Introducing “Network Learning”
This year I am working to publish a book focused on advancing adult learning, human resource development and knowledge management practice – a concept I am introducing as Network Learning: advancements in adult learning and Human Resource Development
Over the last two decades, there has been a common problem across Human Resource Development and Knowledge Management through to University teaching, learning and assessment, being a focus on knowledge acquisition, storage, sharing and creation from the perspective of the individual. Taking cognitive or situative perspectives (rarely a blend of the two) practitioners and theorists have spent a lot of time developing and deploying reductionist approaches to knowledge and learning (e.g. misguided time devoted to Learning Styles on official Chartered Insitute of Personnel Development courses, even though such approaches have been widely questioned). The belief has been that if we can understand/control/influence the individual organisations/educators can better understand/control/influence knowledge and its creation.
Universities and organisations alike have all-too-often missed opportunities to advance the human advantage through a greater exploration of network learning environments – established or swarmed digital and analogue spaces where learning exceeds the sum of its parts, constrained only by the limiting resource within the network (e.g. specialist knowledge on a given subject). [some will point to Communities of Practice (CoP) here, but, for example, CoP deployment, as well as the situative perspective of teaching, learning and assessment in Higher Education Institutions, frequently fails to appreciate/analyse the nature of networks or the influence of the non-human (e.g. representations/artefacts such as leadership books) upon community beliefs].
Such a ‘networked’ approach to human learning requires a move from traditional reductionist methods toward a holistic network understanding of learning and knowledge acquisition, storage, sharing and creation.
Why? In short, individuals and their collective human advantage are under threat from RAID (Robotics & Artificial Intelligence Development), where the value of human capital is dependent on social capital (see www.theiirc.org) and the ability of a given network to adapt to rapidly changing conditions at its boundaries.
The basic hypothesis of Network Learning is as follows:
An individual’s awareness of the interconnectedness and interdependence of individual nodes within a given network (while swarming or when established) can improve the speed, depth, complexity, completeness and security of knowledge acquisition, storage, sharing and creation across a network.
I’ll be blogging about the concept, its theoretical foundations (e.g. Actor Network Theory, Network Theory, Complex Adaptive Systems, andragogy and the situative and cognitive perspectives on learning) and practical deployment of concepts/frameworks/analytical tools, illustrated using case studies, over the coming months.
Some of the core tenets that underpin the Network Learning concept are as follows:
- Network learning is not about the cognitive learning processes of the individual, but the outputs of the network, positive and negative, and awareness of consequences.
- Network learning is about embracing the interconnectedness and interdependence of the individual as a node in an ever-evolving social network driven by interests and beliefs.
- Network learning is, therefore, interested in moving learning designers and educators away from a focus on the individual toward a position where the designer or educator enrols people in the learning process through influence.
- Network Learning is not about control, but an understanding of the learner and their place within a network of other learners and educators (influencers), and the representations that influence an individual’s enrollment in beliefs. Through an understanding of collaboration and competition between networked learners (nodes and influence), it then becomes possible to anticipate, sense and direct learning in such a way so as to increase the depth, completeness, complexity and security of learning.
- Network Learning is interested in harnessing the emotional and rational power of the network, where the learning outputs of the network can exceed the sum of its individual nodes.
- Network Learning approaches limit the risk of shallow, incomplete and insecure learning that cause systems to fail.
I hope you enjoy the change of focus for this blog and I look forward to hearing your thoughts as I reveal some of the research/case examples from the book.