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.[1]
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:
[A] 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.[1]: 99
See also
References
- ^ a b Argyris, Chris (1991). "Teaching Smart People How to Learn" (PDF). Harvard Business Review. 4 (2): 99–109. Retrieved 22 November 2015.
- Argyris, C.; Schon, D. (1978). Organizational Learning: A theory of action perspective. Reading MA: Addison-Wesley. ISBN 0-201-00174-8.
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External sources
- "Teaching Smart People How to Learn," by Chris Argyris, Harvard Business Review, May–June, 1991, pp. 99–109
- "Chris Argyris: theories of action, double-loop learning and organizational learning," by Mark K. Smith 2001, infed
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