| 翻訳と辞書 |
| Double loop learning ： ウィキペディア英語版 |
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）』
■ウィキペディアで「Double loop learning」の詳細全文を読む
スポンサード リンク
| 翻訳と辞書 : 翻訳のためのインターネットリソース |
Copyright(C) kotoba.ne.jp 1997-2016. All Rights Reserved.
|
|