills that can be developed by anyone.
Algorithmic search filters that can push things out, based on certain criteria are what Schank calls “Information that Finds You”. Add geo-location and you can get immediate feedback on things around you. These exist, but take time to setup and maintain. In organizations, providing coaching and support on how to optimize our software & hardware tools (our outboard brains) is often lacking. Not only is there a need for a learning concierge but also a basic digital concierge, so that we can use our tools optimally. For instance, even doing an advanced online search query is beyond the grasp of most people on the Net.
Schank also writes about the need for a Reminding Machine, which is based on the premise that knowledge is best communicated just in time.
A reminding machine has thousands of stories from experts in various areas of life telling about important aspects of their lives that have lessons about life in them, the kind of stories you might tell to colleagues or to students … In order to build this machine it is necessary to collect people’s stories and index them according to the goals and plans that a story instantiates.
In his keynote at DARPA in 2010, Schank discusses story telling and KM in great detail. Here are some highlights
There are 12 Fundamental Cognitive Processes, according to Schank:
* These processes are what Schank calls “The Big Three”.
Several examples of the 12 processes are presented as stories in the second video of the keynote.
For anyone interested in institutional memory, story telling, or knowledge management, all four videos are well worth watching. Roger Schank concludes that the most difficult part in all of this is actually collecting the stories. The best people to collect stories from are those who are able to admit that they mismanaged, botched, or bungled something. This can be a real challenge in organizations that do not discuss failure.