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Can Sports Save Math?

A Sports Illustrated editor emailed me last week:

I’d like to write a column re: how sports could be an effective tool to teach probability/fractions/ even behavioral economics to kids. Wonder if you have thoughts here….

My response, which will hopefully serve to illustrate my last post:

I tend to side with Daniel Willingham, a cognitive psychologist who wrote in his book Why Students Don’t Like School, “Trying to make the material relevant to students’ interests doesn’t work.” That’s because, with math, there are contexts like sports or shopping but then there’s the work students do in those contexts. The boredom of the work often overwhelms the interest of the context.

To give you an example, I could have my students take the NBA’s efficiency formula and calculate it for their five favorite players. But calculating – putting numbers into a formula and then working out the arithmetic – is boring work. Important but boring. The interesting work is in coming up with the formula, in asking ourselves, “If you had to take all the available stats out there, what would your formula use? Points? Steals? Turnovers? Playing time? Shoe size? How will you assemble those in a formula?” Realizing you need to subtract turnovers from points instead of adding them is the interesting work. Actually doing the subtraction isn’t all that interesting.

So using sports as a context for math could surely increase student interest in math but only if the work they’re doing in that context is interesting also.

Real Work v. Real World

“Make the problem about mobile phones. Kids love mobile phones.”

I’ve heard dozens of variations on that recommendation in my task design workshops. I heard it at Twitter Math Camp this summer. That statement measures tasks along one axis only: the realness of the world of the problem.

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But teachers report time and again that these tasks don’t measurably move the needle on student engagement in challenging mathematics. They’re real world, so students are disarmed of their usual question, “When will I ever use this?” But the questions are still boring.

That’s because there is a second axis we focus on less. That axis looks at work. It looks at what students do.

That work can be real or fake also. The fake work is narrowly focused on precise, abstract, formal calculation. It’s necessary but it interests students less. It interests the world less also. Real work – interesting work, the sort of work students might like to do later in life – involves problem formulation and question development.

That plane looks like this:

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We overrate student interest in doing fake work in the real world. We underrate student interest in doing real work in the fake world. There is so much gold in that top-left quadrant. There is much less gold than we think in the bottom-right.

BTW. I really dislike the term “real,” which is subjective beyond all belief. (eg. What’s “real” to a thirty-year-old white male math teacher and what’s real to his students often don’t correlate at all.) Feel free to swap in “concrete” and “abstract” in place of “real” and “fake.”

Related. Culture Beats Curriculum.

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

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Bob Lochel:

I would add that tasks in the bottom-right quadrant, those designed with a “SIMS world” premise, provide less transfer to the abstract than teachers hope during the lesson design process. This becomes counter-productive when a seemingly “progressive” lesson doesn’t produce the intended result on tests, then we go back not only to square 1, but square -5.

Fred Thomas:

I love this distinction between real world and real work, but I wonder about methods for incorporating feedback into real work problems. In my experience, students continue to look at most problems as “fake” so long as they depend on the teacher (or an answer key or even other students) to let them know which answers are better than others. We like to use tasks such as “Write algebraic functions for the percent intensity of red and green light, r=f(t) and g=f(t), to make the on-screen color box change smoothly from black to bright yellow in 10 seconds.” Adding the direct, immediate feedback of watching the colors change makes the task much more real and motivating.

Before I get to the good, here’s the tragic, a comment from a father about a math feedback platform that I don’t want to single out by name. This problem is typical of the genre:

My daughter just tried the sine rule on a question and was asked to give the answer to one decimal place. She wrote down the correct answer and it was marked wrong. But it is correct!!! No feedback given just – it’s wrong. She is now distraught by this that all her friends and teacher will think she is stupid. I don’t understand! It’s not clear at all how to write down the answer – does it have to be over at least two lines? My daughter gets the sine rule but is very upset by this software.

My skin crawls – seriously. Math involves enough intrinsic difficulty and struggle. We don’t need our software tying extraneous weight around our students’ ankles.

Enter Classkick. Even though I’m somewhat curmudgeonly about this space, I think Classkick has loads of promise and it charms the hell out of me.

Five reasons why:

  1. Teachers provide the feedback. Classkick makes it faster. This is a really ideal division of labor. In the quote above we see the computer fall apart over an assessment a novice teacher could make. With Classkick, the computer organizes student work and puts it in front of teachers in a way that makes smart teacher feedback faster.
  2. Consequently, students can do more interesting work. When computers have to assess the math, the math is often trivialized. Rich questions involving written justifications turn into simpler questions involving multiple choice responses. Because the teacher is providing feedback in Classkick, students aren’t limited to the kind of work that is easiest for a computer to assess. (Why the demo video shows students completing multiple choice questions, then, is befuddling.)
  3. Written feedback templates. Butler is often cited for her finding that certain kinds of written feedback are superior to numerical feedback. While many feedback platforms only offer numerical feedback, with Classkick, teachers can give students freeform written feedback and can also set up written feedback templates for the remarks that show up most often.
  4. Peer feedback. I’m very curious to see how much use this feature gets in a classroom but I like the principle a lot. Students can ask questions and request help from their peers.
  5. A simple assignment workflow for iPads. I’m pretty okay with these computery things and yet I often get dizzy hearing people describe all the work and wires it takes to get an assignment to and from a student on an iPad. Dropbox folders and WebDAV and etc. If nothing else, Classkick seems to have a super smooth workflow that requires a single login.

Issues?

Handwriting math on a tablet is a chore. An iPad screen stretches 45 square inches. Go ahead and write all the math you can on an iPad screen – equations, diagrams, etc – then take 45 square inches of paper and do the same thing. Then compare the difference. This problem isn’t exclusive to Classkick.

Classkick doesn’t specify a business model though they, like everybody, think being free is awesome. In 2014, I hope we’re all a little more skeptical of “free” than we were before all our favorite services folded for lack of revenue.

This isn’t “instant student feedback” like their website claims. This is feedback from humans and humans don’t do “instant.” I’m great with that! Timeliness is only one important characteristic of feedback. The quality of that feedback is another far more important characteristic.

In a field crowded with programs that offer mediocre feedback instantaneously, I’m happy to see Classkick chart a course towards offering good feedback just a little faster.

2014 Sep 17. Solid reservations from Scott Farrar and some useful classroom testimony from Adrian Pumphrey.

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:

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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.

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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:

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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.

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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.

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

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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.

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.

Related

  • 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.

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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.

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