Learning new skills, abilities, and mental frameworks is critical to our growth as human beings. We exist in a society where learning is almost mandatory for the first 18 years of our life, and we’re encouraged to continue learning in order to remain competitive in our careers. Given its importance, do we ever take a step back and think about how we learn efficiently and effectively?
Thankfully, psychologists and professors have done some of this for us. In fact, Argyris and Schon (1974) put forward the concepts of single-loop, double-loop, and triple-loop learning to help us understand the basic methods of learning well.
The ‘Loops of Learning’ generally follow three methodologies. The first, single-loop learning, is also known as ‘following the rules’ - it’s a simple cycle of understanding what to do when a particular criteria is met, without the need of expert or outside help. For example, your company might have a smart light that turns off automatically at 5pm. When the light detects that the time has hit 5pm, it carries out the task without question. No further learning is required to perform this step, so it would be considered ‘single-loop’.
In your company, single-loop learning might show up in other ways - especially where there are strict rules and policies. If one of the rules are broken, there is no investigation into why, only a knee-jerk reaction to the violation. Therefore, no further learning is made.
Contrarily, double-loop learning seeks to change those rules. If a violation or disruption to the rule is made, the rule may be reflected on to question its efficacy. New information may have come to surface that impacts the original rule’s viability, or new data may suggest a standard deviation that should be accounted for in that rule. Double-loop learning might show up in the workplace when you miss a deadline. Rather than simply reacting to the unfulfilled expectation, you and the other stakeholders may look at the original timeline and compare it to real data that may have impacted it. For future deliverables of a similar nature, that comparison will assist the people involved in setting a more realistic timeline.
Finally, triple-loop learning takes it a step further - where you learn how to learn better. For example, in the double-loop scenario where you may compare the original timeline against the real timeline, you would have established a process to do so. You and the other stakeholders may have had a meeting where you visualized the timeline on a whiteboard, or perhaps you sent an email outlining a few bullet points of information. Triple-loop learning would be thinking about how that comparison was made, and how it could be made better in the future. Could the in-person meeting be better? How significant should the violation be in order to warrant a meeting instead of just sending an email message? By learning about this learning, future scenarios where a rule, policy, or deadline is missed will have a more effective tool to investigate the discrepancy.
The core issue with single-loop learning is that it provides no way to uncover the underlying problem at hand. Without that analysis, the task or event has no chance to improve. In a competitive business environment, this could be the difference between success and failure. In healthcare, the consequences could be even more dire.
Additionally, single-loop learning doesn’t take into account new information that could lead to performance breakthroughs - instead electing to maintain the status quo under the assumption that it’s good enough. This mentality might seem appropriate in some situations, such as whether the lights should turn off or not at 5pm. However, it quickly becomes ineffective when you take into consideration increased energy costs of running the lights, the type of light being used and new research suggesting the effects on staff, and gradual wear and tear on the light being switched off. If this information is never considered, then your decisions are based entirely on intuition, common-sense, and preliminary research with no opportunity to evolve as new information comes to surface.
These concepts are easy to explain when it comes to lights and missed deadlines—but what about in the operating room? Precision OS has spent many years working on virtual reality training software for surgeons with a double-loop learning model, understanding that the “learning behind the learning” is just as important as the learning itself. This is also one of the main differentiating factors between Precision OS and Osso, which adopts more of a single-loop learning model.
Precision OS uses real-time guidance and feedback, right down to the millimeter as surgeons practice their procedures. When a mistake is made, the software will use both visual and audio feedback to describe the discrepancy, then suggest ways it can be improved for next time based on proven theory and expert input. This provides a loop of learning that uncovers the “why” behind mistakes, rather than just the “what”. Training in a virtual reality environment has other benefits as well, including greater confidence in the operating room which you can learn about here.
The triple-loop component, which is learning about learning, is embedded in the technology itself. Precision OS found that virtual reality can be a more effective tool than costly classrooms or logistically-challenging practice cadavers. By reflecting on the state of surgical training, Precision OS innovated with a new way to learn using state-of-the-art technology alongside expert advisors and theory.
As technology continues to provide new platforms for learning and growth, our understanding of the Loops of Learning can help us make new performance breakthroughs for our world’s surgeons and students.