The Challenge in Adult Learning
Some interesting observations.
Background
Almost a year ago, I wrote a well-received article on Adult Learning (Article 30: The Problem with Adult Learning) where I provided definitional clarity to the domain, criticised the status quo thinking that held us back, and suggested 3 tips for improvements — (i) institute continuous certification for certain types of skills, (ii) encourage teaching as a form of learning, and (iii) moving away from outcome-based learning to a more holistic diversity-based learning.
I’ve been involved in designing and delivering adult training for a decade (I am not a professional educator), and during that time, I’ve noticed some structural challenges within the domain, inspiring me to write a follow-up article. And so I dedicate my 75th article to unpacking and illuminating the messiness of adult learning, drawing upon my experiences with teaching data analytics.
(I write a weekly series of articles where I call out bad thinking and bad practices in data analytics / data science which you can find here.)
Odd Observations
Let me start by making the following observations:
- Universities seem to corner a lion’s share of the adult learning market; that is definitely the case in Singapore where I live. This is odd considering that universities generally do not excel at non-pedagogical curriculum; and adult learning is not pedagogy-based.
- The really useful skills that adults need are not fun to learn, and are difficult to teach. There is insufficient joy in adult learning and so pull factors are unlikely to be successful. Government funding and corporate subsidies are considered pull factors.
- Adult competencies are tool-supported; because adults use tools to get work done. For example, I can’t be good at data visualisation if I don’t also master a data visualisation tool. Teaching a tool-supported competency is very different from teaching the tool, and it is challenging to find those who can.
Why Universities
Let’s unpack the first observation.
Adult learning has no pedagogy. Adult learning is theorised on the basis of andragogy. Andragogy assumes that adults are more self-directed and self-motivated than children, and therefore, the learning must be relevant and contextualised to their lives. Translated, it means that the learning must be highly participatory (i.e, interactive, 2-ways), and incorporating hands-on learning experiences.
Universities may study the theories of andragogy, but it rarely translates into an ability to enact it. Universities are not places of practice. And yet, an adult learning course offered by a reputable university is disproportionately perceived as a better option than if it was offered directly by a reputable consulting company. We can’t help but perceive universities as a place of learning. And coupled with the ability to “rubber-stamp” certificates, there is the obvious signally advantage.
A logical evolution is for universities to partner with industry practitioners, and avoid using faculty or full-time trainers (most lack updated practical experience). In the domain of data analytics, I’ve seen some universities assign their Statistics or Computer Science faculty to the task, resulting in a theory-based curriculum that is paired with stock case studies lacking complexity and natural “noise”. The adult learner is often savvy enough to recognise the low relevance of such courses. But how do we achieve scale and consistency with industry practitioners?
Hard To Learn
Let’s unpack the second observation.
Anything really useful is going to be hard to learn. We intuitively know that. The corollary for this is that we will tend to delay learning the hard stuffs in our careers until we are forced to do so. The self-motivated ones are the exception rather than the norm.
In my teaching of data analytics to adults, I have found that the mental blocks can be very significant. “I can’t do Maths.” “I’m not good at programming.” Research has shown that, in fact, adult learning needs more structure; because there is huge inertia, fear, and wrong knowledge that needs to be unlearnt. To teach data analytics, I get the participants to first appreciate that they’ve been unknowingly doing data analytics their whole life, albeit in an unoptimised manner. By creating this connection to existing experiences and behaviours, I help them to “enter” the difficult subject.
But I do want to make the content challenging. Just like physical exercise, if it’s not challenging, there is no growth and no durable learning. Too often, I have seen instructors water-down the content or the participants expecting it to be watered-down. Again, the really useful stuff is going to be difficult and there’s just no way around it.
Many adult learners approach adult learning asking for the content to be customised to their industries or functional domains. They lack of learning agility and can’t see how the content can be similarly applied to their own use cases. So those providing adult training should incorporate a section where application transference gets sufficient airtime and is discussed.
Bring Me My Tools!
Let’s unpack the third observation.
Besides useful stuff being difficult to learn, they need to be learnt in conjunction with a tool. Corporate human beings use tools at work instead of their bare hands. The choice and availability of tools compounds the problem for adult learning. And this where government funding could be better utilised — pay for the tools (time-bound if necessary). In the data analytics / data science domain, some of the tools are open-sourced, but many of the important enterprise kinds are not. Universities have a bad habit of using the same tools that they use for undergrad curriculum for adult learning, and this only makes the disconnect worse.
One could also approach adult learning by separating the tool training from the competency associated with the tool. Consider the case of data visualisation. Power BI and Tableau are the 2 large incumbents in the marketplace. We could get the tool vendors to train on the technical aspects of their tools, and follow-up that training with use case competencies (e.g. understanding visual vocabulary, the science of visual cues, metrics construction and dashboard designing).
The adult learner has to master both tools and use case competencies. This is the case for most of the skills that adults need to pick up. And this is exactly why we cannot under-estimate the challenges in adult learning. Those hoping to pivot their careers into data science or cybersecurity by taking up a 3-month programme are simply being unrealistic.
Conclusion
Wide-scale adult learning is critical for the ongoing success of the economy. More so than ever before. This is new territory for all. There are no templates or rule books to follow, and many countries and organisations are still figuring it out as best they can. Money is the easiest resource to throw at the problem, but it’s not sustainable.
While the challenges listed above are not exhaustive, they need to be addressed. I don’t have great answers for them. Only some possible suggestions. For example, I’m of the opinion that the Consulting industry can step in and step up here. Those in consulting will have both the practical and technical expertise to provide adult training. Perhaps governments should be re-directing their funding and subsidies towards the consulting industry to get them to take on this additional role.
