You Have to Pay Attention
On navigating the tension between education & entertainment
One of my favorite quotes is : “Knowledge isn’t free. You have to pay attention”. The quote is often attributed to famed theoretical physicist Richard Feyman but there’s no concrete evidence he ever actually said it.
I find the quote powerful because it explains in big part why as the cost to produce content has gone down to zero our learning gains have barely moved. The explosion of content has made it harder than ever to “pay” attention.
The notion of paying attention is at the crux of the debate on “edutainment”. Ignoring this dynamic fuels a lot of unproductive edtech pursuits bundled under this theme.
The inherent operating assumption in the edutainment space tends to be: since tech companies have become so good at capturing our attention (with ever-more interactive and inexpensive content), then those exact same methods can be used to harness better learning. Simply put: anybody that can capture your attention can teach you.
The flaw in this line of thinking is two fold.
First, it assumes that all attention is created equal. It does not appreciate the difference between paying attention and having your attention stolen from you.
Second, it assumes that all learning is created equal. Deep learning is what counts - and it doesn’t often happen in bite-sized chunks. Deep transformative learning must be effortful by design.
What’s missing from the edutainment discussion is that the dynamics of learning are different from the dynamics of mindlessly scrolling through social media or unwinding while watching a great documentary.
As Katelyn Donnelly at AVC has argued earlier this year, learning of course doesn’t need to be hard or dull - quite the contrary. However, to create “hard fun” there are four important pieces of the puzzle that are often missing from the edutainment solutions available.
Each one these pieces deserves it’s own article but here is a high level overview:
Start with the assessment - It sounds blindingly obvious, but you can't measure learning if you don’t measure learning.
As I’ve argued countless times before, we need to move from a Netflix analogy for learning (or edutainment) to a Google Maps one. Until you can figure out the origin and destination of the learning path you are not a learning company - at least not a good one.
Critically, assessment doesn’t have to be overt or explicit - in fact stealth assessments inspired by games will be a big part of the future of learning.
Don’t trust what the learners like - Unlike choosing a pair of shoes, or “hearting” an instagram reel, learners - and especially novice ones - are not a good judge of what supports or helps their learning. Engagement and learning metrics can vary greatly. The trick lies in leaning into the science of (active) learning and leveraging assessment to filter content in addition to user engagement.
Balance desirable difficulty and the principle of least effort - a cornerstone of good UX design is the principle of least effort. Simply put, it “essentially states that people will do the least amount of work to get something done” and we’ve shaped our tools to meet that minimum threshold.
Unfortunately, a “don’t make me think” approach does not lend itself too well to learning. Good learning is predicated on the notion of “desirable difficulty”: the right amount of struggle is necessary. We must motivate learners to dedicate time to engaging with the material as opposed to breezing through it.
Be wary of the expert “blindspot” - The notion of learning from the all-time greats has always been appealing. Yet, there is no evidence that experts are good at teaching. It’s quite the opposite: the literature is full of evidence about the expert “blindspot” and shows that we learn best from peers or near-peers. Experts lose the ability to see the world from a novice’s perspective without a lot of effort and practice.
Learning can and should be fun and engaging. While technology can open new frontiers it’s about how we are fundamentally changing the learning process that will pay dividends. It’s about the gameboy, not the metaverse. As generative AI brings down the cost of content and the value of shallow understanding that notion will be more true than ever.
I hope you enjoyed the last edition of Nafez’s Notes. I’m constantly refining my personal thesis on innovation in learning and education. Please do reach out if you have any thoughts on the framework or frameworks of your own.
Finally, welcome to the over 120 new subscribers that have joined since the last issue.
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Several things:
1. "Start with assessment" is the approach taken by Wiggins and McTighe in their excellent "Understanding by Design."
2. "Desirable difficulty" aligns with Vygotsky's "Zone of Proximal Development." If learning is too easy, there's no real learning (easy means the learner already knows the content) and it gets boring. If learning is too hard, the learner gets overwhelmed, stops paying attention, and then gets behind such that catching up is essentially impossible.
3. On the topic of attention, I'm by no means an academic expert, but I've spent a fair bit of time and effort thinking about and learning about attention. I've outlined some techniques for helping to manage one's own attention here:
https://dieffenblog.wordpress.com/2021/02/03/may-i-have-my-attention-please/
Wise words!