There are two main models of learning: single and double loop learning. Single loop learning is the way we have been taught to acquire new information all throughout our schooling years. This is the method that simply involves performing certain actions (for example, rote learning) to achieve certain outcomes (doing well in examinations). Here, the emphasis isn’t on the learning itself, but the purpose for which we are learning.
One major drawback of single loop learning is the way it handles errors and mistakes. If you make one, there isn’t really a good way to resolve that besides simply doing something different (say, rote learning more effectively). We never consider the underlying causes of our errors, instead dealing with them only on a superficial level.
This is where double loop learning shines. It’s a method of information synthesis and processing. Though harder and more involved than single loop learning, it is substantially more effective at facilitating learning. Here, we constantly utilize feedback and our own introspection to evolve the ways in which we learn. We repeatedly question the methods and steps we follow, as well as why we’re following them in the first place. Instead of simply scoring well in examinations, we learn for the sake of learning, which in turn helps us generate curiosity for our subject matter. This results in holistic learning, which helps us achieve our initial goal of scoring well too.
- Rapid Knowledge Acquisition & Synthesis: How to Quickly Learn, Comprehend, and Apply, and Master New Information and Skills (Learning how to Learn Book 11)
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The Double Loop Framework
The double loop model of learning is not so much a fixed technique as a shift in perspective or mindset. It’s a new approach to taking in, synthesizing and retaining the information we want to learn, whether that’s a new motor skill, a language, or academic material for an important exam. And it’s one that is necessary for the goals of this book.
To make it work, we need to be willing to abandon the old, conventional learning models that we all learned in school. Though we may gravitate toward these out of habit, we need to constantly remind ourselves that the knee-jerk way of doing things is not necessarily the most effective or efficient. The way we’ve done things, the assumptions we’ve held, and everything up until this point could be wrong.
The double loop learning framework makes you embraces this for better knowledge acquisition and comprehension. There’s a reason that many athletic trainers would prefer to train someone starting from scratch rather than someone with a little bit of prior experience; that prior experience or knowledge can lead to skewed beliefs or habits.
Again, we need to get back to basics and change our definition of learning. Typically, we think of learning like a predictable ladder—one step at a time. School coursework is designed like this: you finish one level and then move onto the next, block by block. A, B, C, D, and so on in a forward, linear fashion.
But there’s an alternative to this linear model—a circular one in which A leads to B, B leads to C, but then C can also lead back into A again. In a linear model, you can only advance or fall down (reminds you of the fixed mindset and its conception of failure, doesn’t it?) but in a loop you are always in process, always learning (i.e. working within a growth mindset and having “beginner’s mind”).
In linear models, learning establishes a hierarchy—twelfth graders know more than tenth graders, and bosses know better than their employees. In a loop model, people only compete against themselves, and everyone is merely at a particular point in their process, which is neither better nor worse than any other point.
In a learning loop, you can always improve, and in fact you can sustain yourself forever, continually developing skills and knowledge using constant feedback and starting at the “beginning” again. From this point of view, there is never really any finish line or big prize at the end. Learning is more like an ongoing way of life, it just requires you to honestly assess yourself from time to time.
Consider a researcher who finds some interesting correlations between two diseases that were previously assumed to be unconnected. One stage of her learning is to conduct experiments on participants she’s gathered. She publishes her findings and her work is read by another professional from an entirely new field—and he has some interesting findings of his own to share. The researcher takes this new data and devises another experiment to test fresh hypotheses, learning more and more from other colleagues weighing in on her original publication…
Here, there is no end goal, no point at which the researcher can say she’s finished, or has reached the top of the pile. Instead, her learning inspires yet more learning, her questions spur further questions, and she is taking part in a growing and evolving process rather than a simplistic journey from A to B.
Single and double loop learning
Simply being in a loop is not necessarily all that useful, however. After all, you could be repeating the same error over and over again, or engaging in a feedback loop that only compounds and amplifies any mistakes you made the first time around. Understanding this allows us to see the difference between single and double loops.
Consider this (simplified) learning loop:
Step 1: Lift heavy weights at the gym.
Step 2: When a weight becomes too easy to lift, move up to a heavier weight. Return to Step 1.
Following this protocol, you will most likely end up gaining muscle strength and becoming more fit at the gym. At the very least, you’ll ensure that you’re always lifting the heaviest weight you possibly can. But compare it to the following:
Step 1: Lift heavy weights at the gym.
Step 2: When a weight becomes too easy to lift, move up to a heavier weight.
Step 3: Track your progress and ask why you’re advancing—or not.
Step 4: Identify possible impediment to advancing with heavier weights—consider time of day when training, diet, supplements taken, hydration, mood and recovery time.
Step 5: Isolate each factor and run experiments—i.e. does having fewer rest days actually make training more difficult?
Step 6: Adjust schedule according to findings from Step 5.
Step 7: Return to step 3 and repeat.
The above contains more than one loop, and these feed back dynamically into one another. This is better learning in a nutshell. It is harder, more involved, and leads to undoubtedly better outcomes.
Professor Chris Argyris at Harvard Business School explains that double loop learning, as you can see, is more complex.
In this learning mode, you are constantly zooming in and out of the process, adjusting, factoring in and re-appraising, shifting mental models depending on what works, and so on. Argyris believes that we all have mental maps or cognitive schemas that we work from whenever we learn something, but we can become more or less conscious about which ones we use and how.
To put it simply, a single loop has you take an action, see the results, and feed back that action into the first step. When you engage in double loop thinking, however, you spend extra time considering the mental models and frameworks you are operating within, looking carefully at how they inspire your actions, and in turn the results you’re getting.
Any time you learn to learn, or investigate the way you ask questions, or evaluate the outcomes of your evaluation technique, then you are adding that extra layer of complexity that gives more insight into what is actually happening on a deeper level.
Single loop learning is fine if you’re a machine, but it has its weaknesses. It’s a fixed process. If there’s a problem, there’s no real way to see that there’s a problem, or respond to it. The only choices are to stop or carry on.
In some cases, we can respond to problems that crop up in single loop learning mechanisms. We take an action, find that the result doesn’t match our expected outcome, make some changes to the action, and hope for a better result. However, with this method of rectifying errors we only end up working on symptoms, while the root cause of those mistakes is left unnoticed.
Following this model tempts us into believing that if only we modified our own actions a little, our results would be better. In reality, the number of factors affecting successful learning are far greater than what single loop learning allows for.
With double loop learning, however, you have the opportunity to question underlying assumptions, and the chance to fix and improve them for better outcomes. You are no longer blindly acting, but consciously designing the most optimal way of proceeding. To connect this idea to those we explored in the previous chapter, you might notice that a fixed mindset or a need for control encourages single loop learning, whereas a growth mindset or one founded on genuine curiosity is more likely to lead to double loop learning.
It’s simple: fear of failure, craving control and certainty, ego, not wanting to appear wrong or stupid, intolerance for the unknown or for being in process… all of these establish a mental model that, when unexamined, puts you on a single loop path that won’t necessarily lead to improvement.
Double loop learning is harder. It requires time, patience, and humility to dismantle mental models that aren’t working for you. Most people would prefer to stay firmly inside their mental model and assume that it’s all there is, and solve their problems from within this deliberately limited perspective.
Some of the hardest work in this area is acknowledging that you are in fact inhabiting a mental model at all. The most difficult perspectives to shift are those that you simply experience as reality itself, as natural, inviolable laws that you have never stopped to consider alternatives to, or even the possibility of alternatives.
Whenever we face a problem or some novel situation in life, we can deal with it based on our previous experience. However, the very essence of learning something new is doing and discovering what you didn’t do or know before! If you approach new information or situations using only the same old rules you’ve learned from the past, you risk oversimplifying things, or missing enormous aspects of what’s in front of you.
Addressing the problem from our static inventory of mental tools is a necessary first step, but if we hope to make qualitative leaps in these skills themselves, we can’t help but focus on our learning methods, and ask how and whether they’re working.