## The Do You Know Blue Student Prizewinner

Rebecca Christainsen had the highest score of any student on our Do You Know Blue machine learning activity. Yesterday was her last day of school at Terman Middle School in Palo Alto, CA, so Evan Weinberg, Dave Major, and I sent her math class a pizza party in her honor.

Because we're keeping the activity available for you and your students to use as they study inequalities, we aren't going to go into much depth on all the different rules contestants used. But I asked Rebecca how she came to her final, game-winning rule, and she told all:

My teacher first showed me the website, and I decided to try it out. My first attempt scored me only around 18%, but since hardly anyone had tried it out yet, I was ranked 33rd. After that, I was encouraged to try more equations, and suddenly thought of all the different types of equations that I could use, and moved to squared terms. One of the first equations that I came up with was b2>r2+g2. I simply used trial and error to come up with new equations, and I recorded each equation that I used and the percentage. I combined different equations together, and a few different combinations even had the same percentage.

Nobody beat that.

Extra Credit: How many of the Standards of Mathematical Practice does Rebecca evoke in that quote?

## 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.

## Contest: Do You Know Blue?

a/k/a A Netflix Prize for K-12 Math Students
a/k/a Let Dave Major, Evan Weinberg, and Me Buy Your Class A Pizza Party

Can you teach a computer to recognize the color "blue"? Head to Do You Know Blue? and find out. If you do the best job teaching the computer, we'll send your class a pizza party in appreciation.

Enter the contest as many times as you want. Come back and check out your standing at this page.

You have until Monday 5/27 at 7:00AM Pacific Time.

Disclaimers

• Anybody can participate but the winning entrant will need to be a K-12 student in the US.
• \$100 maximum on the pizza party.
• You'll have to include an e-mail address, school name, and teacher name if you want to compete for the pizza party.
• If multiple people take the top spot we'll draw the winner randomly

## The MTT2K Prize

Let me just point you to Justin Reich's post on The MTT2K Prize he and I are co-sponsoring and co-judging. I only want to add a +1 and maybe a smiley face next to this sentence:

As far as I'm concerned, MTT2K has brought all kinds of good to the world.

I'd like to see some more of the kind of engagement we saw this last week, the kind where online criticism turns into improved outcomes for millions of students in the span of 24 hours. I'm excited to see what comes of this.

## My #1 and #2 Pick

My top two picks were interchangeable until the very end and my top selection, in the end, reflected my slight preference for minimal design over maximal design.

1. Frieder Knauss

I can add very little to the appreciation circulating on this site except to say that Mr. K manages the hat trick of a) personal retrospection, b) data design, and (the rarity) c) editorial.

That he does this in several thousand fewer pixels than all of his competitors is to his credit, as is the vomit-themed color palette which he somehow sells as an element of his NCLB nausea.

2. Sam Shah

That Sam didn't place speaks to the overall quality of the entire slate. From fonts to colors to axes and grids, none of his design choices cohere. Yet he tosses them all on the same wall with a stuffed buck and the whole thing looks like some kind of genius aneurysm. The herkyjerky, undistributed, unaligned tabs on his "Blog Hits" slide are a particular high point for me.

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