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double loop learning
Double-loop learning (DLL) (coined by Chris Argyris) is the modification or rejection of a goal in the light of experience. DLL recognises that the way a problem is defined and solved can be a source of the problem.
== Detail ==
Double-loop learning is contrasted with "single-loop learning": the repeated attempt at the same problem, with no variation of method and without ever questioning the goal. Argyris described the distinction between single-loop and double-loop learning using the following analogy:
() thermostat that automatically turns on the heat whenever the temperature in a room drops below 68°F is a good example of single-loop learning. A thermostat that could ask, "why am I set to 68°F?" and then explore whether or not some other temperature might more economically achieve the goal of heating the room would be engaged in double-loop learning.〔
抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』
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