Sep/091
Surviving the skills-learning labyrinth
How do you and your staff learn new skills? And what can be done to make it quicker and easier to learn those needed skills? One answer is to explore the patterns in the skills-learning process.
On the surface, each skill is different, and different for every person; yet there are also patterns in the learning-process that are the same for every skill. The most common view, perhaps, is that skills-learning occurs in a linear sequence, with identifiable periods of practice needed to achieve distinct levels of skill:
- pick up the basics (typical: 0~10hrs)
- start out as a trainee (typical: 10~100hrs)
- learn a bit more as an apprentice (typical: 100~1000hrs)
- apply the skill as an independent journeyman (typical: 1000~10000hrs)
- achieve acknowledged mastery (typical: at least 10000hrs)
Whilst that’s largely true, the learning-process within each of those overall stages is nothing like a simple linear progression. Instead, thinking side-wise, it follows a pattern that’s more like the classic seven-turn single-path labyrinth found in ancient Crete and many other cultures around the world.
The skills-labyrinth
There’s only one path in a labyrinth: as long as we do keep going all the way, we’ll achieve the end-point – in this case, mastery of the skill.
In colloquial English labyrinth is generally synonymous with maze, but many contemporary scholars observe a distinction between the two: maze refers to a complex branching puzzle with choices of path and direction; while a single-path (unicursal) labyrinth has only a single, non-branching path, which leads to the center. A labyrinth in this sense has an unambiguous through-route to the center and back and is not designed to be difficult to navigate.
So in principle, “not difficult to navigate”. But despite the simplicity, there are all too many opportunities to get lost along the way… So the following is a brief summary of the various stages in the journey through the skills-learning labyrinth, using traditional names for each respective phase.
(prelude) Beginner’s Luck
Starting from Beginnings, we move almost immediately to a point where we seem to have a kind of mastery – but only for a moment. We often succeed, in fact, because we know so little about what we’re doing – which itself can be a source of many difficulties further down the track. We then have an explicit choice: to back out, avoiding any commitment to the skill; or ask “How did I do this?” – and start on the Journey.
(1: Third loop) Control
This phase emphasises Training, moving slowly towards the Apprentice stage. Much of the time the focus will be on rules – the ‘Simple’ domain, in Cynefin terms – and on analysis – the Cynefin ‘Complicated’ domain. Those rules and analyses do seem to give a sense of control, though it’s nothing like the ‘instant mastery’ we achieved back at the very beginning. Yet every now and then things seem to break down – the ‘best-practice’ rules somehow don’t work for us, in our own specific context – and it becomes clear that we are part of the process. At some point, then, we must change direction, and look inwards. Until this point, everything we’ve done should (in principle, at least) have been the same for everyone; this change in direction is also the moment at which the practice changes to a true personal skill.
(2: Second loop) Self
In this circuit we explore our own involvement in the process – the parts of the skill that are specific to us alone. Often there there’s an new emphasis on patterns, on emergence – the Cynefin ‘Complex’ domain. But despite the increasing excperience, and despite knowing more – and having to face challenges of our own that we now need to address – we find our mastery seems to be getting steadily worse. The further we move along this path, the worse our skill will seem to get – until eventually it seems no better than that of a rank Beginner. At that point, it seems self-evident that looking at self was the wrong way to go: so we change direction, trying to revert to ‘the Rules’ to get our skills back on track.
(3: First loop) Survival
This doesn’t do what we expected. The turn-round takes us outward, not inward; far from bringing us back to Control, it takes us to the Cynefin domain ‘Chaos’… Although ‘the Rules’ haven’t changed, we have – and it’s all too easy here to fall into the dreaded ‘sophomore slump‘. In particular, there’ll have been a key personal shift, from ‘unconscious incompetence’ to ‘conscious incompetence’: but an unfortunate side-effect of that increased awareness is that we can now see that ‘incompetence’ – hence it will often seem that nothing works. Stuck on the outer – in several senses – this can seem like a struggle for survival, an endless cycle of “practice, practice, more #!%*&%*! practice”. And comparisons with others only make it worse: everyone seems better at this than we are. This is the worst stage of the Labyrinth, and by far the longest… and as with the previous loop, the longer it goes on, the worse that feeling gets.
(key-point) Dark Night of the Soul
Then comes a key point – classically the day before the exam, or just before (or after) the presentation to the Board – where we’re brought face to face with our apparent incompetence. We realise we’re further away from mastery than when we first started: seems we’re not just worse at this than a Beginner, some raw recruit, we’re no good at all… Traditionally described as the ‘Dark night of the soul‘. this bleak moment of despair can also be called the “Oh, @#!* it!” point.
- It’s crucial to understand here that this period of despair is a normal and necessary stage of the skills-learning process – a crescendo of ‘conscious incompetence’ that is the gateway to the beginning of ‘conscious competence’.
Whilst the despair is all too real, and may well seem as if it will last forever, there is a way through – if we can find the strength to keep going. The danger here is that if we give up at this point, walk straight on and break out of the Labyrinth – as the steep turn encourages us to do – we lose everything we’ve gained, except for a large dose of disillusion… Instead, the key is to trust – ‘to listen to the heart’ – and choose to care about the skill for its own sake rather than for any extrinsic reason. By accepting that we know we don’t know and can’t know – a return to the Cynefin core domain of Disorder, a “surrender to the ‘cloud of unknowing’ and the ‘cloud of forgetting’”, in traditional terms – there’s a sudden breakthrough, a change as fast as that at the beginning: from Chaos we suddenly find ourselves almost at the centre once more. A brief moment of calm: then the Journey continues, changing direction again.
(4: Fourth loop) Caring
Here, for the first time, our effective skill at last extends beyond the best of training – though it’s been a long haul to get here. It also never falls back below that level, as if at least this level of skill has become ingrained into our very being. But there’s another important twist, because, as indicated by the current scientific research on extrinsic versus intrinsic motivators, the usual external ‘carrot and stick’ motivators – promises of reward, or threat of punishment – that pushed us to succeed at the Training levels not only cease to work here, but often make things worse, damaging the quality of decision-making and the like. (In that sense, banks’ bonus-schemes were almost certainly a primary cause of the current global financial crisis.) What does work is caring: finding value in the work itself, and what it means in terms of personal and shared values. So to go further into the skill, we need to care about what we’re doing and why we’re doing it, and care about the skill for its own sake: in effect, “a commitment of the heart as well as of the head”.
(5: Seventh loop) Meditation
At another key point, the quiet euphoria of the previous stage fades as a new focus comes to the fore. This is a different form of observation and self-observation which could be described as ‘thinking about feeling’ – a kind of meditation, a deep, often intense and personal absorption in the work and its processes, yet at the same time seemingly almost detached from it, as if observing from the outside. This sense of engagement in the context is essential for successful action in the Cynefin domain of true Complexity. For a while – and especially to outsiders – this may well seem like mastery: yet there’s actually still quite a way to go before we get there.
(6: Sixth loop) Mind
In yet another disorienting shift of perspective, ‘thinking about feeling’ becomes ‘feeling about thinking’, as the previous changes in practice become embedded at a much more visceral level. For some skills this will literally be ‘embodied’, as in the development of ‘mechanics’ feel’, or the subtle delicacy of touch so essential to true musicianship. In the ‘knowledge worker’ skills that are more common in the business context, this would be embodied more as the deep-learning expressed in an experienced manager’s intuitive grasp of a complex real-time business process, a product-designer’s ability to elicit customers’ real unspoken needs, a trader’s test and trust in hunches and ‘gut-feelings’ about the subtle ebbs and flows of the market. This kind of awareness and sensitivity is essential to work well in what Cynefin describes as the Chaotic domain – the domain of inherent uncertainty, the salesman’s ‘market of one’, this person, unique, right here, right now. The mind here helps us make the link, back through principles and patterns to everyday practice, though in a way that sometimes seems quite opposite to the way we used the mind when – so long ago – we thought we were in Control.
(7: Fifth loop) Communication
Another mode of thought comes through, to provide reflection and review between sessions of practice – typified by techniques such as the US Army’s deceptively simple After Action Review. Sometimes it may seem as if the skill-level is falling once more – an apparent echo of the struggle back at the Survival stage – but in fact this impression arises solely because we’re paying more attention to the fine-detail of the work. To help us learn more, and also to challenge us to greater competence, we’re also likely to need mutual support from and with our peers – a community of like-minded people with similar skills and similar concerns and interests. The other key theme of communication here is that of helping others to find their own skill. Often this will spring from a kind of altruism: the renewed self-doubt, though much quieter than that in the Survival stage, leads to a sense that even if we ourselves may never reach the pinnacle of mastery, we can perhaps do so by proxy, through helping others to reach it in our stead. Yet this activity of educating others also helps us in our own process of reflection: it’s often said that the last stage of learning is to teach it to others. The result, usually unexpected, unheralded, and without any warning…
(postlude) Mastery
…is that we discover that we’ve reached mastery of this specific skill. Yet here we also find that the skills-learning labyrinth has an even stranger twist: it’s recursive, nested, fractal, in that the same overall pattern occurs simultaneously on many different levels. We can be struggling with the Survival level in one skill, or one part of a skill, whilst also experiencing the elation of Beginner’s Luck, the quiet of Meditation, the information-overload of Control and the despair of the Dark Night of Soul in others, all at the same time. Hence plenty of opportunities for confusion, for losing one’s way even in such a simple structure with only one path.
There’s also a social dimension in this. With each circuit, the path alternates from clockwise to counter-clockwise, with the result that everyone on the immediately parallel path – usually either one step ‘later’ or ‘earlier’ – will seem to be going the opposite direction. On top of this, earlier skill-levels will often seem ‘better’ – closer to mastery – than later ones: things seem to get steadily worse as we go onward yet outward from Control to Self to Survival, for example. So others will often try to ‘help’ by telling us we’re going the wrong way, or that we’re doing the wrong things; and we’ll no doubt do the same for them. And even though our immediate cohort would in principle be facing the same way as us, they’re just as likely as we are to be confused by all of this – so they’re likely to ‘help’ us in the wrong ways, too. Tricky…
Learning each new skill takes us into the labyrinth all over again: the tangled, twisted, tortuous path that at times can seem torturous too. Yet in the end, there’s just one simple rule to help us achieve mastery in any new skill: all we have to do is work with whatever comes up at each moment, and keep going, keep going, one step at a time.
Jul/091
Where have all the good skills gone?
[to the tune of "Where have all the flowers gone?"]
Where have all the good skills gone?
Long time passing
Where have all the good skills gone?
Long time ago
Where have all the good skills gone?
Gone to robots every one
When will they ever learn?
When will they ever learn…
This one’s really a corollary or implication of the previous post – 10, 100, 1000, 10000 – on how long it takes to learn real skills.
We hear frequent complaints about skills shortages, in almost every industry. Talented, experienced people are hard to find, it seems. Yet few people seem to be considering the possibility that we’re actually creating that skills shortage by the way we design and ‘engineer’ our businesses. From a sidewise view, it seems likely that the skills shortage isn’t something that’s ‘just happened’, but is a direct consequence of the current fad for ‘re-engineering’ everything, converting every possible business process into automated form. ‘Lean and mean’ and the like will seem great ideas, in the short term especially – but without care, and awareness of the subtle longer-term impacts, they can easily kill the company. Not such a great idea, then…
There are ways to deal with this – but to do so needs a better understanding of skill – and especially of how skills develop, and where they come from.
The first key point is that not every process can be automated. If you read the sales-pitches of some of the proponents of business process re-engineering, for example, it might seem that every aspect of the business can be converted to automated web-services and the like. But the reality is that simply isn’t true: the percentage will vary from one industry to another, but the bald fact is that on average, less than a third of business activities are suitable for automation. The remainder are ‘barely repeatable processes‘ that are not only unsuitable for automation, but require genuine skill to complete.
Which brings us to the second key point: any process which requires genuine skill can never be fully automated. The inverse of that statement is possibly more accurate: an automated process cannot implement the range of skilled decision-making. Automation can do a subset – sometimes a very large subset – but it cannot do it all: which means that if we rely on automation alone, the business process will fail whenever the automated decision-making is not up to the task.
To understand why this is so, look at the Cynefin model, also referenced in that previous post on skills. Cynefin covers the whole scope of decision-making. Given an initially unknown context – which is what we have whenever we start a business process – we have four classic ways to resolve the ‘unknown’: rule-based, analytic, heuristics, principles. In effect, as in that “10, 100, 1000, 10000″ post, they’re a hierarchy of skill-levels, from minimal (rule-based) to extreme (principle-based). Most automation – especially in physical machines – is rule-based: the exact same decision-path is followed, every time, regardless of what else may be in play. IT-based systems can also handle varying levels of analytics, requiring more complicated or calculated decision-paths, and often at very high speed. But everything there is still dependent on the initial assumptions: and if those assumptions no longer hold – as they often don’t in true complexity, let alone in the subtle chaos of the real world – then automation on its own will again fail. Hence the need, in almost every conceivable business process, for ‘human in the loop’ escalation or intervention, to make the decisions that cannot or should not be made by machines alone.
But as machines and IT-systems take on more and more of the routine rule-based and analytic decisions – the ‘easily repeatable processes’, the ‘automatable’ aspects of business – a key side-effect is, almost by definition, that the skill-levels needed to resolve the ‘non-automatable’ decisions will increase. But because it seems so easy to automate some parts of processes, it’s easy to ignore the non-automatable decisions: at best, they get shoved to one side, tagged with the infamous label “Magic Happens Here”. And because the automatable parts of the process can be done ever faster with increasing compute-power and the like, the pile in the ‘too-hard basket’ just keeps growing and growing, until it chokes the process to death, or causes some kind of fatal collapse. Worse, the more simulated-skill we build into an automated system, the higher the skill-level needed to resolve each item which can’t be handled by the automated parts of the overall system. In short, the more we automate, the harder it becomes to resolve any real-world process.
To give a real example, consider sorting the mail. It all used to be done by hand, at the main sorting office and at the local branch post-offices. Sorting staff developed real skills at deciphering near-illegible scrawls; local delivery-staff used their local knowledge to resolve most of the mis-addressed mail. But manual processes like that are slow; so to handle large volumes, two key components were introduced: machines, to read the more easily-interpretable addresses; and post-codes, both to make it easier for the machines, and to pass more of the routing-decisions onto the person or system writing the initial mail-address.
The machines ’succeed’ because they only have to make a subset of the decisions within the overall sorting process. Anything they can’t handle on their own is handballed to a human operator to resolve. But we now require two different skillsets in human mail-sorters: machine-operators, who can handle another subset of decisions at high speed, to keep pace with the machines; and the expert interpreters, who have somewhat more time to do the best they can with the really indecipherable scrawls. In Cynefin terms, the machines do rule-based decisions (simple interpretation of printed post-codes) and analytics (algorithmic interpretation of handwriting); the operators tackle much of the complex domain (heuristic interpretation, plus decision to escalate to the experts); and the experts, who deal with the complex and chaotic domains.
But there’s a catch: how do those ‘humans in the loop’ learn the skills needed to do the job? By definition, they’re doing difficult work – too difficult for the machines to do their own. To put it the other way round, the machines do all of the easy work, and (usually) do it well: but that means that all the hard work is left to the humans. (That it often isn’t even acknowledged as skill is an interesting point in itself – a common cause of failures in classic Taylorist-style assembly-line process designs, for example.) But the way that humans learn skills is in a hierarchy of levels: first the rules, then the more complicated analytic versions of the rules, then the heuristic ‘exception that proves the rule’, and then finally a full almost intuitive grasp of the principles from which those apparent rules arise. If we try to skip any of those stages, everything falls apart: it’s possible to use principles straight off, for example, but without that firm foundation of knowing how and when why the rules exist, in order to ‘break the rules’, we can’t trust it in the real-world. Maybe in an emergency, perhaps, but not on a production-line – and it’s the latter that we’re concerned with here.
So people learn the skill by learning the rules, then the more complicated rules, and so on. The ‘10, 100, 1000, 10000 hours’ rule tells us roughly how long it’ll take: a trainee will take a day to get started; at least a couple of weeks to make some degree of sense of what’s going on; and six months or so to even begin to be able to make contextual heuristic decisions that are better than the built-in ‘best-practices’ of the machines. If we don’t allow them that time, and don’t give them access to the decisions that are embedded within the machines, there’s no way that they can learn. On top of that, if we don’t make it safe to learn – if there isn’t a ’safe-fail’ practice-space in which people can safely learn from their mistakes – there’ll be a serious disincentive to learn the needed skills. And business-specific skills can only be learnt on the job – they can’t be hired in from elsewhere. All of which can add up to serious business problems, especially in the longer term.
So what to do about it? The simplest way, perhaps, is to focus on questions such as these:
What decisions need to be made within each aspect of the business? What skills are needed to underpin each of those decisions? What level of skill – rule-based, analytic, heuristic, principle-based – is needed in each case?
What guides each of those decisions? Are the rules imposed from outside – such as via regulations, industry standards or social expectations – or from the business’ own principles, policies, procedures and work-instructions?
By what means are decisions escalated? Rules are always an abstraction of the real world: there’ll always be situations where they won’t work. The same applies to analytics, and to heuristics: at the end – as typified in so many business stories – everything can depend on principles. But how is each decision escalated from one level to the next? What skills are needed to understand how and when and why to escalate in each case? What mechanisms are used to signal such escalation, and pass the decisions up and down the skills-tree?
If decisions are embedded within automated systems, how may people learn the means by which those decisions are made? Machines and IT-systems can handle rule-based and algorithmic decisions: but people who need to take over those decisions in business-continuity and disaster-recovery need to know what those decisions are, and how to make those decisions themselves. These first- and second-order skills are also the foundation for higher-order escalated decisions: we need to know what the rules are, and why they are, before we can be trusted to break them. We also need to people to be able to take over when the machines fail, or simply when they’re overloaded – as occurs every Christmas in the mail-sorting context, for example. This is one key reason why disaster-recovery planning is a good place for trainees to start to learn the business – and business-continuity a good place to put that knowledge into practice.
What incentives exist for people to learn the skills the business needs? For that matter, how can we make it safe for people to learn? Most people learn by learning from their mistakes, so if it’s not safe to make mistakes, no-one will dare to risk learning anything. If people are to learn the higher-order skills, they need safe ‘practice-space’ where their inevitable mistakes will have minimal impact on the business and its clients; and they need time to learn, too. All these can work if the right incentives are in place – or, perhaps more important, if there are also no serious disencentives to learning.
Where have all the good skills gone? Lost in automation every one, if we’re not careful. But sidewise questions such as those above can help to retrieve the skills that we need – and keep improving quality and value throughout every aspect of the business.