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\u2019re 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? <\/p>\n\n\n\n
Afortunadamente, psic\u00f3logos y profesores han hecho algo de esto por nosotros. De hecho, Argyris y Schon (1974) propusieron los conceptos de aprendizaje de ciclo \u00fanico, ciclo doble y ciclo triple para ayudarnos a comprender bien los m\u00e9todos b\u00e1sicos de aprendizaje.<\/p>\n\n\n\n
The \u2018Loops of Learning\u2019 generally follow three methodologies. The first, single-loop learning, is also known as \u2018following the rules\u2019 \u2013 it\u2019s 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 \u2018single-loop\u2019.<\/p>\n\n\n\n
In your company, single-loop learning might show up in other ways \u2013 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.<\/p>\n\n\n\n
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\u2019s 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. <\/p>\n\n\n\n
Finally, triple-loop learning takes it a step further \u2013 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. <\/p>\n\n\n\n
El problema central del aprendizaje de ciclo \u00fanico es que no proporciona una forma de descubrir el problema subyacente en cuesti\u00f3n. Sin ese an\u00e1lisis, la tarea o el evento no tiene ninguna posibilidad de mejorar. En un entorno empresarial competitivo, esta podr\u00eda ser la diferencia entre el \u00e9xito y el fracaso. En el sector sanitario, las consecuencias podr\u00edan ser a\u00fan m\u00e1s graves. <\/p>\n\n\n\n
Additionally, single-loop learning doesn\u2019t take into account new information that could lead to performance breakthroughs \u2013 instead electing to maintain the status quo under the assumption that it\u2019s 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. <\/p>\n\n\n\n
These concepts are easy to explain when it comes to lights and missed deadlines\u2014but what about in the operating room? Sistema operativo de precisi\u00f3n<\/a> has spent many years working on surgical training VR learning platform with a double-loop learning model, understanding that the \u201clearning behind the learning\u201d 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.<\/p>\n\n\n\n