Empathy

I feel upset: not for myself, but for my son. Something small happened at nursery, something trivial, something lost. And he was upset by it, as small children can be. I’m almost alarmed by the effect it had on me: not just sympathy, not simply compassion, but a visceral empathy and desire to hold him close and tight. Not a logical response, but probably a very human one: i just want to fix his pain, to take it away, to make him forget it ever existed.

If this is what it’s like to be a parent, it looks like it may be harder than i anticipated.

Of course i understand why i feel as i do: not only am i empathic, but he is my son. And as any parents would, we have given him his first few years of life in the most safe and sheltered way that we can. Not hiding him away, but trying to ensure that he learns to fall gently. That his risk, his learning, is appropriate to his size.

But of course this does not work forever: as he grows, he will learn, and some of that learning will be about loss, about pain, about how things fall beyond our control, and about how our actions have consequences that we cannot hide or be shielded from.

These are hard lessons, but of course important ones. Protecting him from some pain now leaves harder lessons down the line.

Again: i understand this, and yet it’s still a hard lesson and harder truth.

I suppose that is my role as his papa, to guide him and to gradually open my arms further, wider, although i guess always ready to wrap him back up, for at least as long as i can. To let him know that even when he may feel pain or loss, he does not do so alone.

In Doubt

Whilst i seek certainty, and speak with confidence, the reality is that my work operates in cycles of  illusion and self doubt. At times, things converge: ideas take shape, and i find certainty. At times i inhabit that space for a while. And then, i tend to dive out of it.

Other times my work becomes so fragmented, divergent, exploratory, and curious, that i begin to lose hold of the threads. This is a vulnerable time: i come to doubt myself and my ideas. Whilst i resist it, i may swim back to certainty, or at the very least the safety of the crowd. At others, i find the bravery to drift – typically only able to do so because i operate in the arms of a tolerant community.

Partly this is due to the things i am interested in: my work explores the Social Age, but the Social Age may not exist. And if it even does, it’s probably not yet been fully manifest. And even if it were, nobody asked me to take a look at it.

My work exists in this context, and explores this context, and hence it makes it up as it goes along, and consumes what it makes up.

By necessity, such an exploration considers what may be, more so than what is, even if i always try to grind it back down to the practical.

If there are any constants in my work, they probably relate to boundaries (i often describe my work as taking place at the intersection of systems), to structures and gradients of power, to space and sense making, to the delusion and movement of crowds, to the underlying social currencies and mystical fabrication of our realities, to the perils of certainty, to the gap between what we need and what we believe.

I think sometimes i think i have certainty when in fact i am deluded or simply wrong. At others, i find simplicity but bury it, and myself, in new ideas.

Overall it works, but it can be exhausting: after a dozen years of this, i recognise the signs. I start to doubt where i am going, i become fractured in my thinking, and i try to push on because sometimes, at those moments, something snaps into focus, provided you can hold or stretch your doubt far enough. Sometimes i feel that i am making up the language that i must speak with: writing is part of that process.

Starting the doctorate this year has only exacerbated these feelings: it is inherently self reflective, and it’s hard to both look within and forge forwards!

I often share that the only constant in my work is in my ability to be wrong, at least in the detail, but i still maintain the direction of travel: if we are not moving towards a new certainty, we are at the very least departing the legacy one. And sometimes, to find the edges, we have to get lost.

I find comfort in the fact that i do not think people judge my work too harshly because of this context, or at the very least they are too polite to say if they do.

A little doubt, of self and system, is probably a good thing.

Fracture and Feelings – Togetherness and Otherness

I’ve been revisiting the writing on ‘Togetherness – Otherness’ today, writing about fracture. It’s been a fractured week: my attention has been fragmented and partial, everything feels incomplete. So it was with some surprise that i found i liked the direction that the writing was taking.

This work will explore how we are together, and how we are apart, which may seem very ephemeral, but in fact is about the most real thing we feel as social humans.

For all our talk of inclusion, the experience of our social lives includes the lived experience of the ‘other’, of being apart. Either by omission or design: we may make people into the ‘other’ on purpose, or simply by coming together in a place that they are not.

But why?

There is something about safety and comfort, about familiarity. Something about similarity. I know Organisations talk about ‘shared purpose’, but that is a abstraction. Something we may articulate when the foundations are there. Although i find people parrot it back when describing community, what they typically then go on to describe are features not of purpose, but of safety, of space, of identity and belonging.

I have captured this imperfectly in the language of coherence: a trait that some communties have. They become coherent, in that they find a connected strength. And with coherence maybe we can find purpose, but it is not the base layer, not the foundation.

This is early stage work, and i will continue to #WorkOutLoud to share it as it develops. I hope it will ultimately form a sequel to ‘The Humble Leader’, but we shall see. Some ideas burn into books, and others are just a simple idea, which is articulated and done.

Learning Science Crash Course #1

Today i am sharing some of our ongoing writing for the Learning Science book: this part sits in the central, second, section, and provides an overview of aspects of the ‘hardware’ and ‘software’ in our heads. This is our second draft of this section, as we have been figuring out at what level to pitch this work, and in the broader context of the book. You could really write a full book about any of these things.

In our first collaborative post, back in November 2022, we described the high-level landscape of the multidisciplinary field of Learning Science. In it, we mentioned that Learning Science techniques and tools can be organised into five categories, and in this week’s post, we revisit and expand on the first of them: Learning Science is concerned with how people learn. 

1. How people learn:
A description of people’s brains and minds

The first subdomain in our Learning Science model is concerned with the way people learn. It includes neuroscience (how our physical brains and nervous system work, which is sometimes considered ‘The Hardware’) and cognitive science (practical observations and conceptual theories to understand how our ‘minds’ emerge from our brains, sometimes considered ‘The Software’).

As a rough concept, you can envision this category as a description of what happens inside a person when they learn. From this, we glean hallmark areas of study such as:

• Brain structure, neurons, and physiological encoding: This subfield considers the physical structure of the brain, how it functionally enables learning, and how it changes as we learn. Work in this area might discuss ‘neurotransmitters’ (the chemicals that moderate brain activity), ‘functional and neural localization’ (identifying which parts of the brain enable different cognitive activities) or ‘neuroplasticity’ (the brain’s ability to adapt and respond to new learning). We have, ourselves, talked about some of these concepts in our post on ‘Nervous System Sensitivity’, which discussed how stimulation (stress) and individual differences in our nervous systems interact with learning and performance. This study of the ‘hardware’ has accelerated through a succession of ever-more effective sensing technologies, which allow us to observe and interpret, in real time, aspects of what’s happening in the brain. This work has been challenged, in part, because the brain doesn’t work ‘one way’ (like a car engine does), but rather it displays fluid interrelationships between parts, a range of types of interactions, and even intrapersonal differences across time and context.

• Stages of human development: Both neuro- and cognitive scientists (‘hardware’ and ‘software’ specialists) also examine how people develop from infants to adults and eventually into elders – understanding the maturation process, for instance, when and how babies learn to verbalise or mentally manipulate objects, as well as the ageing process, such as the cognitive changes that occur in later life. When we buy a computer, the ‘hardware’ is constant, whilst the software, the Operating Systems (OS) may be updated over time. The brain is not like this: both systems change – the physical structure (as both a function of age and learning) and the ‘software’ (as our knowledge, experience, and strategies develop – something akin to ‘wisdom’ that we discussed earlier). Lev Vygotsky’s cornerstone theory, the Zone of Proximal Development (ZPD), also fits in this subfield. ZPD describes the conceptual space between what we can already do and what we could do with some support (such as expert feedback and scaffolding). Or to put it another way, ZPD describes the space between our capability and our potential. Vygotsky’s work has had an enduring influence, and it’s easy to see why, because it highlights a practical link between understanding of ‘how the brain works’ and just ‘what on earth we can do about it’.
  
• Memory and forgetting: The study of memory examines things such as ‘synaptic plasticity’ (how memories are physically formed, strengthened, or pruned) and the role that different brain components (such as the amygdala and the emotions it regulates) play in encoding and retrieval. This work is complemented by concepts like the different types of memory (sensory, working, and long-term memory); memory concepts including ‘chunking’, ‘linking’, ‘rehearsing’, ‘reinforcement’, and ‘retrieval’; and studies on the fragile nature of ‘accurate’ memories. (For example, see Elizabeth Loftus’s work on eyewitness testimony or Daniel Schacter’s Seven Sins of Memory.) Another foundational concept in this subfield is the Ebbinghaus Curve, which explains the decline of memory over time and ways to strengthen retention. The study of memory and forgetting is interesting: it sits in a rather practical space and gives actionable insights.
    
• Information processing theory: Cognitive scientists have developed robust frameworks to explain how we process information. Models also consider the different types of information stores (that is, forms of memory described above) as well as cognitive processes (such as attention, perception, encoding, and retrieval) and executive functions (which helps us understand the mechanisms for higher-order cognitive activities, such as sense making, decision making, and metacognition). Concepts – such as Schema Theory, Cognitive Load Theory, and Embodied Cognition – help us envision how information makes its way from the world, into and through our mental pathways, to be transformed into our own personalised knowledge, and for later integration with other ideas and retrieval for use. And through these lenses, we also better understand the interplay between our ‘minds’ (executive control, System 2) and ‘brains’ (midbrain influences, System 1) – Daniel Kahneman’s Two Systems of Thinking.

Essentially, this subfield attempts to explain what happens when information enters our heads. Starting with our senses – because the brain doesn’t directly ‘experience’ stimuli – be that heat or cold, a good book, or a Bond film. Our ‘sensory organs’ – our eyes and ears, skin and tongue – are stimulated by movement, light and taste, and then those sensations are translated by our brains into ‘perception’, our interpretation and sense making of the signals. Information processing models are also concerned with the mechanisms of storing and retrieving information, meaning that they partially overlap with more focused studies of memory and forgetting.  

• Motivation and emotion: In this subfield researchers might explore topics such as how positive emotions (like curiosity and interest) can enhance attention and memory or how negative emotions (like anxiety and stress) can impair learning. Additionally, this area of study examines the facets of motivation, such as intrinsic versus extrinsic motivation, goal setting, and self-regulation. Motivational theories related to drivers and demotivators also fit here, perhaps starting with well-known conceptual models, like Abraham Maslow’s ‘Hierarchy of Needs’ or Frederick Herzberg’s Two-Factor Theory of motivation and organisational ‘hygiene’, and progressing into more nuanced (and rigorous) studies such as core drives (such as curiosity, meaningfulness, accomplishment, and social influence) and other sociological and psychological levers that influence us, for example, how we’re wired to engage in social reciprocity or have a strong bias for internal consistency among our thoughts, values, and actions. Mihaly Csikszentmihalyi’s famous theory of ‘Flow’ – that feeling of being ‘in the zone’ – could also fit in this subfield.
   
• Expertise: The study of ‘expertise’ examines the stages of learning from novices through to experts (for example, see the Dreyfus model), and it explores the seemingly superhuman abilities of experts, like Recognition-Primed Decision Making and automaticity – when what we’ve learned become a subconscious routines that ‘frees up’ executive cognitive resources for other matters. (This is one of the reasons why our brains can operate so efficiently, on such little energy, because we don’t have to expend conscious effort on everyday matters, like where to place your fingers whilst typing!) Since we’ve limited ourselves to 8 subfields, we might also squeeze ‘Transfer of Learning’ into this bullet. It describes the extension or generalisation of knowledge and skills from one context to another. For instance, when a person learns about first aid from a seminar and then later applies those skills in a real-world emergency or builds upon the knowledge when considering the anatomy of their pet cat.
   
• Brain injuries and congenital differences: Many of the earliest attempts to understand how our brains work relied on the victims of mining accidents or war – those with visible damage to parts of their brain. Today, the study of how physical injuries and neurobiological conditions (such as dyslexia) affect learning continues, and we’ve added depth to this subfield through the exploration of neurodivergence. Examining those capabilities that make us learn ‘differently’ not only helps us better understand how the brain works, but it’s also changed the conversation – recognizing that there’s no single ‘perfect’ brain structure or way to learn. (You can probably already see why this leads us away from simple and mechanistic perspectives on learning: instead learning is truly a personal and unique affair – but a process that we can nonetheless understand, support, and shape – and we’ll get more into those methods in Part 3 of this series.)
   
• Theoretical paradigms of learning:  Finally, there are a seemingly countless array of learning theories. Some of the best known theoretical paradigms are behaviourism, cognitivism, constructivism, and connectivism. There are also social learning, experiential learning, and self-directed learning theories, as well as an extensive subdomain focused on developmental learning (that is, how we grow and learn from infancy). While these could arguably be grouped under Part 3 (instructional methods), we included them here because they’re founded on theoretical descriptions of how we learn.

The principles outlined here are the foundational tools – the hammers and spanners of – Learning Science. They help us envision what happens ‘inside our heads’ and inform our approaches to instructional design, assessment, and more L&D systems. 

Of course, it’s necessary to caution that while Learning Science offers robust insights, our brains are not ‘one size fits all’. And despite our body of knowledge, the brain (and how it learns) remains both one of the most common things to be found on our planet, and yet one of the most complex and misunderstood.

#LearningScience – The Hardware and Software

Just #WorkingOutLoud today with an illustration for the next section of the Learning Science book that i’m working on with Sae and Geoff.

Earlier we described how Learning Science techniques and tools can be considered in five categories:

[1] How people learn – a description of people’s brains and minds

[2] Factors that affect learning – a description of the variables that moderate learning

[3] Instructional Methods to enhance learning – conceptual tools we can use to (try to) positively influence learning

[4] Technology to facilitate learning – tangible tools we can use to (try to positively influence learning

[5] Assessment Methods – checks on learning outcomes, conditions, and/or tools

This illustration lives in the first of these sections, where we use the crude analogy of the neuroscience as ‘hardware’ and cognitive science as ‘software’.

I will share the full section when we have completed our final review. Coming back to this work today, after a holiday, i realise that we have already generated a pretty decent body of work. It’s still relatively early days, but we have made good progress and, more importantly, i think we are getting out heads and hands around the structure.

Change Within Systems

In general, the systems that we design are designed to persist: it’s not that they are not intended to change, ever, but rather that they are not intended to accidentally or carelessly change right now. At least, not without our oversight and under our control.

Day to day, this can be a good thing: longer term, or as our context of operation shifts, it can be fatal.

Whilst the fact that change is ‘hard’ is commonly spouted, the reasons why this is so may be less clearly understood.

Change can be difficult because of innate complication (it’s hard to create a new technology), but may also be hard because it carries us from the know into the unsure, from power into uncertainty, and from control into collaboration, co-creation, or chaos.

Essentially the primary experience of change may initially be loss, even if there is a promise of eventual gain. Even if that loss is simply loss of certainty.

In this context, it’s valid to ask how we should explore, consider, or initiate and even ‘drive’ change: from within, alongside, our outside of, the current structure.

Change that sits within can tend to be diffused, or made too safe, too fast.

Change alongside can lead to de-lamination of spoken and lived culture, as the story progresses but the behaviour does not.

And change that is held externally, may provoke immune responses from the legacy Organisation. It lacks connection into the very structure that need to change – this is most commonly seen when e.g ‘innovation’ is housed in a ‘skunkworks’, but where the dynamism of that entity cannot be replicated or assimilated more broadly. It is forever the ‘other’ and indeed may be a source of conflict.

All this may mitigate for diffused change, or socially co-created, where there is no change ‘function’ per se, no new home for ideas – but rather where the future state is collaboratively created, requiring a greater ability for synthesis, storytelling, and rapid iteration, including of structure itself, hence why i argue that a Socially Dynamic Organisation will be more guided than governed, and will be reconfigurable according to need. Because it is not static, requiring ‘change’ to be done, but rather is perpetually fluid, which may seem like a dream, but in reality simply requires us to re-evaluate our relationship with risk and structure itself.

The Time Till Failure

Sometimes failure is easy to perceive: a crash, shock or fracture. But if we focus our vision on the specific point of failure, we may miss the opportunity to learn from the run up, and legacy of it. Indeed, in some ways it’s more valuable to consider an overall journey of failure than simply the point of fracture, as this journey provides us greater opportunity for insight and understanding.

This journey will typically involve the context of failure (is it something breaking down, or the context that surrounds us that changes and hence exposes weakness), the opportunities for insight (which voices were dominant, which were silenced, what markers were rationalised or ignored), the cascades of failure (what impacted on other things, and how did it accelerate – and how could it have been diverted, diffused, or countered), the perception of failure (it’s possible that we fail without even realising it till much later, when judgement occurs – so how would we know that we have failed?) and the legacy of it (is failure rationalised, retrospectively judged, hidden, or is there a methodology to create an artefact of learning?).

If we view failure in this sense – as the run up, the event, and the legacy – then we may realise that the greatest value lies in the first and the third aspects of it. Whilst failure as an event is dramatic, our insight may come more from context and shadow.

Our Spaces of Difference

There is much discussion about the polarising nature of social media, the increasingly partisan nature of politics, and the seemingly endless spaces of our difference and dissent. And it is true that we live in noisy times, where often the first reaction is rejection. But there may be value in considering the structural nature of difference, in that we inhabit three spaces, not simply two.

When we talk about agreement or argument, about belonging or exclusion, about love and hate, we are inclined to see a binary state, but it can be useful to consider a third space. A space of tolerated difference. Which may also be a space of potential.

On some things we are clear: there is ‘us’ and ‘other’, but for many topics, there are spaces of overlap, or gaps between the systems. Spaces were we are not unified, and yet not fully opposed. Potentially we could describe the polarisation of many of our systems as a collapsing of that space – a narrowing of the gap, but not necessarily an entire closure.

I find that this thought is useful as a matter of our individual practice in Social Leadership: we see that in many cases our expenditure of our Social Currencies, like kindness, trust, and pride, happens within existing social structures, and within systems of unity. But we may choose to consciously identify not only difference, but the spaces between. And these may be tangental to our opposition.

These are spaces in parallel, or surrounding, our difference, but which may be explored to test ideas of commonality, or to negotiate shared understanding.

The opposition of disagreement does not have to be full agreement: it may lie in a narrated difference, where we can at least articulate what holds us apart.

This may sound idealistic, and yet our differences, for all their emotion, are rarely intractable, if we can find the gap, the lever, and the understanding.

There is an irony that many modern Organisations describe ‘difference’ as a strength, and yet ‘difference’ is where our conflict is held.

Perhaps one way to consider it is that ‘difference’ is a landscape that can be mapped, and in doing so we may discover new pathways.

Considering Bias in Generative AI, in 250 Words

We consider something biased when it gives disproportionate weight to one particular perspective, feature, or output.

Humans tend to be biased because we form our worldview largely from personal experience, and through what we read, watch, are taught, or told. And this is, itself, biased – by culture, representation, and the social ‘norms’ of the day.

In this sense, bias may be bad in outcome, but not necessarily held in bad ‘intent’.

Generative AI tools do not have ‘intent’, although they may be coded to prioritise certain outputs (or avoid them – for example, not being ‘allowed’ to write rude words), and they are ‘taught’ (although not in the same way we ‘teach’ children).

In some ways, what we see is Generative AI mirroring the biases of the societies that build them (or at least in the artefacts of that society that they are ‘fed’).

And hence because ‘society’ shows bias, so too may Generative AI.

This is a slightly simplistic view: AI can demonstrate bias for reasons other than a poor choice of training data. The ‘coding’, or their parameters of operation may induce bias too.

But we should not conflate todays outcome with tomorrows potential.

Humans are inherently subjective and flawed. It’s arguably what makes us human. But AI tools are not: we can build them better.

With care, Generative AI may not only be unbiased, but may help us to identify and tackle our own imperfections.

Rejecting, fearing or banning them may demonstrate a lack of foresight or understanding.

The Impact and Opportunity of Generative AI on Learning in 250 words

To find simplicity, we sometimes need to travel through the complex. Here i’ll share a simple view about Generative AI and learning in just 250 words. Better not waste any.

‘Generative AI’ describes a family of algorithms that deliver seemingly realistic answers to pretty much anything you ask, with variable levels of accuracy.

They have ‘learned’ to do this by analysing vast amounts of what has been created before, because there is a pattern to language. If you ‘read’ enough of it, It can be computed.

They may appear human, but are not truly ‘intelligent’: they cannot carry out logical reasoning or explain how they found the answer.

To Google something feels like an act of interrogation. When chatting to a Generative AI, it feels like a conversation. And like all good conversations, it can ramble off in different directions.

Generative AI can support dialogue in learning and hence unlocks curiosity.

This technology is being embedded into various systems, supporting our writing, meeting, and learning. It can take notes, summarise and prompt, offering alternatives and contextual feedback.

Generative AI is a supporting technology, not a magic answer, but we’re at an inflexion point.

It will commoditise things like individually contextualised feedback that used to be costly, and it can calibrate it’s responses to be more creative or factual.

We’ll need to create new technical and creative roles, whilst some existing roles will be invalidated: writing, design, illustration (through similar technologies), even analysis will change.

This is a time to learn.