The Intent of Kindness

To act in kindness is to help others, not for your own benefit, but solely for theirs. Or so thought Aristotle, one of many social commentators who has professed a view.

Why Kindness Counts

In the context of a reputation economy, i guess that one could take a more cynical view, and say that to act kindly delivers the tangible benefit of reputation, which is increasingly quantified back into more traditional markets. We could effectively claim that ‘kindness pays’, in dollars and pounds. But to act in that way may be to reduce the quality of kindness to mere transaction, and the ‘help’ given to utilitarian, not social.

The ostensible gifting of things, a feature of certain US chat shows (where every audience member walks away with a microwave), and a mainstay of points based reward programmes, is not specifically an act of kindness, although the action may be deemed generous (which equates in some case to kindness). Or to put it another way, we may be unintentionally kind. Or an action that is deemed to be e.g. marketing, may be received in kindness.

But i think that most of us would define, or at least consider, kindness something more ethical and possibly intangible. Perhaps a value that we live by. Perhaps in part it is the intention behind the action: if you gift something, if you are generous with tangible assets, in expectation of reward, then that itself denies you the badge of kindness. Or to put it another way, you may give things away and be kind, but if you transact the very same things in expectation of reputation, you are not being kind. Indeed, you may be being cynical, or manipulative.

In that sense, kindness falls into the category of qualities that are awarded in judgement, not claimed in expectation. So i may act, through whatever imperative, and be judged to be kind, or otherwise.

It is this view, the judgement of kindness, that is perhaps most relevant to consider in the journey and context of Social Leadership: we do not set out to develop a transactional route to reputation, an instruction book for kindness, but rather we curate a space, build our mindset, and take action, that is value led, deeply fair, and continuously reflective. And in doing so, we may be kind.

A place to focus on may be that statement, ‘take action’. If we establish that action itself does not qualify automatically as kindness, then we must beg the question, can you be kind, but not take action? Can kindness be held solely through intent? Or aspiration?

I doubt it: if our intention is to be kind, and our actions are unkind, then in judgement, we are unkind. If our aspiration is to be kind, but our action is selfish, mean spirited, or unfair, then we are not kind. As with any value, holding it alone is not enough.

So perhaps kindness lies in an interplay of intention and action: an intent may inform action (but does not determine it), and action may be deemed to be kind. An imperfect definition, maybe, but possibly workable, at least at some level.

Considering Social Leadership, we can build our awareness of values, and hold an intention to be kind, and we must take action, but not purely transactionally. Which may be a balance so nuanced as to be practically impossible. Indeed, that would not be a wholehearted surprise: kindness may only ever be a thing that we imperfectly aspire to, judged in totality, over time. Certainly that ‘judgement over time’ is a common feature of aspects of the reputation economy.

So you cannot simply intend to be kind, not can you simply act your way to kindness transactionally. It is, perhaps, a striving.

I would not necessarily argue that Organisations can be kind, but they can be led with kindness, and their systems and rules may hold kindness as a value. Certainly, as leaders we can be kind.

I’m also unaware if there is a taxonomy of values: do some count for more than others? But would certainly argue that fairness is a more deeply distributed value e.g. you can always act in fairness, even if you do not actively help someone without expectation of reciprocity. And Organisations can act fairly in all contexts, be that in how they handle their customer service, to the ways they make people redundant. Indeed, in that context, you can be fair, even when taking something away, so long as you treat everyone in an even handed manner. So you could not be fair by making one person redundant, and retaining another, if you made that decision purely on how much you liked that second person.

To be a Social Leader is to act in ways that are deeply fair, and hence thoroughly transparent. And if we are thoughtful, we can act in kindness too. Certainly, we can, and should, strive for that.

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Learning Science: The Problem With Data, And How You Can Measure Anything

Today i am #WorkingOutLoud, sharing a section of the writing on Learning Science from the Modern Learning Capability Programme. These three pieces discuss qualitative and quantitative data, and how you can measure anything. This is early stage work, shared out of context.

Quantitative data is that which can be caught in numbers, measured precisely, and replicated. Scientists love quantitative data, as it has a certain unarguable quality. For example, if i weigh myself, and say that i am 75 kilos, you could weigh me, and i would still weight 75 kilos. Unless your scales were broken, but then i could quantify that, by doing comparative tests (weighing a bag of sugar on each, and demonstrating the difference).

But perhaps you would weigh me and say that i weigh 11.8 stone, but that’s ok too, because the two different scales of measurement are correlated.

Correlated scales use different gradations, or gaps, to measure something, but the thing that they measure is the same. So, once you have a couple of data points, you can correlate the scales. [e.g. if you measure me, then measure an apple, in both scales, then you should be able to figure out how each scale runs. This works the same for the temperature too: i can sunbathe in centigrade, Celsius, or Fahrenheit, all at the same temperature.]

Qualitative data, by contrast, is observational: it characterises, or approximates, the thing itself. So you could weigh me and say that i am 75 kilos, or you could try to pick me up, and say that i am heavy. Both are correct. The first is quantitative, expressed as a number, and measured in a replicable way, and the second is a characterisation, which may be unique to me.

My son, at four months, weighs seven kilograms. You could measure him (if you could get him to sit still for long enough) and he would weigh the same, even on your broken scales [Remember, your scales record a number, not an absolute: so your scales may say 1kg, but that’s not the same thing as a scientifically defined kilogram. Yes: there really is a block of metal, sat in a series of nested glass bell jars, in Paris, that is that actual, and only, real kilogram. Every country has a series of local weights that balance against that. You couldn’t make it up. Although it’s on the way out… because every time it is picked up, or a stray atom falls off, the ‘kilogram’ legally changes. If you polish it, you actually change the legal and scientific standard. Which is why it’s being replaced by something altogether more complex, involving clever scales, which i will let you google for brevity.].

You may say that he is light. But for me, he has got a lot heavier since he was born, weighing just under four kilos. So i think that he is heavy. And we are both right. That’s the issue with qualitative data: he is both light, and heavy, at the same time as weight exactly seven kilos. Or probably eight by the time i finish this explanation.

No problem, you may say: stick to quantitative data, and everything will be fine. Except that whilst it’s easy to measure weight, other things cannot be measured quantitively. Like happiness. Or God.

I cannot determine if you are happy or not: i mean, i could measure levels of your stress hormones, your pulse, skin conductivity, and take a look if you are frowning, but none of those would definitively tell me how you feel. But if i ask you, i would probably know.

Qualitative data is typically self reported, and concerns feelings, subjective interpretation, and belief.

Sometimes researchers try to constrain this, by presenting options: instead of asking ‘how do you feel’, which may produce an annoyingly diverse range of answers, they may say ‘Do you feel [a] happy, [b] sad, or [c] confused’. Indeed if they are good at research, they may first ask a hundred people to give free form answers, and then analyse those to produce a subset that best represents the range of expressed options, but constrains them down to e.g. 3.

If they then use that scale to survey say a thousand more people, then they could come up with a result that says ‘400 people (40%) are happy, 200 people (20%) are sad, and the rest (40%) are confused. Which sounds pleasingly quantitative. But it’s not: this is a case of generating quantitative data from qualitative research. The number describes the result, but the result is a subjective observation: just because we have a number does not change the nature.

Problems with data

There are many ways that data are manipulated, or misunderstood, to cause confusion, or to deceive, by design, or accident.

Masquerading qualitative as quantitative is one way, but do remember that it’s also done to carry out valid interpretation, and to create presentable results.

One of the biggest problems is that just because we can measure something, does not mean that it is valid data. Earlier i said that i could measure your skin conductivity to see if you are happy. But how do i know that skin conductivity relates to happiness? It’s good practice to track back, to discover if what we measure actually correlates to the thing itself. It may be that i can measure ten different people, and get ten different skin conductivity results, but that may not correlate to their state of happiness. Maybe some of them ran here, and skin conductivity relates to sweatiness and fitness!

Another problem is when we select data to prove a point: let’s say i am measuring happiness, and i survey 500 people and the final result is that 40% are happy. Chances are that as i collect the data, the results fluctuate, so in the first two hundred people, sixty percent were happy, but the final three hundred dragged the average back down. So why don’t i just survey another two hundred, and perhaps get lucky, and hit a happy team? By so doing, i am still measuring a thing, but i get to 50% happiness. Which is real?

This is why researchers need to define what they will measure at the start: we are taking a snapshot. If we move the camera, we will blur the picture.

Finally, there are issues where we selectively choose perfectly good data to support a point or conclusion that we wish to draw: conclusions should be drawn from data, not data to support a foregone conclusion.

Why you can measure anything

The qualitative to quantitative switch means that you can measure anything; how you feel about breakfast, the weight of your shoes, or the validity of democracy. But it does not mean that the scale of measurement you choose, or the mechanism of measurement, is valid. So measure anything, but do it with care. And be both wary and careful of the measurements that people give you to prove a point. Especially when they are charging you for it.

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Prototyping the Podcast

I’m running a series of six podcasts, to prototype a format: each one takes a blog post, which i read out, adding context to the original writing, and also sharing how my own thinking was shaped, or has evolved, since writing it.

I’ve selected a range of pieces, which cover complexity, Social Leadership, Social Learning, and aspects of the broader Social Age. At this stage, i’m primarily interested in two things: does the podcast format allow me to add value, and can i fit this into an easy workflow.

The first two are now live, and subsequent episodes will release on Thursday each week.

After six weeks, i will likely run another series in a different format: from this first series i want to crack the technical aspects of recording and workflow, and in a second series continue to explore format. So an iterative approach.

That being said, i am open to feedback: i am doing this to see if it opens up a more accessible format to busy people, and to see if i can ultimately build in interviews with the many and varied people i meet as i travel around the world (which presents another workflow/technical challenge).

Let me know what you think.

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What I’m Thinking About: Domains of Knowledge

I write the blog four days a week, Monday to Thursday, but on Friday i write a longer form newsletter for my subscribers. This forms a deeper reflection, a commentary on certain news items, and a broader perspective on the Social Age. Over four weeks, i’m also sharing it here on the blog too (my readership here is about ten times the size of the newsletter subscription). I hope you enjoy it.

Newsletter 82: Plateaus

There’s a bar in London powered by AI: in certain zones, facial recognition technology will identify who got to the bar first, and line them up in a virtual queue, circling faces on a screen so that bar staff can work out who to serve next. The service is offered as a reasonably cheap subscription for the bar, with some data (generated by the company that makes the system) about how it can increase profit and volumes of alcohol sold.

I’m interested in it from a different perspective: the evolution of dominant narratives, in this case, the specific social context of queueing. Growing up, entering the social scene, was often a journey of learning how to queue and get served at the bar, with a whole range of concomitant social behaviours, rituals, and cultural connotations.

For my American friends, who have not visited the UK, it may be worth mentioning that the experience of a bar is very different on both sides of the pond: in the UK, bar staff are paid (usually minimum wage), and your position in the serving queue may be a function of aesthetic appeal as much as order of entry. In the US, where bar staff are tipped, you can more readily buy your way to good service. Certainly I have found that the bar area in the US is a far more social scene than here in the UK, partly because bar staff have a vested interest in making you feel welcome, but also because remaining sitting at the bar is more accepted and normalised.

Here in the UK, I have to resort to prominently holding a ten pound note, catching people’s eyes, and engaging in the time honoured ritual of pointing to my neighbour and saying ‘he was here first’. I once waited forty minutes to be served on New Years Eve.

Facial recognition and crowd filtering sorts all this out (although imagine if it introduced an overlay of who tipped best, and hence imposed a taxonomy within a queue). Time at the bar could be reinvested: you could read a book, check your emails, or even talk to a stranger, with no need to keep pushing to the front, or figuring out who smiled at you last. In such a world, what do we lose, and what do we gain?

This is the social context of technology: it facilitates and enables, it reinforces and promotes difference and inequality, it provides fairness and democratisation, as long as you can afford it. But certainly it is challenging and fracturing dominant narratives of the past, and providing space for new ones to emerge. In my own writing, I am finding the language of Dominant Narratives, to describe existing social scripts and behaviours, particularly useful to understand and describe this.

My Writing

Landing Apollo

Last week I shared the final piece of writing about Apollo, a piece that was harder to write, describing the accident on Apollo 1, and the death of the three astronauts: I used this to consider complexity, risk, and humility, or arrogance, of both systems, and leadership.

A Rough Path Leads To The Stars

Exploring Learning Science

From then, I’ve been focussed on the new work around Learning Science, which is the first module I’m building out fully for the new Modern Learning Capability Programme.

This first piece aims to set the context: that ‘Learning Science’ is a broad discipline, based upon multiple established sciences, and that our role is not to ‘master’ it, but rather curate a space of interest.

Learning Science

This second piece encourages individuals to consider the Organisational philosophy of learning, and recognise and reflect upon reductionist, constructivist, and emergent approaches. I suspect that this second piece is a clearer indication of where this work is evolving.

Learning Science – WorkingOutLoud on Philosophical Approaches and Science

I am an Island

This was a short piece, written at the end of a busy day, but often brevity equates to clarity, and I rather like it: it considers how we are each, ultimately, an island, holding our own personal understanding of ‘meaning’, and making occasional voyages to share this with others.

I an an Island

This final piece starts to build out the Learning Science work: it begins to explore existing disciplines, and to make explicit links back to what it means in practice. This area will be my focus next week.

https://julianstodd.wordpress.com/2019/08/08/what-different-disciplines-can-contribute-to-our-learning-science-a-workingoutloud-post/

What I’m Reading

I’m half way through Serhii Plokhy’s book on ‘Chernobyl: History of a Tragedy’, the first definitive account of the nuclear accident, from 1987 right through to 2018. It’s a fascinating read, and three of the first perspectives I’m taking away are these:

  • The level of ignorance and misunderstanding around the risks and radiation are staggering: the reactors were built, and run, on a highly vernacular model. The complexity was managed by committee, and process, not distributed capability and comprehensive understanding.
  • The paralysis of the system in responding to the disaster is a good illustration of where one frame persists well beyond the point at which it is broken: in this case, nobody admitted the reactor had exploded, despite the clear evidence in front of their eyes. The older frame, that this was just a fire, persisted for many hours. This speaks to the risk of speaking out against a dominant narrative.
  • Complexity is additive, a phrase I started to explore in the Apollo writing: a system is not necessarily ‘simple’, or ‘complex’, but rather the layers of complexity create meta effects. That may either be insight, or blindingly obvious, but perhaps something I will think on further.

In the News

AI in the NHS

This piece, which includes an overview of a few applications, provides a timely reminder that AI is most certainly moving mainstream. Aside from the medical diagnostic applications, I thought the point about how AI may help identify those patients more likely to miss appointments is a good illustration of how technology is increasingly disruptive of social norms (or Dominant Narratives), that I mentioned in the introduction.

AI in the NHS

The AI Bar

And here is the press release about Datasparq’s AI powered bar.

Press release: Launching the world’s first AI bar

Educational Disruption

This piece, behind the hyperbole and fear, speaks clearly to how technology will disrupt education. I share it not specifically in terms of the pedagogy, but rather as commentary on how prepared both the education, and technology, sectors are to exploit this. I suspect the next decade will see the widespread intrusion of disruptive, and global, players, possibly with established entities retreating to remain simply as brand names, owned by tech.

A Grand Experiment in Education

#WorkingOutLoud on the Certifications

As you can see from my writing above, I have started the work on Learning Science with energy: I have felt a bit daunted about building out the Modern Learning Programme, because although I have all the material and structure in outline, which I use with small groups, this is a programmatic piece, and will be offered at greater scale.

I am particularly excited that I will be publishing the full curriculum and materials as I go: this will take the form of a series of Social Age Guidebooks, the first of which will be ‘The Learning Scientist Guidebook’. As with the other Guidebooks, these will each be sub 10k words, and have a practical focus.

So far, I feel I am holding my head above the complexity, but yesterday I did find that I lost my way a bit.

The core idea is this: we will explore the landscape of Learning Science, with a view to understanding what each discipline looks at, and where it is heading, and then curate your own personal discipline as a Learning Scientist: this is the key for me, in helping us use the totem of the word ‘learning science’ as a thing, and moving to practical ideas for what we explore, what it will inform, how it is limited, and where it may fail us.

What I’m Thinking About

I’m mainly considering my own learning, in two contexts: often we work within an existing domain of knowledge, creating new meaning, or applying what we know to a matter in hand. But with the Apollo writing, and now the Learning Science, I am consciously trying to fracture or expand my own domain. This takes me to a place that is both more exciting, and more frightening, because certainty comes with familiarity, and in this strange land it is harder to be certain.

I felt this in my writing on neuroscience yesterday: I know my way around this area, I am comfortable with the language, and I have an underlying conceptual model of how it all fits together, but there is, of course, a gulf between the type of knowledge we have, to have to understand something, and the language we need to explain it.

In practice, what this means is the writing sometimes ties itself in knots, as a very visible representation of my thinking taking shape. And sometimes it falls down a rabbit hole altogether.

The main risks are either of staying too high for too long, describing the challenge as infinitum, without ever getting to detail, or conversely, falling into radical detail, and losing momentum for the overall journey.

Part of the reason why I am focussed on writing a new Social Age Guidebook out of this work is that it will force me to work to a 10k word overview, so if I use half of that up with a structural description of the brain, or an interesting aside on imaging technology, I will fail.

The other challenge is that, whilst I consider my understanding to be incomplete in ways that I know, it is, naturally, incomplete in ways that I do not yet understand. Comprehension is always a series of false summits, on a mountain that is infinitely tall.

Or as Terry Pratchett used to say, learning helps you to become ignorant on a whole new level.

Still: I am enjoying the stretch, and better still, enjoying my new plateau of ignorance.

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Learning Science: Neuroscience

Today i am sharing part of the work for the Modern Learning Capability Programme: this piece is an overview of Neuroscience, with a view to understanding how it may inform your personal discipline of Learning Science. Note that this is early stage work, and presented out of context, but shared as part of #WorkingOutLoud. Note also that this version does not include the footnotes that include referencing in the final book.

Neuroscience is the broad study of the nervous system, which provides us with the biological foundation of learning, consciousness, behaviour, memory, sensation, and perception. It is, again, an integrative discipline, which draws upon other fields.

In modern learning Organisations, neuroscience can be toted as a key that will unlock learning and engagement. It can be viewed as a torch shining light over ignorance. But it equally falls victim to totemicism, aspiration, and the projection of bias, with a belief that we can use it to reinforce existing dogma. Certainly our understanding of the brain is improving, but we have not yet ironed the magic out of the system, and it is not a deterministic science.

To best understand neuroscience, and what it can, and cannot, give us, consider again the philosophical debate about reductionism and emergentism: neuroscience can give us a view of the map, and can show some interrelationships and dependencies, it can give us a clean and effective overview of the space, but it does not give us familiarity with a landscape in the same way that hiking through it does. At best, neuroscience will provide us with a foundation for our broader understanding, and the development of our personal discipline of Learning Science, as well as more rigorous, evidence based, and hence effective, learning methodologies.

Considering Plasticity

An important notion is neuroscience is plasticity: the brain is not like your car, or indeed any physically engineered and hardwired system. It is dynamically able to restructure itself, indeed, through cycles of learning, and sleep, it reinforces, prunes, and culls, different connections at will. Put simply, if the brain cannot achieve something today, it may be able to rewire itself to achieve it tomorrow: learning to juggle, reading French, and changing your mind, all evidence of this awesome trait.

The brain is highly plastic, with changes that we can physically measure: in part, our capability may be based upon innate structural differences, and in part, behaviour and experience change that physical structure itself. Some things may surprise you: you can learn a language, for sure, but you can also learn to balance better. Neither is innate, although there may be structural differences that give us a propensity to be better able to learn to do either.

But plasticity can cause us to misunderstand: for example, to see limitation where it does not exist, or to fall into the traps of phrenology. It is worth remembering that the brain is an extraordinarily fluid structure, and that, on top of that, are the correlates of learning, belief, and the creation of meaning, all of which are largely beyond science, at least for now.

Lateralisation of the Brain

Some brain processes or functions may tend to locate to one hemisphere of the brain or other, and there is an active research discipline that explores this, but latterly there has been a great move in pop culture (and related management theory) to draw conclusions that simply are not true, or at the very least are beyond our current knowledge.

You will see studies of gender based differences, even the notion that there is a ‘male’ and ‘female’ typical brain, almost all of which is soundly without basis. As mentioned elsewhere, we also see great plasticity, so functions in one person that are predominantly based on one side of the brain may, following injury or disease, migrate to the other.

It’s possibly safe to say that there are lateral relationships with function, but that they are plastic, and that, in all practical terms, of little or no use in our understanding of the practically applied business of learning and development, leadership, or change.

On a related note, we can see that almost the entire conversation about emotional intelligence is build upon quicksand: there is no demonstrated consensus of what evidence would form an ‘emotional’ intelligence, and no experimental mechanism to measure it, as opposed to other ‘types’ in any event.

It’s not to say that work on Emotional Intelligence cannot teach us anything, so long as we consider it as a theoretical framework, a way of thinking about intelligence. But it is neither deterministic, not evidence based, and hence, simply one idea among many.
Observation and Imaging

Much of our understanding of how the brain works comes from observing dysfunction: we have a large sample size of ‘normal’ brains to observe (not that there is anything like a ‘normal’ brain), but when something goes wrong, we are able to see what is lost from (or indeed enhanced in) normal function.

For example, through illness, a lesion forms in the brain of a particular patient, and that patient loses the ability to speak, although they can still sing. From that, we may deduce that the lesion is in part of the brain that governs speech, and that song is held in a different space. Or maybe the lesion simply blocks the initiation of speech by putting pressure on a different area.

That’s important, because understanding ‘initiation’ or actuation, is important in understanding the brain, both from an electrochemical, and behavioural, perspective.

Take right now: i’m sat in a cafe, typing with the sun shining onto me. I am therefore warm, and sat in the most comfortable seat, but also the screen is increasingly hard to see. I am annoyed, but not yet so annoyed to bother doing anything about it. You can consider the initiation of behaviour, or indeed much motivation, at both the cellular, and macro, levels in similar vein. One nerve firing may not initiate action, but builds a shallow foundation that others build upon. When a certain critical mass is reached, action may be initiated.

The real revolution in neuroscience has come through the advance in imaging, and measuring, technologies, which allow us to look inside in ever greater detail. These broadly divide into ‘structural’ imaging, which as the name implies, looks at the macro physical layout, and it broadly concerned with injury and structure, and ‘functional’ imaging, which seeks to understand function on a micro level, through measuring e.g. blood flow, or electrical signalling, as well as proving insight into e.g. Alzheimer’s, where changes are much more fine grained.

Examples of what neuroscience looks at

  • Structures of the brain, and how structure relates to behaviour or capability: for example, are there structural differences that correlate to creativity, agility, bias etc
  • Mechanisms of encoding: do we learn more if we read something twice? The roles of reinforcement, repetition, manipulation, reflection and personal articulation, the role of time (spaced repetition) etc. This is essentially the foundation of how we learn.
  • Differences between people, either within a broad population, or related to e.g. age, or structural differences in the brain (which is often contentious e.g can you spot the physical indicators of genius, or homosexuality? The temptation to do this is clear: you can spot an Olympic athlete by their muscle tone, so why not Beethoven by his brain?)
  • The relative effectiveness of different things e.g. different therapies to treat depression, or the impact of violent images on compassion etc
  • Engagement: why, at a neuro-cognitive level, we seek to belong, or engage with things.
    The neurological basis, imperative, and limits, of behaviour, and behavioural change. This is about the evidence basis for pretty much any learning or development activity, and especially behavioural change, and hence culture change.
  • Unconscious aspects of behaviour, often around bias (relating to race, gender, sexual preference, identity etc), but also around attraction to new ideas (marketing and advertising), as well as belonging (politics).

Myths of Neuroscience

  • Neuroscience can give us cause to believe things that are not, or may not be, true, as well as drawing conclusions too early.
  • Notions of ‘left brain’ and ‘right brain’ activity may not take account of plasticity, and may provide a simplistic, yet incomplete, picture.
  • A myth outside neuroscience, but presented as part of it, Emotional Intelligence makes claims that are not validated at even the conceptual level. Most likely, conversation about emotional intelligence is just reflecting on aspects of general intelligence.
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My Father

My father died yesterday. A peaceful end to a long and eventful journey. I am overwhelmingly sad, tempered by a gratitude for what he gave us, and how we were able to be together at the end. Writing about it is the only way i know to make sense of the story: sometimes you just have to write, no matter how hard it is.

Of course, i feel a certain pressure to write something good: i can sense the look he would give me if i resorted to sloppy eulogy or, worse, poor grammar.

To know my father is probably to know me: i have been fortunate to inherit some of his better qualities, and a fair few of his worse or more annoying ones.

When i say ‘better’ qualities, i mean those things he did as I was growing up that drove me to distraction: questioning, probing, and generally embarrassing me in front of my friends by demanding clarity and detail. In other words, the questions i ask myself now, the questions that make my own work occasionally strong, are the questions he instilled into me.

Not, i should say, through any formal teaching: i am for the life of me at a loss to think of a single time when my father taught me how to do anything well. His approach to parenting appears to have been to give me enough space to learn, and enough safety to trip up as I did so. Whichever direction we turned, our parents just gave us an open door (and often a taxi service to get us through it).

Of course, in the other column (and possibly an explanation for why they gave so much space) is that i am as stubborn as my father was. And as annoyingly opinionated at times. Although doubtless we would both claim that by ‘opinionated’, what you really mean is that we stick carefully to a well thought out position.

My father was a man of strong faith, as well as an accomplished academic, and i remember once having a lengthy conversation about the Anglican church’s view of homosexuality. The next time i visited, i saw that he had been reading a book about it. That’s another habit i have picked up from him: to be curious and i hope rigorous, and open to change. I feel sure you can find plenty of times when both of us were wrong, but few times when we were unwilling to change position if the argument was good.

Growing up, all of our shopping lists were written out on strange little rectangular cards: i did not know it at the time, but these things (boxes of which remained with us for decades, and still occasionally turn up as the bookmark in an old book) were punch cards for the giant mainframe computer in the basement of City Hall, that he had learned to programme and used for statistical analysis of his PhD, and then later to support other research.

I’m still unclear how father surfed his way into sitting at the heart of IT in his academic institution, but i suspect by seeing the mountain of the future and then climbing it. Because his first and, i suspect, enduring, love was of the outdoors.

Our standard family joke when growing up was to go on a long drive somewhere, only to hear father proclaim “i’ve canoed there!”

Before he was a father, and before he was an academic, he was an Outward Bound instructor, and his tales of camping, hiking, sailing, and kayaking, were doubtless what inspired me to find my own love of the outdoors. I only told him recently, but on all of my biggest adventures and expeditions, i carried the compass that he gave me.

When i hiked coast to coast at age 30, a fairly epic adventure that saw my friend Paul and i covering twenty five miles a day for two weeks solid, we got to the distant shore, waded in to the sea, then made for the nearest payphone. Back in the days when you had to carry coins, i had several five pence pieces ready for occasion, and i remember clearly when, over the crackling line, he told me that he was proud of me.

I grew up thinking that my father was an ‘old father’, as he had been slightly older than the parents of some of my classmates. So it was with considerable surprise that i realise i was a full twelve years older than he had been when i became a father myself. And more surprising still when i realised how totally unprepared i was. Not in the practical details, but in terms of the responsibility to create those open spaces, to instil those values, and to be the person who says that they are proud, not just of the big things, but the little things too.

As i try to find my own role, i will try to carry forward those things he taught me, not by teaching, but by being.

I suspect that my father was sometimes surprised by how different we were, but if he looked closely, i am sure would see what i guess i will see in my own son: that we are born in the shadow of our parents, but find our own light, that we are an accumulation of the things we are given by our parents, and the space we have to grow beyond them. We are all different, and yet recognisably the same.

As our own son reaches four months old, i feel acutely the responsibility to stand over him, to protect him. At this age, it is visceral: i ache when i am not with him, or able to comfort him. But the lesson i hope i can carry forward is that this responsibility changes and evolves over time. Perhaps at first we have to hold our children safely, but then later we just have to stand aside and let them find their own path. But what would i know: i am at the start of this journey.

I will be forever grateful that my father was able to meet, to hold, and to smile with, my son: whilst i feel loss, and great sadness, i also feel that this is the journey that we make.

And perhaps also something more: i think that i had always looked at the end of life with a fear of it’s finality. Certainly, i had rehearsed the first words of this piece in my head, time after time, as we felt the end draw near, with sadness. But i realise, with some surprise, that this is the last gift that my father has given me. The knowledge that a long life, lived humbly, and lived well, a life dedicated to family, to community, and to the search for knowledge and meaning, is a life well lived. And that, if we live that life, there is little to be afraid of at the end.

For now, i just want to focus my time on our family, to be together. But as we move forward, i still have my compass. Not that one that sits in my desk drawer, but the one who lives on in my heart and my head.

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What Different Disciplines Can Contribute To Our Learning Science: a #WorkingOutLoud post

This is a #WorkingOutLoud post, sharing a section of the work i’m doing on the ‘Learning Science’ module of the ‘Modern Learning Capability Programme’. In this section, i will share an overview of different scientific disciplines which may form part of ‘Learning Science’, and consider what, and how, they contribute. This is very early stage work, so very full of holes, but i hope you can see the emerging structure.

As we curate our personal discipline of Learning Science, we can consider the different buckets that things sit within.

Some fields of science explore the individual: how we work, how our brains and bodies develop, and the different ways we learn to operate at each stage. Also the ‘software’, exploring consciousness, and everything it means to be a functioning, conscious being.

Some fields explore the physical context of learning: environmental factors, as well as broader cognitive aspects of culture.

Some fields explore the social context: communities, teams, interpersonal dynamics, networks etc.

The Personal Bucket: How We Individually Learn

This may include:

  • Structural understanding of learning: neuroscience, physiology, anatomy, etc which let us understand memory, retrieval, reinforcement, practice, building manual dexterity and fine motor skills, physical prowess, refined skills etc. Also let’s us consider different modalities e.g. video, audio. Let’s us understand building blocks such as repetition, manipulation, practice, as well as differing ability and loss of function.
  • Emergent understanding of learning: consciousness and ideas, intelligence, bias, thinking, creativity, imagination, storytelling etc. Essentially anything that cannot be directly observed, but must be articulated or experienced. Let’s us understand belief, discrimination, motivation, engagement etc

The Context Bucket: The Environment We Learn Within

This may include:

  • Environmental factors: heat, cold, noise, distraction, etc, may help us to understand or plan for utilisation, rehearsal, experience etc
  • Cognitive or Cultural factors: complexity, risk, consequence, opportunity reward, will help us to understand engagement, confusion, adoption, adaptation, subversion, spread and amplification etc

The Community Bucket: The People We Learn With

This may include:

  • Sociological factors: trust, pride, respect, empathy, recognition etc, helps us to understand conformity, control and structures of power, consequence etc
  • Linguistic factors: language, jargon, dialect etc let’s us understand tribes, teams, conformity, efficiency, identity etc
  • Cultural factors: difference, exclusion, permission, curiosity etc which let us understand framing and perception, wilful blindness, change etc

Let’s take a tour through some of the relevant scientific disciplines, and consider what each can contribute to our overall approach.

Structural Sciences: Looking at the Brain and the biological basis of learning

When relating to learning, these sciences are typically described under the broad discipline of Neuroscience, although they can relate to the broader functioning of living organisms too.

Physiology

A branch of biology that looks at the normal functioning of the human body, and it’s constituent parts.

Why is this of interest to Learning Science?

Our physiology is our spaceship: the vehicle in which our minds navigate the universe. So it gives us both capability and constraint. There are a range of areas or key learning which may be relevant. For example:

  • How strong or dextrous you are will impact your ability to use tools. Tool use and manipulation of objects has a feedback loop to learning.
  • The size, and structure, of various organs (like the brain, stomach, muscles) will impact on their efficiency or capability, which can all impact on physical disciplines, from endurance to cognition (the brain needs energy and security).
  • People get seasick in VR environments because of the way their peripheral vision is processed: understanding this will help us to create useable learning environments.
  • There may be a correlation between movement, and empathy: this will impact effectiveness of learning design, especially between e.g. passive experiences, such as eLearning, or video, interactive ones (scenario based) or VR ones (experience based with movement).
  • Height and success are correlated: and if taller people are more confident and successful, then VR allow us to engage in a ‘body’ that is taller.

Myths of physiology:

Persistent myths include: male vs female structural differences and impacts on ability, which persist in dominant cultural narratives

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