Yesterday i shared the first part of a glossary of learning related ideas, to run alongside the recent Learning Transformation Maps that i have been drawing. These are ideas that are central to a Learning Organisation. In the first part i discussed ‘Learning’, ‘Knowledge’, ‘Design’, ‘Capability’, ‘AI & ML’, ‘Collaboration’ and ‘Learning Science’.
Engagement: is often described as something that we want to take or demand from learners, but in reality is something we must earn, or create opportunity for them to invest. At times we often also confuse ‘engagement’ with ‘agreement’, or even ‘completion’. They are separate things. People may be highly engaged, but not agree, not pass an assessment, or not complete a programme. I can engage in either agreement, or in dissent, and both are equally valid in a ‘sense making’ learning context. There is a relationship between design, and engagement, but it may be more nuanced than we typically believe. Good design can create the space, interaction, and opportunity, for engagement to be invested. What this means for Organisations is that they must build understanding in how engagement is invested, as well as understanding the currencies in which it is traded. People typically equate ‘trust’ with ‘opportunity’, which is a space for engagement.
Co-Creation: is central to social collaborative models of learning, models that take formal knowledge, and layers of socially created knowledge, and synthesise them into new meaning. Co-creation happens in multiple layers: locally, globally, within trusted social relationships, and through formal mechanisms. But at heart, it is a process that we can best understand as a process of loss: the difference between ‘my story’, and ‘our story’ is that part of my truth is typically left out of the shared narrative. That’s not a bad thing, but it is important, because that collected ‘loss’ is a wealth of grounded truth and personal understanding: the very things we seek to access through Social Learning. If we create the right conditions, then the co-creation will happen in a community within which people can have open and honest discussions about how much of themselves they can invest in the new story. If we lack that trust and cohesion, then we will simply share the disposable parts of ourself that lies on the periphery. Truly meaningful co-creation is an emotionally, and socially, risk proposition, and again speaks to the idea that we must earn the conditions for it to happen within. It takes more than pot plants, good coffee, and expensive glass offices.
Culture: is a subject that i barely dare address in one paragraph, but perhaps a simple definition is all that we need. Culture is every conversation we have with someone else, everyday. It is co-created in the moment through our actions towards others. In that sense, culture is not like gravity: it does not just ‘happen to’ us, but rather is generated ‘by us’. So we are all equally to blame, or should take equal credit! Culture is relevant to learning in many ways: culture sets the scene within which we may act, or chose not to act. It frames the dominant narrative of behaviours that surround us, which includes the dominant narratives of consequence, blame, failure, gratitude, kindness, fairness, and trust. Whilst typically described as monolithic, and resistant, it can in fact be highly fluid, held as it is on foundations of action in the moment. Essentially our culture is a dream we repeat everyday, and an aspiration we hold for tomorrow. A learning organisation will not buy a learning culture from a consultant: it will create one through it’s own action. Similarly, leaders will not give us culture as a gift from above, but rather will create the space for us to build it.
Big Data and Analytics: aligns with the entry on AI & ML in some ways. ‘Big Data’ is perhaps a term to describe a book so long that you or i will never read it. Even though we know we would be better off if we had. Technology can now read that book for us, and (hopefully) tease out the meaning from it. The benefits are clear, but so too are the risks. Learning systems truly ‘learn how to learn’, which is another way of saying that they may be biased, depending upon what they have learned before. The design of analytics is a specialist skill, but of little use if not aligned to business needs. Hence, rather like the application of technology into learning, we find that the learning specialists know what they want (but often within an outdated frame), the technologists assume what we need (outside the learning domain), and we all suffer from the result. Organisations that are serious about mining the treasure of Big Data around learning, and more broadly around Organisational effectiveness and productivity, will build expertise in subject, expertise in technology and analytics, and a third area of expertise which is the bridging roles between the two. This speaks of a broader challenge for Organisations: what do they currently do that they should continue to do, what can they learn to do, and what do they need to either buy in, or build, because they cannot learn it.. Balancing those three questions is important.