The Problem With Adult Learning

Re-thinking adult learning approaches.

Photo by Artem Maltsev on Unsplash

Background

My part-time hobby is being an adjunct associate professor, lecturing in data analytics for adult learning. I call it a hobby because my full-time job is running my data analytics consulting practice. But lecturing forces me to sharpen my skills to explain difficult abstract concepts in simple terms — “Explain it to me like I’m a 5-year-old!” as they say. One of my most successful module is on visual analytics that co-created with a friend. It has been running for over 5 years (with edition updates obviously!). So I have a vested and acute interest in this adult learning domain. But I think it’s time the entire industry needs a re-think.

The global adult education industry was valued at USD 373 billion in 2023 and is expected to reach USD 800 billion by 2030, with an annual compounded growth rate of 8.6%. That’s a HUGE market. What’s driving it is the demand for workforce up-skilling and re-skilling brought on by the digital revolution, which includes the advent of traditional and generative AI. A 2023 Forbes report suggests that 50% of current skills will be made irrelevant within 2 years!

Now, we bandy about terms like “adult learning” vs “continuous education” vs “lifelong learning”. Do they mean the same thing, or is it just semantics? Is the workforce genuinely benefiting from all this supposed up-skilling and re-skilling? I’m not convinced it is. And so I dedicate my 30th weekly article to discussing the problems around Adult Learning.

(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.)

What Exactly is Adult Learning?

I always like to start by first unpacking definitions. It’s important to get alignment at the beginning. A quick research reveals the following definitions:

1. Adult Learning: focuses on structured educational experiences post high school or tertiary education. Can be delivered formally or informally, can be instructor-led or self-directed, in classroom or online.
2. Continuous Education: similar to Adult Learning, but with a more narrow focus on professional development; staying up-to-date in a particular field or acquiring adjacent knowledge and skills for job performance. Structured and delivered formally.
3. Lifelong Learning: encompasses learning from childhood through adulthood, focusing on personal acquisition of knowledge, skills and experiences contributing to personal fulfilment and adaptability.

Note that the key differences in definition lie in their end objectives. But it’s also about the expectations and mindset of the learner. For example, my data analytics adult courses would be considered as continuous education, and possibly adult learning if participants enrol out of interest. And what I’ve found is that if the participants could not directly connect the concepts and learnings to their immediate work (key objective of continuous education), they tend to view the course as a washout for them. They don’t see learning as scaffolded and incremental, but rather as an immediate utility. And that is unfortunate.

Frustrations

Despite the massive investments in the adult learning domain over the last decade, there’s a lot of empirical and observational evidence that re-skilling and up-skilling initiatives aren’t working. A key problem is how to demonstrate that you’ve acquired the skills in your daily job? Organisations struggle to connect outcome and impact with learning inputs and activities. A recent research by the Brandon Hall Group found that less than one-third of companies believe that their talent development objectives are strongly tied to learning objectives.

Another challenge is that the half-life of professional skills has declined very significantly, from an average of between 10 to 15 years, to just 5! What this means is that any new skill acquired becomes half as relevant within 5 years. This puts pressure on the velocity and sustainability of training and re-training.

Despite the shortening half-life, there is insufficient participation from employees to pursue re-skilling. Many employees cite time constraints, family priorities and insufficient funds as key reasons, but employers claim that employees are simply lazy and not prioritising. The truth is likely somewhere in the middle. Technical skills such as coding, data science and data visualisation have become the standard bearers of the new digital knowledge economy, but the perceived hurdle to acquiring these skills for employees that are insufficiently data or digital literate can feel insurmountable.

Changing Corporate & Learner Mindset

It’s not a question of insufficient funds to bring about change in the adult learning domain. There is a lot of money to go round and there is a lot of wastage, and this is counter to the sustainability goals that we are all trying to achieve. We need to be investing in the right things. We need to move from adult learning to continuous learning. That is the key thrust. I make the following 3 proposals to shift the needle:

1. Mandate continuous skill certification. Most people don’t enjoy learning; self-motivated learning is uncommon, and you can’t change that. Every job has to be attributed with a set of skills, and these need to be certified at periodic frequencies and at increasing difficulty correlated to years on the job. Use it / apply it or lose it. That’s the simple message. Your tertiary educational certificates are not perpetual ‘passports’ on abilities.
2. Make everyone teach. Teaching is the best form of learning. The evidence is there. The learning is durable. I’m not talking about making employees do one-off “brown bag sessions”. Instead, every employee from mid-level up or those having spent a threshold number of years in the job MUST put up a curriculum to instruct their juniors. They must design the curriculum (with coaching and guidance, of course) and this forces them to research and get intimate with the learning material. Making the target employee deliver the learning curriculum repeatedly over a period of time allows them to reflect, internalise and fine-tune. (I have learnt so much more about my craft in data analytics and data science in the last decade as an adjunct professor than I have as a corporate CAO.)
3. Stop the tyranny of Kirkpatrick, Jack Phillips and Kaufman. These are the typical training assessment frameworks. L&D practitioners have been trying to grasp the holy grail of linking outcomes with training inputs. These assessment frameworks are not empirically supported, but people cling onto them either because of reflex or intellectual laziness. For continuous learning to take place, you need to learn anything and everything; there is no waste in knowing stuff. You never know when your brain makes the connections. It’s like training data for AI. If we are only training for what’s in front of us (i.e. “just in time” learning), we won’t embrace diversity, we won’t embrace the adjacent and definitely not the obtuse. But that breadth of learning is exactly what’s needed to create learning agility and adaptability. Don’t treat learning or training as an outcome-oriented task.

Conclusion

There are no easy answers for this. There needs to be a tectonic shift in mindset and behaviour for adult learning and continuous learning to succeed — the only real objective of learning is learning. It’s forming that repeatable habit; we don’t have to like it. Much like eating healthy and exercising, we just need to do it.