The Leading Source Of Original Research And Insights On Business Model Strategy, Digital & Tech Business Models
What Is Single-loop Learning? The Single-loop Learning In A Nutshell
Single-loop learning was developed by Dr. Chris Argyris, a well-respected author and Harvard Business School professor in the area of metacognitive thinking. He defined single-loop learning as “learning that changes strategies of action (i.e. the how) in ways that leave the values of a theory of action unchanged (i.e. the why).” Single-loop learning is a learning process where people, groups, or organizations modify their actions based on the difference between expected and actual outcomes.
Whether we care to admit it, every person thinks in a certain way based on their current beliefs and assumptions about the world. This thinking guides their actions (what they do) and also influences their performance (what they get).
As the individual experiences life, they continually assess outcomes to determine whether they occurred as expected or whether there were things that could have been done differently. This form of self-inquiry may be subconscious and unstructured, or more deliberate and formal.
Argyris defined this near-constant inquiry process as the detection of error. In single-loop learning, the error results when an individual isn’t where they want to be. Put simply, a difference exists between the expected outcome and the actual outcome.
Once an error has been detected, the individual must revisit their actions (and the strategies guiding them) to assess and develop new action strategies. Importantly, this must be done without altering an individual’s core beliefs or assumptions.
This process of developing new actions borne from the same thinking is the basis of single-loop learning. In an organizational context, single-loop learning encourages employees to consider alternative strategies that respect governing factors such as goals, values, plans, and rules.
The four steps of the single-loop learning cycle
There are four simple steps to a single-loop learning cycle:
Observe current outcomes – what happened, and how long has it been occurring?
Assess possible corrections – where did the individual, group, or organization deviate from the norm?
Develop action strategies based on the insights uncovered.
Implement the new action strategies and then observe current outcomes to repeat the process once more.
Limitations of single-loop learning
There are a couple of major limitations of single-loop learning.
For one, the approach only addresses the symptoms of a problem. By ignoring the problem’s root cause, it is likely to reoccur in the future. This issue is exacerbated because the approach does not consider that core beliefs and assumptions may be contributing to the problem in the first place. Many consider single-loop learning to be a band-aid solution at best, capable of producing nothing more than minor or short-term fixes.
Single-loop learning also assumes problems and their associated solutions to be close to each other in time and space. That is, the method does not consider any intangible factors that might be impacting events and processes. This means single-loop learning will rarely encourage creative or innovative solutions, instead defaulting to ideas that address deviations from more tangible actions, processes, procedures, systems, and strategies.
Single-loop learning is a learning process where people, groups, or organizations modify their actions based on the difference between expected and actual outcomes. It was developed by the teacher and author Dr. Chris Argyris.
Single-loop learning can be described in simple terms via four steps. The individual observes the outcome, evaluates possible corrections, develops action strategies based on viable corrections, and then implements them. After implementation, the loop, or cycle, starts again as the impact of the new strategy is observed.
Single-loop learning has two major drawbacks. For one, it addresses the symptoms of a problem without paying any attention to the root cause. It also assumes problems and their associated solutions to be close to each other in and time and space.
Strictly Necessary Cookies
Strictly Necessary Cookie should be enabled at all times so that we can save your preferences for cookie settings.
If you disable this cookie, we will not be able to save your preferences. This means that every time you visit this website you will need to enable or disable cookies again.