Cooking out loud

The simple structure of the company, with its solidly embedded organizational chart restricting knowledge flow, cannot deal with the complexity of the networked economy. It takes too long to make decisions or try new things out. Looser hierarchies and stronger networks are required, but how do you go about this?

Working and learning out loud are essential practices that can change the nature of work. They help make transparent what is happening in the organization and democratize knowledge creation. First of all, everyone must be engaged in observing their environment. Then groups of people can work on problems together and learn as they work. The results of working and learning out loud can then be codified as network knowledge, which is always open for modification, as knowledge flow becomes knowledge stock. PKM – Seek > Sense > Share – is a core part of enabling knowledge to flow, unrestricted by hierarchies.

flow to stockImagine a community of explorers in a new land. There are many cooks who try out new recipes, testing to see what tastes good or goes well together. As they cook in groups to feed their families or part of the community, they talk and share their latest work. The cooks have a friendly competition to see who can come up with the most interesting meal. They have to prepare three meals a day, but each day is different, as the situation changes and new foods are discovered. Some of the recipes are popular, or especially good during certain seasons, so recipes are informally published. These are shared throughout the community and with travelers passing through, who also have their own recipes and bring new spices. In such a complex adaptive environment, working and learning together just makes sense.

cooking knowledgeImagine if this community instead had a single chef and a team of food preparation specialists that reported directly to him. He would decide what to cook for the community kitchen. Weekly reports from community leaders would be collated by the chef’s staff, reviewed by the chef, and would inform the next week’s menu planning. Certain recipes would be published annually in the official community cookbook, certified for general use. Imagine how long it would take, and how much knowledge would be missed, with such a structure. Imagine how many wonderful recipes would not be created. The community members would be merely passive consumers of food, disconnected from the environment that nourishes them. Well, this is what happens with knowledge in most organizations today.

Learning Engineering

Last week I had the opportunity to attend the inaugural meeting of the Global Learning Council.  While not really global in either sense (little representation from overseas nor from segments other than higher ed), it was a chance to refresh myself in some rigor around learning sciences. And one thing that struck me was folks talking about learning engineering.

If we take the analogy from regular science and engineering, we are talking about taking the research from the learning sciences, and applying it to the design of solutions.  And this sounds like a good thing, with some caveats.  When talking about the Serious eLearning Manifesto, for example, we’re talking about principles that should be embedded in your learning design approach.

While the intention was not to provide coverage of learning science, several points emerged at one point or another as research-based outcomes to be desired. For one, the value of models in learning.  Another was, of course, the value of spacing practice. The list goes on.  The focus of the engineering, however, is different.

While it wasn’t an explicit topic of the talk, it emerged in several side conversations, but the focus is on design processes and tools that increase the likelihood of creating effective learning practices.  This includes doing a suitable job of creating aligned outcomes through processes of working with SMEs, identifying misconceptions to be addressed, ensuring activities are designed that have learners appropriately processing and applying information, appropriate spread of examples, and more.

Of course, developing an accurate course for any topic is a thorough exercise.  Which is desirable, but not always pragmatic.  While the full rigor of science would go as far as adaptive intelligent tutoring systems, the amount of work to do so can be prohibitive under pragmatic constraints.  It takes a high importance and large potential audience to do this for other than research purposes.

In other cases, we use heuristics.  Sometimes we go too far; so just dumping information and adding a quiz is often seen, though that’s got little likelihood of having any impact.  Even if we do create an appropriate practice, we might only have learners practice until they get it right, not until they can’t get it wrong.

Finding the balance point is an ongoing effort. I reckon that the elements of good design is a starting point, but you need processes that are manageable, repeatable, and scalable.  You need structures to help, including representations that have support for identifying key elements and make it difficult to ignore the important elements.  You ideally have aligned tools that make it easy to do the right things.

And if this is what Learning Engineering can be, systematically applying learning science to design, I reckon there’s also a study of learning science engineering, aligning not just the learning, but the design process, with how we think, work, and learn.  And maybe then there’s a learning architecture as well – where just as an architect designs the basic look and feel of the halls & rooms and the engineers build them – that designs the curriculum approach and the pedagogy, but the learning engineers follow through on those principles for developing courses.

Is learning engineering an alternative to instructional design?  I’m wondering if the focus on engineering rather than design (applied science, rather than art) and learning rather than instruction (outcomes, not process), is a better characterization.  What do you think?

Science of Happiness MOOC

Along with 100,000 other people, I’ve enrolled in a free MOOC on The Science of Happiness.

UC Berkeley’s Greater Good Science Center, is producing the course. It’s being administered on the EdX platform

Week 1: Introduction to the Science of Happiness
Will be available starting on September 9 

Week 2: Happiness & Human Connection
Will be available starting on September 16 

Week 3: Kindness & Compassion
Will be available starting on September 23 

Week 4: Cooperation & Forgiveness
Will be available starting on September 30 

Week 5: Midterm Exam (and time to catch up on course material)
Will be available starting on October 7. Must be completed by November 18. 

Week 6: Mindfulness, Attention, and Focus
Will be available starting on October 14 

Week 7: Mental Habits of Happiness: Self-Compassion, Flow, and Optimism
Will be available starting on October 21 

Week 8: Gratitude
Will be available starting on October 28 

Week 9: Finding Your Happiness Fit and the New Frontiers
Will be available starting on November 4 

Final Exam
Will be available starting on November 4. Must be completed by November 18.

I’ll post about my experiences as we march through the material.

“The Science of Happiness” is being produced by the Greater Good Science Center (GGSC) at the University of California, Berkeley; course co-instructors Dacher Keltner and Emiliana Simon-Thomas are the GGSC’s founding director and science director, respectively. The GGSC is unique in its commitment to both science and practice: Not only does it sponsor groundbreaking scientific research into social and emotional well-being, it also helps people apply this research to their personal and professional lives. “The Science of Happiness” is a prime example of the GGSC’s work.