What makes a good learning designer?
Some of the qualities we look for when hiring new team members.
As some of you might know, Paula Hughes recently joined the Custom Learning team here at Mind Tools.
Paula has worked in L&D for almost twenty years. She’s a board member of the Learning Network, and she won silver in the ‘Learning Designer of the Year’ category at the Learning Technologies Awards in 2022.
These credentials, combined with the fact that we’ve known Paula a long time, made the decision to hire her an easy one. But recruiting a learning designer isn’t always so straightforward.
As Ross G has written, the modern learning professional is multifaceted. Unlike other providers, who distinguish between ‘designers’ and ‘developers’, Mind Tools requires LXDs to manage projects end-to-end, from scoping, through build, to delivery.
It’s a role that demands a range of skills and characteristics. And while some of these can be developed internally over time, others are more easily acquired through recruitment.
Last week, I asked our team to define the essential qualities of a ‘good’ learning designer. Below is a brief summary of their responses, combined with some thoughts of my own. Broadly speaking, these are the traits we look out for when hiring new team members:
🤔 A willingness to embrace ambiguity
Many learning-design projects begin with a degree of ambiguity. Others begin with an overdose of certainty.
Learning designers need to be able to operate in the space between these two poles.
Whether the project starts with a poorly defined problem or an unshakeable conviction that ‘training’ is the solution, the learning designer’s immediate response should be curiosity. Or, as Paula put it, ‘they should be more interested in asking the questions than having the answers’.
These questions might include:
What problem are we trying to solve?
How do we know that this is a problem?
What is causing the problem?
What would be the consequence of doing nothing?
What would success look like?
In my experience, most learning designers are nerds. And at their core, nerds are people who can find an interest in any topic, once they’ve asked the right questions.
🤯 A knack for synthesizing and structuring complex information
Of course, once you start asking questions, you need to do something with the answers.
One of Ross G’s favorite lines is that, as learning designers, we are ‘usefully ignorant’. What that means is that, by asking the right questions and openly conceding that we don’t know the answers, we can help subject-matter experts communicate their expertise in a way that makes it digestible for a novice. In other words, we ask the ‘stupid’ questions so learners don’t have to.
Once we have the answers to those questions, we then need to structure or ‘scaffold’ this information to design an effective learning experience.
🙃 An ability to communicate clearly and creatively
From consulting with SMEs to managing stakeholders, from scripting videos to writing e-learning modules, communication is arguably the most important skill in learning design. And, in my view, it’s also one of the hardest to develop.
Unlike, say, learning to use an authoring tool, where basic competence can be achieved relatively quickly, becoming a ‘good communicator’ takes time. As my use of quotation marks suggests, ‘good’ communication is also inherently subjective and subject to taste (see point 5 below). Though, as US Supreme Court Justice Potter Stewart notably argued in Jacobellis v. Ohio, I like to think that ‘I know it when I see it’.
Because we’ve found that the journey from ‘good communicator’ to ‘good learning designer’ is easier when travelling in that direction, we place a lot of weight on communication skills during recruitment. Which brings me nicely to my next point.
🤓 A sound grasp of learning science
Obviously, if you’re applying for a job as a learning designer, you should probably have at least some knowledge of learning science, and an appreciation for how the brain works. But I wouldn’t completely disqualify someone who had no prior experience or qualifications in these areas.
Before you [@] me on LinkedIn, hear me out.
Like many of my colleagues who have ended up working in this field, I didn’t set out to pursue a career in learning design. I did some teaching, did some copywriting, and somehow landed where I am today. #livingthedream
Of course, the transition from teaching to learning design is a fairly natural one. But much of what I know about learning science has been picked up during my time at Mind Tools, either through my colleagues or through hosting the Mind Tools L&D Podcast.
In a hypothetical recruitment scenario, faced with a choice between a candidate who had no knowledge of learning science and one who had no skill as a communicator, I’d sooner choose to up-skill the former than the latter.
🧐 A sense of ‘taste’
Finally, although none of my colleagues referenced this quality in their set of criteria, I’ve previously written about the importance of taste in learning design. In short, I believe a learning designer needs to have an instinctive understanding of what ‘good’ looks like. And they need to know how to get there.
One way we test this during the recruitment process is by assigning candidates a task. Often, this task will involve creating part of an e-learning module or workshop, based on a brief we have provided. The candidate’s response to the task not only allows us to evaluate their communication and design skills — it allows us to gain an insight into their understanding of ‘good’, and how closely this understanding aligns with our own.
Want to share your own criteria? Interested in working with our award-winning Custom team? Then get in touch by emailing custom@mindtools.com or reply to this newsletter from your inbox.
🎧 On the podcast
Depending how you count it, 70:20:10 is almost 40 years old. The model provides a high-level outline of how we learn at work: 10% through formal learning, 20% through working with others, 70% through doing the work.
The numbers get criticized, but this insight is widely accepted: Most of what we learn does not come from formal training. But how, then, should L&D practitioners apply the model to the work that they do? Is it still a useful concept after all this time?
In the first of this two-part series, Ross G and Owen explore these questions with three practitioners: Ceri Sharples, Learning and Development manager at Somerset Bridge Group; Cath Addis, L&D manager at Ascential; and return guest Carl Akintola-Davis, Head of Leadership Development at Phoenix Group.
Check out the episode below. 👇
You can subscribe to the podcast on iTunes, Spotify or the podcast page of our website. Want to share your thoughts? Get in touch @RossDickieMT, @RossGarnerMT or #MindToolsPodcast
📖 Deep dive
While many believe that generative AI will revolutionize education, democratize access to tutoring, and close the achievement gap, Dr Jared Cooney Horvath argues there are three reasons to be skeptical of such claims.
Empathy
Although AI has the potential to enable personalization, it does not (yet) have the capacity to empathize with students. For Horvath, this is a problem:
‘… individualized instruction is not the most important driver of learning. After analyzing data from thousands of studies, educational researcher John Hattie recently reported that a strongly empathetic learner-teacher relationship imparts 2.5x greater impact on learning than personalization.’
Knowledge
Another oft-repeated claim about generative AI is that it will allow learners to focus on critical thinking and problem-solving, effectively outsourcing facts and information to an LLM. Horvath believes that, while this idea is appealing, it is based on a misapprehension of how high-level thinking actually works:
‘Unfortunately, much of what we term “creative” and “critical” thinking occurs via subconscious processes that rely on internalized knowledge. When we consciously think about a problem, humans can only actively consider a very finite amount of information due to the cognitive limits of working memory.
However, once we stop consciously thinking about a problem, we enter into an incubation period whereby our brains subconsciously sort through our memory stores by seeking out relevant ideas. It’s during this sorting process (known as reconsolidation) that novel connections are made and better thinking emerges.’
Multitasking
The third issue that Horvath identifies is the fact that accessing gen-AI tools requires learners to engage with technologies that often lead them to distraction:
‘A pre-Covid survey revealed that students across the United States spent nearly 200 hours annually using digital devices for learning purposes. However, they spent 10 times as long — more than 2,000 hours — using these same devices to rapidly jump between divergent media content. […]
It’s not that computers can’t be used for learning; it’s that they so often aren’t used for learning that whenever we attempt to shoehorn this function in, we place a very large (and unnecessary) obstacle between the learner and the desired outcome — one many struggle to overcome.’
Horvath, J. C. (2024). ‘The Limits of GenAI Educators’ Harvard Business Review.
👹 Missing links
⚡ Sam Altman’s guide to productivity
I stumbled across this old blog post by OpenAI CEO Sam Altman while browsing Substack. In the article, Altman presents his theory of productivity. Unsurprisingly, he focuses heavily on optimization and the concept of ‘compound growth’ — the long-term benefits of small, incremental improvements. But he also emphasizes the importance of deciding what to work on: ‘It doesn’t matter how fast you move if it’s in a worthless direction. Picking the right thing to work on is the most important element of productivity and usually almost ignored.’
Some people have questioned whether breakdancing belongs in the Olympics. But what about poetry? In this podcast, the 99% Invisible team explores the games’ relationship with the arts. First up, a story about the iconic design of the 1968 Olympics in Mexico City. And secondly, a journey back to the early 20th century, when the IOC unveiled five artistic categories, including music and architecture.
🐶 Reinforcement learning explained… by puppies
Alongside supervised learning and unsupervised learning, reinforcement learning is one of the most common methods for training AI systems. It sounds complicated, but it’s easily grasped by watching a group of puppies try, fail, then succeed to navigate a maze and reach a bowl of food.
👋 And finally…
One of the most compelling stories of the Olympics has been the rivalry between Jakob Ingebrigsten and Josh Kerr. Both men openly backed themselves to win gold in the 1500m, but were ultimately pipped to the post by Cole Hocker, who quietly ran his own race.
👍 Thanks!
Thanks for reading The L&D Dispatch from Mind Tools! If you’d like to speak to us, work with us, or make a suggestion, you can email custom@mindtools.com.
Or just hit reply to this email!
Hey here’s a thing! If you’ve reached all the way to the end of this newsletter, then you must really love it!
Why not share that love by hitting the button below, or just forward it to a friend?