Understanding organisations (schools, universities, professional development networks, communities of enquiry…) as complex adaptive systems enables us to explore patterns and relationships between agents and processes rather than only focusing on individual entities or variables which we think are important. For example, if we are trying to understand and therefore improve student engagement in learning, it’s more helpful to be able to identify the many factors which inter-relate with each other and contribute to engagement, than to simply focus on, say student self-regulation or task mastery. Other factors that we know might be relevant include the quality of student teacher relationships; external assessment frameworks and the sequencing of students’ encounters with knowledge. Complex systems thinking enables us to begin to get a handle on the sorts of patterns and relationships between such factors in a way which is closer to the lived reality of learning and leading in such contexts.
Exploring social organisations as complex adaptive systems helps us develop a holistic approach, in which we view each entity as part of the whole. Thus we can explore the emergent property of factors, and the synergy created by their interactions which is more than the sum of the parts.
All hard systems are embedded in soft systems (people use technology) and understanding soft systems as they really are, is crucial for understanding learning and change. Tony Bryk and his colleagues say of schools that they ‘are complex organisations consisting of multiple interacting sub-systems. Each subsystem involves a mix of human and social factors that shape the activities that occur and the meaning that individuals attribute to these events’. (2010: 45). The real challenge is understanding this complexity in such a way that we can make good enough judgements about practice. To understand such complexity we need tools for re-representing it and for modelling it.
The concepts, representations and software tools from Systems Thinking and Complexity Science have already demonstrated their relevance in diverse fields. The Learning Emergence network is investigating to what extent they provide us with insight into the patterns and relationships between the parts and the whole in systems specifically concerned with learning and sensemaking. One way to summarise this is to integrate Purpose, with People and Process.