I’ve been sharing some eclectic work around ‘Learning Transformation’ over the last few weeks, centred around some core ideas: things we need to start doing, things to leave behind, environmental factors impacting the design and delivery of learning, and so on. Today, i thought it may be worth rewinding to basics, and sharing a brief glossary: these are some (it’s not intended to be definitive!) terms that are central to the idea of a Learning Organisation, and the transformation of learning. You may not agree with my understanding, and that is the point: our ability to develop shared vocabulary, and understanding (which is about more than just repeating the same words) will be central to the teams that delivery transformation.
Learning: is a process of disturbance, sense making (both individual and collective), and reformulation. I recently described it as a process of ‘breaking yourself’, and making a new picture out of the broken pieces. Key elements to consider: we trade in those broken pieces, the picture we make is unique to each of us, but may resemble each other, the process is both internal and individual, whilst also being collective and co-created. Learning is about more than knowledge transfer and retention, although understanding how knowledge ‘works’ will be central both to design, and learning itself. Learning may be increasingly distributed, and pragmatic: less about what you know, more about your ability to find things out, understand validity, share, and change.
Knowledge: is one of those areas where one can rapidly get stuck in quicksand. Within Organisations, we are often concerned primarily with ‘explicit’ knowledge (which can be considered to be what we have codified and accepted), and ‘tacit’ or ‘tribal’ knowledge (which is typically held in social groups, or by individuals, and neither codified, nor universal. Much discussion of ‘knowledge’ related to Organisational learning concerns either getting explicit knowledge transferred, and to stick, or increasingly is about ways we access, validate, and exploit, tacit knowledge.
A useful alternative lens to take is to recognise that some types of knowledge are ‘discovered’, intact, whilst others are created (or co-created) by Organisations or teams. For example: the speed of light has been discovered, our sense of fairness is created. One may take a view that much of what we seek in Organisations is that second type, the ‘created’ type of knowledge. Challenges for Organisations include how they find knowledge, how they validate it, how they hold it, how they view ownership, how they measure transfer and understanding, how they trade in it, how they evolve it, how they learn to forget it at the right time (not the wrong one), how they earn co-creation, and how they capture and codify it in fair ways.
Design: more recently we have seen a diversification in the use of the word ‘design’, moving away from perhaps conversations about ‘learning design’ relating to the technical delivery of content, through to the much broader applications such as ‘experience design’, where we consider the creation of spaces, support structures, diverse ecosystems of interrelated technologies, and creative spaces fo assessment. Perhaps at the highest level, we could consider that shift has been away from utility and infrastructure, more towards holistic narratives, and a focus on the nature, and type, of interaction. For example, with a greater focus on ‘sense making’, we need to consider delivery of both formal content, and tacit content, safe spaces, suitable support, and storytelling channels, as well as ‘story listening’ space, as well as diverse models of formal, and peer moderated, assessment. Perhaps we could say that ‘Design’ is growing up, but possibly stuck in it’s moody teens as Organisations struggle to meaningfully implement the best of e.g. Design Thinking, whilst holding onto too much of the older patters of interaction design, and control of infrastructure.
Capability: is the power, or ability, of a person to do something. Pretty obvious i guess, but a central context within Organisational learning, because we tend, in that space, to have a greater pragmatic focus, and outcome orientation. Or at least we should. Possibly we can view ‘Capability’ as a better term than learning in that sense: we own a ‘capability function’, in that our role is to deliver the foundations or, or potential for, capability. A central concept in this understanding is that this capability comes in two flavours: a capability to do things we know how to do, and the capability to figure out things that we don’t. Or things that have changed. For example: i can teach you to reverse park a car. We both understand the concept, and there are tips and techniques to do this, as well as a very obvious and undisputed measurement of success. You just need the theory, the motivation, the space to practice, with contextual feedback, and perhaps a reward at the end. That’s the easy part. In the context of the Social Age, our Organisational focus is often less on this ‘known’ space, more on the unknown, or at least the rapidly evolving. In that sense, we tend to use ‘capability’ as a proxy for agility, for curiosity, for problem solving, for creativity, and so on. We use it to describe a desired state. Gaining clarity on our personal, and Organisational, view on what ‘capability’ means may be a sound starting point.
AI and ML: ‘artificial intelligence’, and ‘machine learning’ are two separate, but frequently interchanged, concepts, and are gaining great traction by opportunity, more so than strategy. Essentially if a system were ‘artificially intelligent’, it would be smarter than me on a good day, and would exhibit creative, innovative, and even give unknowably brilliant insights. ‘Machine learning’ systems, by contrast, may give the appearance of the same, but typically do so by pattern recognition (at great scale), and an ability to operate outside of the learnt constraints that inhibit our own thinking. A simple way to understand this it to recognise that these systems can deliver results that they cannot explain e.g. they can produce a useful output without the ability to narrate their work. True artificial intelligence is probably decades away, but proxy systems, ones that learn fast, are here today, and early applications typically trawl vast pools of data, and provide insight, or calls to action. This is our key focus for learning: systems that can connect, can provide insight, can assess, can direct. But an effective learning Organisation should rely at least to some degree on strategy more so than simply opportunistic insight. So we need connective roles; people who can source, deploy, challenge, and dissect, capability, and connect up multiple systems and approaches in ways that give us what we know we need, whilst remaining open to opportunistic ways to discover new insight or space of opportunity.
Collaboration: describes the activity of working together to produce an output (e.g. not just thinking about things, but producing assets/plans/outcomes). Collaboration is an activity then, but also a term used to describe output e.g. it’s a collaboratively written document, or the outcome of collaboration. The central and most important aspect to consider is that collaboration is predominantly a social, rather than technical, phenomena, and is also most likely a learned behaviour too. Technology clearly has a role: technologies of social collaboration are ascendant at the moment, but often focus on connection, and space, whilst collaboration may be more about rehearsal, prototyping, insight, and action, all facets of the social aspect. Technology can clearly facilitate and enable collaboration, but without social connection we probably remain within tribal units that we know, which are safe, but that may lack full potential. In my own work i differentiate between ‘collaboration’, and ‘complex collaboration’, the latter of which considers challenges which are themselves poorly understood, and which require connection across boundaries e.g. to work with people you do not know or fully trust yet. Organisations should be considering the technology of collaboration, the recognition and reward of it, but primarily the behaviours, the skills, and the storytelling and listening spaces to gain insight. Also worth recognising that most collaboration probably happens locally, and in opposition to the system, producing workarounds to poorly designed and deaf formal systems.
Learning Science: is a diverse set of disciplines that, together, may be hoped to provide insight into how we learn, and hence provide guidance into how we should design and deliver, support and structure, learning. It is not a magic bullet or set of answers: instead, it is perhaps an insight into the methodology and mechanisms by which we may discover valid insights and plan for action. In modern learning spaces, there is much discussion of learning science, but possibly in much the same way that there is much discussion about being fitter, healthier, and kinder: aspiration not always backed up by action. In very grounded and practical terms, we should deconstruct the notion of ‘learning science’ as the magic pill, and instead focus on central themes: how we know things, how we understand validity, how people learn and practical implications, the risks of reductionism and constructivism, understanding emergence, and the qualitative into quantitative cheat. Plus a ton of other stuff: perhaps consider Learning Science as a beautiful river in which we may pan for gold. We will be spending a lot of time here, so enjoy the scenery, but do not imagine you will get rich quick.
More to follow…