## Great Lessons: Evan Weinberg’s “Do You Know Blue?”

If you and I have had a conversation about math education in the last month, it's likely I've taken you by the collar, stared straight at you, and said, "Can I tell you about the math lesson that has me most excited right now?"

There was probably some spittle involved.

Evan Weinberg posted "(Students) Thinking Like Computer Scientists" a month ago and the lesson idea haunted me since. It realizes the promise of digital, networked math curricula as well as anything else I can point to. If math textbooks have a digital future, you're looking at a piece of it in Evan's post.

Evan's idea basically demanded a full-scale Internetization so I spent the next month conspiring with Evan and Dave Major to put the lesson online where anybody could use it.

That's Do You Know Blue?

Five Reasons To Love This Lesson

It's so easy to start. While most modeling lessons begin by throwing information and formulas and dense blocks of text at students, Evan's task begins with the concise, enticing, intuitive question "Is this blue?" That's the power of a digital math curriculum. The abstraction can just wait a minute. We'll eventually arrive at all those equations and tables and data but we don't have to start with them.

Students embed their own data in the problem. By judging ten colors at the start of the task, students are supplying the data they'll try to model later. That's fun.

It's a bridge from math to computer science. Students get a chance to write algorithms in a language understood by both mathematicians and the computer scientists. It's analogous to the Netflix Prize for grown-up computer scientists.

It's scaffolded. I won't say we got the scaffolds exactly right, but we asked students to try two tasks in between voting on "blueness" and constructing a rule.

1. They try to create a target color from RGB values. We didn't want to assume students were all familiar with the decomposition of colors into red, green, and blue values. So we gave them something to play with.
2. They guess, based on RGB values, if a color will be blue. This was instructive for me. It was obvious to me that a big number for blue and and little numbers for red and green would result in a blue color. I learned some other, more subtle combinations on this particular scaffold.

This is the modeling cycle. Modeling is often a cycle. You take the world, turn it into math, then you check the math against the world. In that validation step, if the world disagrees with your model, you cycle back and formulate a new model.

My three-act tasks rarely invoke the cycle, in contrast to Evan's task. You model once, you see the answer, and then you discuss sources of error. But Evan's activity requires the full cycle. You submit your first rule and it matches only 40% of the test data, so you cycle back, peer harder at the data, make a sharper observation, and then try a new model.

The contest is running for another five days. The top-ranked student, Rebecca Christainsen, has a rule that correctly predicts the blueness of 2,309 out of 2,594 colors for an overall accuracy of 89%. That's awesome but not untouchable. Get on it. Get your students on it.

## 3/25 5:30PM Public Talk At Cambridge

I'll be giving a public talk at the University of Cambridge 25 March at 5:30PM on the state of math modeling in print curricula. The event is free but seating is limited. Grab your ticket and I'll look forward to seeing you there.

## Teach The Controversy

I was walking with my wife along the River Corrib in Galway last weekend when we got into an argument that lasted the rest of the walk. I'll present our two arguments and some illustrative video. Then I'd like you or your students to help sort us out.

Argument A: It would be much harder to swim to the other side of the river in the fast-moving water as in still water.

Argument B: It would be just as easy to swim to the other side of the river in the fast-moving water as in still water.

I hope this gets as out of hand for you and your students as it did for me and my wife.

Featured Comment

This excellent question exhibits a quality that is not found often in math curricula: it has the "specificity sweet spot": it is specific enough for a student to answer, but non-specific enough for every kid to agree on the answer. Students making different assumptions will have different responses, thus creating a real mathematical argument.

## Hollywood Hates Math

A supercut of moments in cinema and television where characters hate on math.

In fairness, people hate math. Hollywood just turns on the cameras.

BTW. Here's the behind-the-scenes. I went to SubZin, searched for "math," crossed off the (few) movies that had anything positive or neutral to say on the subject, queued up all the other movies in NetFlix and ripped those scenes over the course of a few months. Then I wrote down all the lines and started moving them around like an essay. The supercut was easier to edit knowing I had some large passages where kids talked about flunking math or adults referred to their own trouble with math. I was also able to make the movies talk to each other, like the dialog between Jamie Lee Curtis and Megan Fox.

Great supercut, but there are also Hollywood movies that celebrate math. A Beautiful Mind, Mean Girls, the TV series Numbers, Good Will Hunting… Maybe fodder for another supercut. :)

Mathematical genius appears in too many of those pro-math movies. Good Will Hunting, Numbers, Real Genius, Beautiful Mind — everyone watching those movies recognizes that the protagonists are far outside the norm.

Worse yet, the math geniuses often aren’t even really doing math. They are math like Buck Rogers is science. Numb3rs is a particularly horrific example of this.

Yup, we are the academic equivalent of dentists.

I disagree with the title. I suggest: Hollywood Hates [School] Math.

2013 Apr 24. Nick Douglas of the Slacktory as a nice rundown of the supercut business.

## My Annual Report 2012

I'm still recording dozens of vital statistics like alcohol, coffee, and media consumption, geographical location, sleep habits, and some others that are no one else's business, really. For the sake of time, I'm only visualizing one of them. The graph of my coffee consumption tells an interesting story about my 2012, the year my body decided it hated caffeine:

I spend about a minute each day recording statistics on my phone and an hour each month transcribing them to Excel. That takes time. But your devices are collecting a lot of those data passively all the time. If you'd like the excuse to learn data visualization and pivot tables in Excel, here's your assignment:

Log into your wireless carrier's web page and download your voice and messaging data for as long as you want but at least the last two years. Then prepare the data in Excel, getting your columns uniform and labeled. Then figure out the top five people you've a) called or b) messaged over those years. Do they change? For extra credit, are the people you call most and message most the same people? If not, what accounts for the disparity?

Leave your work in a comment once you're done. We're looking forward to it.

BTW. 2007, 2008, 2009 (my favorite), 2010, 2011.

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