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?
Felizmente, psicólogos e professores fizeram algumas dessas coisas por nós. Na verdade, Argyris e Schon (1974) propuseram os conceitos de aprendizagem de loop único, loop duplo e loop triplo para nos ajudar a compreender bem os métodos básicos de aprendizagem.
Os ciclos de aprendizagem
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.
A sua empresa está presa no aprendizado de ciclo único?
O principal problema com o aprendizado de loop único é que ele não fornece uma maneira de descobrir o problema subjacente em questão. Sem essa análise, a tarefa ou evento não tem chance de melhorar. Em um ambiente de negócios competitivo, essa pode ser a diferença entre o sucesso e o fracasso. Na área da saúde, as consequências podem ser ainda mais terríveis.
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.
Como o aprendizado de loop duplo está tornando melhores cirurgiões
These concepts are easy to explain when it comes to lights and missed deadlines—but what about in the operating room? PrecisionOS has spent many years working on surgical training VR learning platform 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 PrecisionOS and Osso, which adopts more of a single-loop learning model.
PrecisionOS usa orientação e feedback em tempo real for our surgical training VR learning platform, 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.
O componente de loop triplo, que aprende sobre a aprendizagem, está embutido na própria tecnologia. O PrecisionOS descobriu que a plataforma de aprendizagem de realidade virtual pode ser uma ferramenta de treinamento cirúrgico mais eficaz do que salas de aula caras ou cadáveres de prática desafiadora logisticamente. Ao refletir sobre o estado do treinamento cirúrgico, o PrecisionOS inovou com uma nova maneira de aprender usando tecnologia de ponta ao lado de consultores especializados e teoria.
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.