Sunday, June 15, 2014

Double-Loop Learning, Eric Okula

Eric Okula
Double-Loop Learning
            Double-Loop learning falls under the overarching theoretical school of Organizational/Developmental. 
The basic principle goals of Organizational and Developmental of learning are:
·         To build personal interpretations of the world based on individual experiences and interactions (constantly open to change, cannot achieve a predetermined, "correct" meaning, knowledge emerges in relevant contexts)
·         That learning is an active process of constructing rather than acquiring knowledge
·         That instruction is a process of supporting knowledge construction rather than communicating knowledge
·         You should not structure learning for the task, but engage learner in the actual use of the tools in real world situations 
·         Learning activities should be authentic and should center around the problematic or puzzlement as perceived by the learner
·         The focus is on the process not the product
·         The role of teacher is a mentor not a teller
·         One should encourage reflective thinking, higher-order learning skills
·         One should encourage testing viability of ideas and seeking alternative views

Basic Characteristics of Double-Loop Learning
            This model is based off of the work of Professor Chrys Argyris.  To better understand double-loop learning I think it is best to first example single-loop learning.  Single-loop learning, as illustrated below has two parts; the action strategies and the results.  In this format you have a certain unwavering ideal result or consequence.  After a failed attempt you make a correction to your strategy and attempt again to reach your original ideal result; If unsuccessful, once again you exam your attempt to tweak or alter your technique before trying for the same ideal result or consequence.  This cycle continues forever or until the result is met.  In a double-loop learning situation however there are two separate occurrences instead of a single-loop happening over and over again.  When using double-loop learning if a result or consequence was not ideally met instead of simply re-attempting it the learning thinks deeper about the process and asks “why?” instead of simply “what?”   This deeper thinking and reflecting allows for one to better align their goals with a more structural framework. 



   
Links:

Practical Example of Double-Loop Learning
            I can think of a very practical example of how double-loop learning could be a great learning style for a baseball player or coach.  Let’s, for our example, look at a player who is trying to hit a homerun off of a pitcher that is known for his best pitch which is a low and outside curveball.  In order to hit a homerun you must make contact on the lower part of the baseball thus giving you the necessary trajectory for the ball to clear the outfield wall.  If the pitch is low that is very hard to do, plus because it is a curveball the ball will be dropping as it approaches the hitter making it even harder to lift into the air.  Moreover, the fact that the pitch is outside makes it even more difficult to hit a homerun as for most hitters their power decreases as the ball moves away from their body.  Trying to hit a homerun on this pitch would result in failure after failure.  In a single-loop model the player would just keep trying to do different things (swing sooner, swing later, swing with more of an uppercut, drop their shoulder, etc.) but it would be very unlikely for the player to reach success as the cards are stacked against them.  In a double-loop model however the player can reassess his/her goal.  In doing so it would be discovered that a homerun is not the ideal outcome.  Low curveballs are easy to get on top of with your bat thus making them ideal for hitting line drives.   Outside pitches are great if you want to hit a line drive because allowing the pitch to travel outside gives the hitter more time to see the pitch and thus a higher percent of successfully reaching their goal. 

Embedded is a silly video that points out the differences between single-loop versus double-loop learning.   In the video the girl in hungry and when applying the single-loop model she continues to try and avoid a banana while when applying the double-loop model and reflecting on the situation she comes to the realization that the banana is actually exactly what she needs.


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