Example: Three Principles of Learning Every Teacher MUST Know - I
Explaining difficult concepts to people with different levels of prior knowledge
Your Understanding of a Concept is Determined by Your Prior Knowledge
So, in the first part of this post, I shared about the first principle of learning.
And, the teaching/learning implication is:
Find out the prior knowledge of the students. Is it relevant? Is it complete? Is it wrong? Compared to the concept you are teaching.
Then, build the understanding step by step from that point, starting from concrete to abstract.
How Do Artful Explainers Do It Then?
I am quite fascinated by the video series on youtube titled “5 levels of Difficulty” in which usually a scientist or an expert tries to explain complex concepts to five different levels of learners.
Level 1: 5 year old child
Level 2: A teenager
Level 3: A college student
Level 4: A grad student
Level 5: An expert
The reason I’m fascinated about these videos is because the scientists artfully check the prior knowledge of the learners through a few questions. They also gauge the learners about the expected levels of understanding the learners should have in regards to the topic. And start working up from simple to complex explanation.
Here’s one such video in which neuroscientist Bobby Kasthuri takes the challenge to explain the scientific concept of “Connectome” to 5 different people; a 5 year-old, a 13 year-old, a college student, a neuroscience grad student and a connectome entrepreneur.
I suppose Level 1 is the most challenging level because one would have to make the explanation down to the most basic concrete level. In many ways, Level 1 is an absolute novice with simple, shallow and blurry understanding of the world.
Here’s how Bobby Kasthuri does it. Pretty successfully.
Level 1: 5 year old child
“Do you know why we’re here today?”
“Because we’re talking about science.”
“Do you know what a brain is?”
”Yes.”
”What is it?”
”Something that’s helps you remember things.”
“Do you know that your body is made up of really tiny things called cells?”
”Yes I know that.”
”Well, there’s more cells in your brain.. like way more cells… than all the stars we can see.”
“So what the connectome is, we would like to know where every cell in your brain is, and how it talks with every other cell in your brain.”
Brilliant explanation, for two reasons.
First, Kasthuri connects each concrete concept the child already knows (science, brain as something that helps us remember) with another concrete concepts (cells, stars in the sky) and builds the through-line of his explanation (cells talk with each other). This is the new information for the child.
Second, he does not use any word the child doesn’t know already.
Now you might argue: is this explanation correct?
And I would say, the explanation is correct ‘enough’ for that child. Any details more complex or technical would overload the child’s working memory and jam the understanding process. At his age, all he needs to understand is the super simplified version of Connectome, that there are thousands of cells in our brain, and each cell talks with one another.
(On a philosophical level, every explanation is incomplete, inaccurate and thus a lie. But that is a topic of discussion for a time when we’re both tipsy.)
Now let’s examine, what sort of prior knowledge the Level 3 college student brings in. From the description, we know that she is an Interdisciplinary Science Major student. We can safely assume that she already knows a lot about the brain, cells, and neuroscience. Meaning, she has familiar and relevant knowledge related to the topic.
“So today we’re talking about Connectome. Have you ever heard of that?”
”A Connectome? No I don’t think so.”
”Awesome.”
Now Kasthuri digs for more prior knowledge.
And she says she knows something like “a mapping of the pathways between the neurons that can lead to evidence of patterns in your brain that are common between different people.”
Now that Kasthuri knows that the college students already knows about “mapping, neurons, and patterns”, he gives a slightly technical detail about how they perform the mapping (by using electron microscopes, thin slicing the brain, taking picture of each slice of the brain, and using computers to put all the slices back).
Then to dig more, Kasthuri poses her a question: if we could get he map of every connection and we knew how neurons fired, and put that in a computer, do you think the computer should be able to think just like the brain it mapped?
“The computer only communicates itself in binary so it only has two options, yes or no. But a human brain has an infinity of directions that can go.”
It seems, she is right about the computer and not so right about the human brain. Kasthuri capitalizes on this new information, that she has incomplete knowledge about human brains and neurons.
He gives a perfect analogy,
“Neurons are also digital, meaning a neuron either fires or it doesn’t fire. So that’s either one or zero.”
It seems, our brains too can be called to operate in a binary system.
Watch her face light up at that moment, because she can now see the comparison clearly.
“And it’s the combination of those ones and zeros that produce the 10000 answers that you say.”
Again, just brilliant. The way Kasthuri connects the prior knowledge with the new information and builds the understanding.
The More You Know, The More You CAN Know
Enough of my break downs. Here are some of my favorite ones from that series.
Know more about how to check for prior knowledge and how to use those information to build your explanation. Enjoy.
From the archives:
Teachers can learn a lot from Richard Feynman about the art of explanation. Here’s something I wrote a couple of weeks back.