Hack:
Unlock the Value of Learning Labor in Your Organization - quickly before someone else does

Let’s look at some examples.

Some years ago I had the opportunity to train engineers at a leading truck manufacturing company.  The engineers needed to learn procedures for troubleshooting engine issues. The course involved learning basic concepts about engines and how to reason diagnostically.  Much of the learning involved studying decision trees and then constructing new ones to solve problems they would encounter on the floor. We used a low-cost expert system shell to have trainees code, test and debug their decision logic.  They loved the software because it was easy to use, interactive, visual and gave them immediate feedback on their thinking and learning process.

They structured content by designing decision trees and testing rules using a knowledge editor to drive their learning. After running the class for a while I noticed we were building a library or online knowledge base with a good number of rules. As an instructor, I could easily direct how this knowledge based evolved by which problems or cases I assigned specific students or classes.  In short order, the students were able to build out a small and useful “expert system” that could be used on the floor and in related departments to help troubleshoot engine issues.  The training/learning process created structured content that had immediate business value.

And as things changed on the floor – new equipment, engine features/models, quality control procedures – we would adjust the training program and that would automatically update the expert system (within limits).   This was an exciting result because maintaining expert system applications can be very costly and this fact has limited their use.

I was using the process of knowledge engineering (building expert systems) as the primary learning method in the class.   The job of a knowledge engineer is to restructure human knowledge for computer use.  To do that you must do a lot of learning about the domain. Students learn by teaching a machine.  In effect, the method involves the co-education of people and machines.

In this case they taught the machine by coding and testing rules that capture the diagnostic logic of troubleshooting truck engines.  When I say “coding and testing” I don’t mean students had to do computer programming. This is not learning by programming. Instead they used a knowledge editor, much like a visually enhanced word processing editor, to type in and run the rules.  Having such editors (and ultimately simpler tools) is an essential ingredient for learning by teaching a machine.

Others have recognized the potential of this approach.  In his article, The Student as Knowledge Engineer: A Constructivist Approach to Science Education, Ralph Morelli discusses an experiment  where junior high school students learn how to do botanical classification.  Students achieved the learning goal by building an “expert advisory system capable of identifying tree specimens from a description of their gross morphology..

The differences in the examples reveal the broad applicability of the method.  In one case we have professionally trained engineers working on trucks and in the other we have junior high students doing tree classification.

Many applications are possible. For example, using this approach when we teach anatomy, physiology and other health science and medicine related courses we could generate large-scale rule sets  that would help make our electronic medical records more intelligent.  Or  we could tap history, geography and sociology classes to craft the knowledge needed to take Google maps to the next level.  I’ve outlined a few of the small and large-scale applications that are possible in the document Physics Research Interests included in the materials section below.

Rather than relying on traditional labor in business to do the work of crafting the online content or computable knowledge we need to solve a problem or create a new product or service, we could harness the ready and macro-scale supply of essentially free learning labor to do the work. I say essentially free because the students doing the work and the supporting training and educational institutions have already decided to make the mental and financial investment to structure content for learning purposes. 

And this learning labor wants to be unleashed.   Alan November makes this point vividly in his book, Who Owns the Learning, when he writes:

“From immersing themselves in Facebook and Twitter, to writing their own apps, to creating avatars and their own websites, today’s students demonstrate a huge interest in creating and sharing content.  Socrates was right: learning for many of our students is a social interactive enterprise.”

The book introduces the idea of a digital learning farm and talks about how students and teachers are structuring content not only to learn in their classroom but to teach others.  Students use podcasts, wikis, custom search engines and other methods to curate, structure and share content.  Some of the content is shared on the web and has attracted a global audience of users and contributors. See for example http://mathtrain.tv/

Students want voice, they want their learning efforts to have social and economic  impact that goes far beyond the classroom.   Student-sourcing platforms that aggregate their individual learning efforts into large-scale repositories of structured content can make that happen.

While the examples cited make use of tools and methods that were created for other purposes, the time is right to create new platforms and tools designed explicitly for learning by creating digital content with economic and social importance.

One example I am working on is a platform for student-sourcing knowledge cards briefly described below.

Some of the training and education we do is focused on soft-skill development not hard-skills. For example, we learn how to communicate more effectively or influence others (soft), not how to troubleshoot trucks or build a spreadsheet (hard). How can students and employees master new soft skills while at the same time creating digital content with immediate economic value?

A major challenge in developing new soft skills is that they are based on broad competencies that are fuzzy and hard to take direct action on. For example, one skill involved in developing your capacity to influence others is to be more likeable. Well exactly how do you go about doing that?   There are existing sources (e.g. books and training programs) that explain it well and offer techniques and motivation but they often fall short of producing the behavior change we want.  Such sources offer great general advice, good stories and interesting frameworks but all of that content and knowledge is too broad or abstract to put into the immediate action that drives the learning from experience we need to develop the target skills.

In short, it is difficult to produce good outcomes with soft skills training because the content we have is not structured to support learning from experience and behavior change.  It is possible to restructure the content but that is labor intensive and costly if we use tradition authoring and publishing methods. That’s where student-sourcing comes in.  Students could learn the new soft skills by restructuring the content of existing how-to books and training guides into a format that is more suitable for learning from experience and creating behavior change. This newly formatted content could then be sold as a supplement, summary or even replacement to the original content to produce immediate economic benefits.  

There are tens of thousands of how-to, general advice, self-help and organizational improvement books and soft skill training programs.  Restructuring that corpus of content and knowledge into supplements and summaries that help drive behavior change represents not only a way to improve learning but also suggest a serious entrepreneurial opportunity.

The key is to convert the general advice and complex how-to knowledge found in existing sources into small chunks (call them knowledge cards) that naturally fit into how we learn from experience.  A collection or deck of knowledge cards would then script a small-steps learning experience that over time accumulates into the soft skills we want to develop.

I have developed the methods and some tools for doing this conversion and have used it successfully to teach several soft skills courses including for example, leadership influence, innovation and developing your emotional intelligence.  I am now building the platforms and tools to generate and commercialize additional content as decks of knowledge cards for making behavior change and developing new soft skills.

Initially I adapted generally available software such as the Ning tool for building social networks and Word templates to help students create, use and share knowledge cards.  A bit clunky but it works well from a teaching standpoint.  This has produced 1000+ knowledge cards and has proven that students can collaborate to quickly create publication-quality decks of knowledge cards on a diverse range of topics.  But it offers no way to effectively sell knowledge cards and unlock the economics of the learning labor.  To do so, I teamed up with a former student to develop NewHabits, a free iPhone and iPad app that creates a marketplace for publishing and selling decks of knowledge cards.

The NewHabits app creates a distribution channel for taking the restructured content created through the student’s learning efforts and delivers it to others as a social mobile learning experience.  Social mobile learning is a rapidly growing market today and offers a natural way to generate immediate economic value from learning labor.

Together the templates, Ning site and NewHabits app make up a platform for unleashing the economic value inherent in students learning by restructuring content into deck of knowledge cards.  To jump start the marketplace I published decks on willpower, creativity, influence, observation skills and several other popular soft-skills areas.  Two decks come free with the App and new decks are forth coming on happiness, teamwork, brain health, emotional intelligence and managing money.  So far we have not minted any millionaire students or best-selling student authors but this is a new publishing model and marketplace.

To successfully scale this application of learning labor the tools for creating, sharing, using and selling knowledge cards must be improved.  Ideally a single mobilized web platform can be created that offers the required functionality.  It is a straightforward (but nontrivial) application based on existing mobile, web content management and ecommerce technologies.  

Other uses for the platform have emerged. For example, Jamie and Maren Showkeir have used it to publish a summary of their book Yoga Wisdom at Work: Finding Sanity Off the Mat and on the Job.  The book presents the eight limbs of Yoga and provides stories and insights for how it can be applied to all aspects your worklife.   You play a knowledge card daily from a mobile device to experiment with and eventually master a proven Yoga practice. See the example card below.

Knowledge cards are a new unit for restructuring content to achieve both learning outcomes and economic results in the same production system.  Like business rules, they are an important unit of learning labor in that they scale across many but not all possible applications.  Other examples of units with general utility for learning labor include the tag, case (as in case-based reasoning from artificial intelligence), model and graph.  We need simple low-cost tools (knowledge editors) for building these units of learning labor into our training and education efforts.