Month: September 2014

Total 7 Posts

The Money Animal Marketplace Was The Most Fun I Had Doing Math This Summer

In my modeling workshops this summer, we first modeled the money duck, asking ourselves, what would be a fair price for some money buried inside a soap shaped like a duck? We learned how to use the probability distribution model and define its expected value. We developed the question of expected value before answering it.

Then the blogosphere’s intrepid Clayton Edwards extracted an answer from the manufacturers of the duck, which gave us all some resolution. For every lot of 300 ducks, the Virginia Candle Company includes one $50, one $20, one $10, one $5, and the rest are all $1. That’s an expected value of $1.27, netting them a neat $9.72 profit per duck on average.

That’s a pretty favorable distribution:


They’re only able to get away with that distribution because competition in the animal-shaped cash-containing soap marketplace is pretty thin.

So after developing the question and answering the question, we then extended the question. I had every group decide on a) an animal, b) a distribution of cash, c) a price, and put all that on the front wall of the classroom — our marketplace. They submitted all of that information into a Google form also, along with their rationale for their distribution.


Then I told everybody they could buy any three animals they wanted. Or they could buy the same animal three times. (They couldn’t buy their own animals, though.) They wrote their names on each sheet to signal their purchase. Then they added that information to another Google form.

Given enough time, customers could presumably calculate the expected values of every product in the marketplace and make really informed decisions. But I only allowed a few minutes for the purchasing phase. This forced everyone to judge the distribution against price on the level of intuition only.

During the production and marketing phase, people were practicing with a purpose. Groups tweaked their probability distributions and recalculated expected value over and over again. The creativity of some groups blew my hair back. This one sticks out:


Look at the price! Look at the distribution! You’ll walk away a winner over half the time, a fact that their marketing department makes sure you don’t miss. And yet their expected profit is positive. Over time, they’ll bleed you dry. Sneaky Panda!

I took both spreadsheets and carved them up. Here is a graph of the number of customers a store had against how much they marked up their animal.


Look at that downward trend! Even though customers didn’t have enough time to calculate markup exactly, their intuition guided them fairly well. Question here: which point would you most like to be? (Realization here: a store’s profit is the area of the rectangle formed around the diagonal that runs from the origin to the store’s point. Sick.)

So in the mathematical world, because all the businesses had given themselves positive expected profit, the customers could all expect negative profit. The best purchase was no purchase. Javier won by losing the least. He was down only $1.17 all told.

But in the real world, chance plays its hand also. I asked Twitter to help me rig up a simulator (thanks, Ben Hicks) and we found the actual profit. Deborah walked away with $8.52 because she hit an outside chance just right.


Profit Penguin was the winning store for both expected and actual profit.

Their rationale:

Keep the concept simple and make winning $10s and $20s fairly regular to entice buyers. All bills — coins are for babies!

So there.

We’ve talked already about developing the question and answering the question. Daniel Willingham writes that we spend too little time on the former and too much time rushing to the latter. I illustrated those two phases previously. We could reasonably call this post: extending the question.

To extend a question, I find it generally helpful to a) flip a question around, swapping the knowns and unknowns, and b) ask students to create a question. I just hadn’t expected the combination of the two approaches to bear so much fruit.

I’ve probably left a lot of territory unexplored here. If you teach stats, you should double-team this one with the economics teacher and let me know how it goes.

This is a series about “developing the question” in math class.

Answer Getting & Resource Finding

I posted the following three tweets yesterday, which I need to elaborate:

“Answer-getting” sounds pejorative but it doesn’t have to be. Math is full of interesting answers to get. But what Phil Daro and others have criticized is our fixation on getting answers at the expense of understanding math. Ideally those answers (right or wrong) are means to the ends of understanding math, not the ends themselves.

In the same way, “resource-finding” isn’t necessarily pejorative. Classes need resources and we shouldn’t waste time recreating good ones. But a quick scan of a teacher’s Twitter timeline reveals lots of talk about resources that worked well for students and much less discussion overall about what it means for a resource to “work well.”

My preference here may just mean grad school has finally sunk its teeth into me but I’d rather fail trying to answer the question, “What makes a good resource good?” than succeed cribbing someone else’s good resource without understanding why it’s good.


  • I felt the same way about sessions at Twitter Math Camp.
  • Kurt Lewin: “There is nothing so practical as a good theory.”
  • Without agreeing or disagreeing with these specific bullet points, everyone should have a bulleted list like this.

Featured Comment

Mr K:

This resonates strongly.

I shared a lesson with fellow teachers, and realized I had no good way to communicate what actually made the lesson powerful, and how charging in with the usual assumptions of being the explainer in chief could totally ruin it.

Really worthwhile comments from Grace Chen, Bowen Kerins, and Fawn Nguyen also.

Adrian Pumphrey:

Really, we need to literally go back to questions such as ‘Why am I teaching this?’ ‘Where does this fit into the students learning journey?’ and ‘How am I going to structure the learning so that the student wants to learn this?’ before we even think about where resources fit into our lesson. This takes a lot of time to think about and process. Time and space many teachers just don’t have.

Chris Hill:

Early on I would edit resources and end up reducing cognitive demand in the interest of making things clearer for students. Now I edit resources to remove material and increase cognitive demand. Or even more often, I’m taking bits and pieces because I have a learning goal, learning process goal and study skills goal that I have to meet with one lesson.

Kelly Stidham:

Great lessons in the context of learning around mindset and methods are the instruments we use to “do” our work. But the reflection and coaching conversations where we “learn” about our work are critical as well. Without them, we use scalpels like hammers.

But this work is much harder, much more personal, much more in the moment of the classroom. Can we harness the power of tech to share this work as well as we have to share the tools?

2014 Sep 8. Elissa Miller takes a swing at “what makes a good lesson good?” Whether or not I agree with her list is besides my point. My point is that her list is better than dozens of good resources. With a good list, she’ll find them eventually and she’ll have better odds of dodging lousy ones.