Learning professionals need to understand the driving motivations and needs of their learners in order to structure learning effectively in the post-course world. In doing this they need to be less model-driven and more evidence-based.
This was just one of a number of fascinating insights that arose from our latest Think Tank dinner.
We assembled an invited group of L&D leaders to discuss these issues in a three-part discussion held under Chatham House rules. Contributing to the debate were delegates from the worlds of Finance, Mining, Telecomms, IT and commodity trading.
You can read highlights of the discussion here.
But for those who want a deep dive into the second part of this fascinating discussion, read on, as we address the following question:
Part 2: What alternative ways of structuring learning are emerging as we move towards less reliance on ‘the course’?
- Changes in the workforce and the work environment to a less directive culture mean that L&D is now focusing more on learners’ motivation, and techniques from the world of marketing: how to motivate learners becomes a key question
- Adaptive change might still require the immersion and guidance offered by a course, but trainers who follow rigid instructional models are seen to be less effective in this regard than those who have the skills to engage in challenging and difficult dialogue on a human level
- No-one wants to replace the old instructional baggage with a new set of models: there is interest instead in insights from areas such as psychology and anthropology/ethnography (although one model was mentioned, the ‘Human Givens’ model, that draws on insights from these fields)
- There is an impatience with 70:20:10 rising in some cases to active dislike. It is too often used as an excuse, when learning hasn’t worked, or as a justification for ‘doing things on the cheap’
- The greater ability to personalise learning that technology is now bringing gives opportunities for L&D to be able to target needs more accurately and supports them in acting more like marketers
- Greater availability of learner analytics can aid L&D in listening to the learners, but there are also dangers in being too obsessed with tracking every single piece of learning activity – the objective should be not just to produce more learner data, but better learner insights
Understanding the motivations of learners
For at least one of our delegates the key to structuring learning in this new, post-course world lies in finding the right kind of motivation for the learner.
Motivation can have both negative and positive aspects.
In the case of regulatory compliance, for instance, urgency can be given to a learning programme by emphasising the bad things that can happen to the learner on a personal level by failure to comply – e.g. ‘do you want to be the one who goes to gaol for breaching such and such a piece of legislation?’ Granted, this approach can be overdone: our delegate mentioned a colleague of his in Information Security who ‘likes to bring out the black raven of threat at every possible opportunity’. But humanising the effects of non-compliance like this can make for far more compelling learning. You give the learner a good reason to pay attention.
On the positive side of the balance sheet, he used the example of leadership programmes, which work best when they tap into the desire for personal advancement – a very primal human motivator. Tapping into this desire effectively, however, might involve negotiating a very common disconnect between what the manger wants, which is for her staff to do their jobs better, and what the staff themselves want, which is more likely to be a better job. It’s the difference between saying ‘this is how you become a better finance manager, and ‘this is how you become a finance director’. The second of those two propositions is liable to be much more motivating for staff – although it could give a few worries for managers (one reason why leadership programmes can often become political!).
Motivation has always been an important part of training, but with changes to a less directional culture, with fewer mandated training interventions – and without the backbone of compulsion offered by physical attendance at a course, often – we look at motivation in a different way. It has always been of concern how we might use learning to motivate people towards a particular goal, but now before we even get them into the training room (physical or virtual) we often now have to consider how we might motivate people to actually turn up in the first place.
Looking at motivation in this way brings us back to the theme of thinking like a marketer. Marketers are good at understanding (and exploiting) human motivations. However there is a point here where learning professionals need to to differentiate themselves from marketers, who can tend to look at motivation in quite cynical ways: it has been said (by a marketer) that there are only three basic motivators: fear, greed and personal advancement. Learning professionals, on the whole, tend to a more optimistic view of human nature.
Adaptive versus technical change
In looking at how learning might be structured, it is also important to observe the distinction between technical and adaptive challenges (as defined by Heifetz). L&D faces plenty of both, and might need to structure learning differently in each case.
Technical challenges – e.g. change to a new software system or procedure – are simpler, and are typically the type of challenge that elearning will be brought in to address. Adaptive challenges – e.g. changing leadership culture within an organisation – are more complex, requiring changes in attitudes and behavior within individuals. Here, felt one of our delegates, the wraparound and hand-holding offered by a course might be more necessary – and clearly face-to-face human contact is an important part of the process.
We might have more self-directed learners in our organisations, but tough adaptive challenges will tend to show up the limits of self-direction in learning. An individual might know they are required to change, but might not be aware in exactly what way they need to change, or how to find the help and support they need to make that change.
Such learners need to be given structure, support and a certain amount of one-to-one guidance. The challenge for L&D, in a world where there is less face-to-face training time available, was summed up by a delegate as: ‘how do we replicate that in a digital environment?’.
Although personalisation of learning is a powerful trend in learning technologies, and many interesting developments are being made in adaptive learning, we do not as yet have AI systems smart enough to take on the human role in a comprehensive way. Adaptive training challenges, at the moment, and probably for the foreseeable future, will continue to require a skillful blending of human and digital resources.
Old models, new models
When reflecting on the type of trainers who perform best in such adaptive challenges, one of our delegates said that it was the less ‘model-driven’ people who get the best results; those ‘who are adaptive themselves when delivering adaptive learning – so they’re authentic, they’re honest, they’re real. They’re not just saying, “This is a model,” or, “These are five models, I’m just going to push it on you until you accept it,” and they engage in a dialogue and they encourage criticism and debate. They’re the ones that you then see real, tangible benefit out in the workplace’.
Others agreed, and speaking more generally, there seemed little appetite to replace ‘the old instructional baggage’ with a new set of models. Delegates seemed uninterested in talking about 70:20:10, for instance. Although it is generally agreed to provide a useful shorthand fro talking about a more holistic approach to learning that looks beyond formal courses, it is felt to be too often abused and even, in some instances, used as a ready excuse for failure, or a justification for ‘doing things on the cheap’.
More inspiration seemed to be drawn from a range of academic fields from psychology to anthropology (neuroscience, of course, also getting a mention or two). One delegate cited the Human Givens approach as being helpful. It’s about ‘what drives our behaviour at a very fundamental level … the need for love, the need for attention, the need for achievement, recognition, all those things. I think that’s the way we change the structure’.
This of course brings us back to the question of human motivation, and understanding what makes learners tick.
‘When we talk about campaign learning and all that stuff, what we’re trying to do is tap into those human fundamental needs that say, “For me to feel whole, for me to be nourished, for me to feel part of something I need to understand that this is part of a bigger purpose. I need to feel part of a collective and that’s why face-to-face training works because I’m with people.” It’s how we say, “Right, we understand those needs.”
‘So for me, when you ask the question, what are the alternative ways of structuring learning? That, I think, is when we start to play in that space.’
Big data … or big insights?
Being ‘evidence-based’ is frequently advanced nowadays as a preferable approach to being ‘model-driven’. And there is more and more evidence to go on.
Where once the LMS tracked only fairly sparse data about course completions and scores, today’s digital environment is increasingly data-rich. Advanced learner analytics can give yield huge amounts of useful information about how learners interact with digital content and resources. This, together with AI, was seen as a big area of future development for L&D. However, it was felt to be important that gathering data should not become an end in itself. The aim should be not just to gather more data, but to get better, more accurate and more actionable learner insights.
We began to explore these questions further as we moved on to the next part of our discussion, which responded to the question: How will technology shape the future of learning in this post-course world?
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