What if surgeons learned the skills necessary to operate in the same way we attempt to develop problem solving skills in our people?
“Good morning. My name is Dr. Gross and I will be operating on you today. Don’t worry, you’re in good hands. My day job is in accounting, but I was recently hand-picked by my management to pursue a Green Belt in surgery because I’m told I’ve got ‘potential’. I have completed more than two weeks of classroom training where I learned from some of the most expensive surgical consultants in the world. Rest assured, I am willing and able to use each and every tool in the surgical toolbox.
Today, I’ll be using the standard, 5-step surgical framework known as DMAIC, which stands for Don’t-Move-And-I’ll-Cut. Unless, of course, something goes wrong in which case I’ll insist that we follow PDCA – Please-Don’t-Call-Attorney! That’s just a little surgical humor. You look tense. Shall we get started?”
Developing strong problem solving skills throughout the entire organization is critical to building a lean culture. Yet, most organizations never come close to achieving this “everybody, every day” ideal. One reason is that most of us know little about how people develop complex skills like problem solving. How learning works is a black box in which we find ourselves feeling around in the dark, looking for a way out.
Thankfully, cognitive development scientists do know something about this. Research illuminates our core learning mechanisms for processing new information and developing new skills. We can think learning as something we’re already quite comfortable with in the lean community: as a process that can be continually refined and improved over time.
Informed by the work of Dr. Monisha Pasupathi (The University of Utah), what follows is a simplified, but highly useful model I use for how we as humans transform new information into knowledge and new capability:
Step 1: Encoding of New Information
In this first step, our brains capture sensory information – what we hear, taste, smell, feel, and see – and encode it for short-term storage in a part of the brain known as working memory. This temporary stop on the learning path allows us to work with the new information. The degree to which we retain this new information depends on two factors: 1) the extent to which we can connect that information to what we already know, and 2) the degree to which we expect to need it in the future.
The theory that our minds are a blank slate, a ‘tabula rasa’, is a myth. Without anchoring new stimuli to our existing knowledge, there is no learning. But when the learning process is focused upon the actual problems that we face in the familiar context of performing our work, the more connections our brains can make to it and the more “sticky” that information becomes.
Simply making connections, however, is not enough to retain information beyond working memory. Our brains must believe we’ll have a future need for this new information. Learning that occurs passively, without our engagement, like most forms of classroom-based learning, rarely ever convinces our subconscious self that that information needs to be retained. However, by elaborately encoding new information – actively thinking about how new information will be used – we increase the degree to which we retain information long-term.
Step 2: Recall Learned Information
Storing new information of course is not enough; we must be able to recall new information when we need it. Optimizing our recollection begins before information is ever encoded.
The Generation Effect shows that our recollection improves significantly when we consciously set expectations about an outcome or a new piece of information, versus having new information simply dictated to us and trying to absorb it. When we make a prediction, like establishing a standard, we are able to compare our current understanding to our perception of reality. Any differences help make visible the limits of our understanding. We then update our mental models accordingly, increasing our ability to recall information in the future.
A second way in which we can improve our brain’s ability to recall information is to ensure that the original encoding is transfer appropriate. We are most likely to recall prior knowledge when it is learned in a manner similar to how we will use that information in the future. For example, learning about problem solving from a book or in a class doesn’t usually help us develop problem solving skills. For this, we need to actually practice solving problems. Similarly, improving our proficiency with PDCA requires practice in the form of planning, doing, checking ,and acting. It’s a slight difference, but an important one:
Step 3: Re-Encode to Deepen Understanding
Whenever we recall information, we’re presented with an opportunity to strengthen our mental connections and refine our abilities to apply what we know. Again, the difference between our predictions and perceptions allow us to update our mental models by re-encoding new information made visible to our brains. Repeated recall and re-encoding is what allows us to develop expertise.
Maximizing learning is a matter of maximizing the number and diversity of ways we connect with learned information. The number of times we recall and apply what we know is what helps us learn. How that repetition occurs has a big impact on the effectiveness of the re-encoding process.
It’s important that the repetition is spaced apart in time. The establishment of habits and routines that we practice in a repeatable, consistent fashion – such as leader standard work or A3 management – makes it clear to our brains that this information will be vital for future application.
Skill development (as opposed to knowledge development) requires practice in order to re-encode the breadth of connections needed for proficiency. Skill-based learning can’t be achieved passively; it requires active practice with consistent, meaningful, and timely feedback from an outside source (i.e. a coach).
Moreover, in the same way that playing catch in the backyard may not improve our ability to catch pop flies or ground balls, developing a new skill that is variable in nature, such as problem solving, requires practice under variable conditions. In other words, we develop a strong foundation not by the sheer number of problems that we solve, but by the breadth and diversity of those problems.
Implications for Effective Problem Solving
As the title implies, skill-based learning is harder than you think. To summarize:
Maximizing skill-based learning requires that we connect new information to what we already know, elaborate about how that information will serve us in the future, and use that knowledge to generate new expectations about our world. We then need to actively test our understanding through repeated application in the presence of clear feedback spaced across time under varying conditions.
Unfortunately, most of us try to learn and attempt to help others learn using the traditional, classroom-centric, tool-based model of skill development. The one that emphasizes the certification of specialists with limited real-world, hands-on, problem-solving experience. Real lean thinking and practice requires something different.
So, how do we use what we have learned about learning to improve how we develop organizational problem solving skills? I can tell you what I think, but as we now know, this doesn’t help anyone learn anything! So, what do you think? How have you applied these ideas in your organization? How will you change your approach based on what you have learned?
Tell us by using the comments section below!