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Envisioning the deep learning enterprise

This article appears in the issue February 2015 [Vol 24, Issue 2]


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We are well into what might be referred to as the knowledge transfer decade, brought about by the wave of retirements and ensuing panic as organizations scramble to capture, retain and grow their critical knowledge. Two consistent drivers accompanying this wave are increasing speed and complexity both within the enterprise and in the global marketplace. As organizations attempt to accelerate their knowledge transfer efforts, they quickly discover that those two drivers are at odds with each other.

Shallow vs. deep structure

Sponsors of knowledge transfer tend to demand not only speed but also simplicity. That is evidenced by the constant pressure to encapsulate knowledge into bite-sized, memorable “nuggets.” For example, a top portfolio manager in the financial sector might give an apprentice a set of catchy phrases like: “When volatility is high, we buy; when volatility is low, we go. Now get to work!”

The apprentice applies the rules and does rather well. That is, until either the rules no longer work or external conditions change so wildly and rapidly he or she can’t decide which course of action to take. This is because the underlying sense that created the rules is missing.

The expert, now long gone, possesses something innate that didn’t get imparted to the novice—the capacity to deal with novelty. The expert knows when the rules work and when to break them. But ask that same expert to explain how to tell the difference, and the response usually goes something like: “I really don’t know why I know, or how I know … I just know.”

In the crunch for time, experts are often forced to give “CliffsNotes” versions of what they know. Such oversimplification fails to consider the stratified deep structure involved in human sense making. Breaking the shallow learning barrier means transferring not only the rules but also the underlying processes generating the rules. That can only occur through repetitive cycles of observation, self-directed inquiry and self-discovery. Recent advances in the field of neuropsychology give us a better understanding of how to tap into those processes.

Enhanced knowledge transfer

When an expert knows something but can’t explain it, that’s a sign that he or she is operating at the engram level. An engram is the most fundamental element of memory. Each engram taken alone makes no sense. But according to neuropsychology, the “capacity” to deal with novelty comes from the expert’s ability to observe external phenomena, break the observation down into engrams, store them in some way (e.g., as memory), reassemble them into a proper situational assessment and formulate alternative courses of action.

The military calls this the “OODA loop” (observe-orient-decide-act). Winning generals and strategists have mastered it, usually after years of war gaming, being mentored, and if the opportunity presents itself, direct experience in combat and/or crisis situations.

So how can you apply it to your own knowledge transfer efforts? Picking up from where we left off in a previous article on knowledge transfer mentoring (kmworld.com/Articles/Column/The-Future-of-the-Future/We-must-all-be-mentors-now-92696.aspx), here are some simple steps you can take to get started down the road to breaking the shallow learning barrier:

  • Have the mentor/expert build a list (or better yet, a visual map) of topics relevant to the problem domain. Ideally, topics should be arranged into a learning space, with an imaginary boundary separating the topics the mentee knows from the topics the mentee needs to learn.
  • Have the mentee/apprentice select a topic to be learned and give that topic a name. In a subtle way, naming imparts a sense of ownership and unconsciously starts the process of self-inquiry, a key component of deep learning.
  • For the selected topic, have the mentee explain it in the form of a written narrative. Handwritten notes have proven to be far more effective at stimulating deep learning than typing, or worse yet, passive listening.
  • Have the mentee write a completely different explanation/illustration of the same topic (recall our discussions in previous articles on the importance of introducing varying perspectives). Repeat until the mentor/expert is satisfied that the full dimensionality of the topic has been enumerated.
  • Have the mentee write a summary narrative about the topic and its relationship to other topics in the learning space.

The process of guided self-discovery applies a principle borrowed from mathematics: Knowledge about a topic is canonical if and only if it is orthogonal and complete. Like the portfolio-balancing rule mentioned earlier, information passed along in tweets, rapid-fire Q&A, e-mails and many other so-called attempts at knowledge transfer is anything but canonical. The five steps outlined above are designed to achieve the orthogonality and completeness found lacking in shallow learning approaches.

Another important element is that deep learning creates long-term changes in the individual. Such changes are generally positive because experiential learning improves long-term retention, allowing the learner to grow more confident and enhance his or her self-image. This has the potential to be truly transformational.

Insights from math education

Shifting responsibility for learning from the mentor/expert to the mentee/apprentice is reflected in an awakened sense of ownership and the responsibility to demonstrate knowledge. We’ve observed this transformation in freshman college math classes where students who were given the opportunity to use deep knowledge transfer methods expressed new ways of thinking about mathematics and even about how to study mathematics. The goal was not to create a new mathematics, but rather to remove latent disdain and apprehension typically found among first-year college math students.

As the deep learning techniques were applied both online and in the classroom, many students experienced increased courage and capacity to excel in other academic disciplines as well. We expect that applying those same techniques in the workplace will accelerate breaking down organizational barriers and silos as people begin to understand at a deeper level the interrelationships among the many topics, perspectives and disciplines at work in their organizations.

A new vision for the enterprise

The ultimate goal of the deep learning enterprise is not just to transfer institutional knowledge, but also to increase the organization’s capacity to perceive and respond to risks and opportunities. The basic approach is to know and continually expand your organizational topic space by making the five steps mentioned earlier an integral part of your KM efforts. Use your infrastructure to communicate ideas and insights that coalesce around those topics.

Think about the richness of that approach, especially when used in conjunction with traditional codification techniques such as rules, checklists and flow diagrams. The capability for continuous innovation and capacity building is truly exciting. We encourage you to start finding ways to incorporate this new, expanded dimension of learning into your organization.


 

 


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