Innovations in Organisational and Community Learning

  • Lisa Vos


Human beings have an extraordinary capacity to self-organise and accomplish great results. We have proven so since ancient history. Mankind also has an amazing capability to learn collaboratively and to create innovative solutions by combining a diversity of multiple perspectives, brains, personalities and ideas. Despite overwhelming evidence of the effectiveness of self-organising systems, the dominant approach to organising and design is top-down, structured and planned. In this chapter the argument is made that the dominant mental model and approach in organisations and in learning are ineffective in the face of most of the challenges people and organisations need to handle today. An alternative way of thinking and acting is needed to effectively deal with adaptive challenges. One possible alternative model is that of self-organisation and collaborative learning. The dominant mental model will be explained first, then a framework will be introduced which helps discern in which circumstances this model is effective and which circumstances require a different mind-set. Then self-organisation is offered as an alternative mind-set. When self-organisation is applied to knowledge creation and innovation, “collaborative learning” is discussed as mental model and as a set of methodologies. The argument will be made that collaborative learning methods have proven to create novel solutions to wicked problems, enabling input from many and diverse stakeholders, establishing ownership and alignment and doing all this more efficiently than traditional top-down learning models. The chapter will draw on research and publications on the nature of learning in social contexts, organisational and system change, chaos theory and complexity theory. In addition the author’s professional experience in Organisational and Leadership Development in The Netherlands, Australia and New Zealand provides a source of data.


Mental Model Collaborative Learn Wicked Problem Collective Learning Learn Domain 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.Lisa Vos Consulting and Melbourne Business SchoolWageningenThe Netherlands

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