Category Archives: BMTN

Welcome to the Math Kitchen

A couple of weeks ago, at an ATOMIM Dine & Discuss launching our Becoming the Math Teacher You Wish You’d Had book study, Tracy Zager shared the following quote:

The front and back of mathematics aren’t physical locations like dining room and kitchen. They’re its public and private aspects. The front is open to outsiders; the back is restricted to insiders. The front is mathematics in finished form—lectures, textbooks, journals. The back is mathematics among working mathematicians, told in offices or at café tables.

Front mathematics is formal, precise, ordered, and abstract. It’s broken into definitions, theorems, and remarks. Every question either is answered or is labeled: ‘open question.’ At the beginning of each chapter, a goal is stated. At the end of the chapter, it’s attained.

Mathematics in back is fragmentary, informal, intuitive, tentative. We try this or that. We say, ‘maybe,’ or ‘it looks like.’

-Reuben Hersh, Professor Emeritus, Department of Math and Statistics, UNM

Tracy explained at the meeting that restaurants and theaters have a “front” where everything is presented perfectly to the public and a “back” where the chaos happens. This is the metaphor that Hersh is using. Too often our students are only exposed to the “front” of mathematics and none of the “back.”

I recently shared this at a BMTN meeting when a colleague coined the term “math kitchen.” And then she said, “Put on your apron – it’s going to get messy in here.” It made me think about how often my students want to have their math papers be perfect. Every mistake must be erased. Nothing can look messy. Am I alone here?

Another colleague said that she used to make all of her students do math in pen. That way they had to cross out mistakes. They couldn’t erase them. I think this is a brilliant idea.

Too often I hear my students say things like, “I remember doing something like this” or “I’m trying to remember what my teacher told me” or, God forbid, “I never learned this before.” What are we doing to our students that makes them think that they should have memorized or learned before what we are trying to teach them now?

So, in the spirit of exposing students to the “back of math” I say, “Welcome to the math kitchen. Grab a pen and put on your apron. It’s going to get messy in here.”

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My new PDSA

It’s the beginning of another year with the Better Math Teaching Network, so it’s time to figure out a new change idea. Okay, to be honest, the beginning of the new BMTN year happened back in July, but I was struggling with a new change idea. At least I’m teaching a bunch of algebra this year.

The aim of the Better Math Teaching Network (BMTN) is to increase the number of students who connect, justify, and solve with depth in algebra. Here’s how we’ve defined what that means:

  • Connect. Making connections among mathematical algorithms, concepts, and application to real-world contexts, where appropriate.
  • Justify. Communicating using mathematical thinking as well as critiquing the reasoning of others.
  • Solve. Making sense of and find solutions to challenging math problems beyond the rote application of algorithms.

Last year, I was solidly in the justify category and it was really fun. Even though I’m not teaching that class again this year, I have lots of ideas about how to infuse my classes with the concepts of “claim, evidence, reasoning.” In fact, I think that will be part of my term 2 PDSA since I’ll be teaching Intro to Logic again.

Back to this term. This term I am teaching 9th graders. Not only are they new to our school, they are coming from so many different backgrounds. These students did not grow up together. So, part of the purpose of the class is to help them to get to know each other. Another purpose of the class is to introduce them to a math class (possibly) unlike any other that they’ve experienced. This is a math class where the teachers don’t tell the students exactly what to do so they can “practice it” 50 more times on “exercises” where only the numbers have changed. You see, that’s not deeply engaging with math, or algebra in this case.

Given that I would be teaching this class in term 1, I had to figure out what I could focus on that would make this experience better for my students. Last year, a bunch of people in the BMTN attempted the Connect strand and found it to be really difficult. I was thinking to myself, I like the Justify strand – I’m really comfortable there. Solve wouldn’t be too bad, either. But the more I thought about it, the more I thought that I really needed to get into that Connect strand. So that’s where I am.

Ultimately, I landed on connecting to the concept of slope. It’s a huge concept, with so many connections. But when you ask kids about slope, they typically say something like “rise over run” or they’ll quote a formula or they’ll say “y = mx + b.” It’s not their fault that they don’t have a deep understanding of slope. It’s ours.

So, my term 1 PDSA is about giving my students opportunities to see slope in different contexts. I wonder if I’ll broaden their thinking … stay tuned.

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Claim, Evidence, Reasoning: Final results

In this final post about the Claim, Evidence, Reasoning approach to teaching statistics, I will share some student results. Fundamental questions with the PDSA approach to reflecting on and improving practice are:

  • Will students engage?
  • Will students learn what I am attempting to teach?
  • Will students produce quality work?

Nearly all of the students in these two classes had prior experiences with statistics which allowed me the freedom to find a new approach. That said, there were definitely times when it became clear that some content instruction was needed, especially when we got into correlation and linear regression. But instead of trying to front-load all of the content, I waited until the need arose. For example, in looking at what students wrote about the class data it became clear that some instruction about regression lines and correlation coefficients was needed.

Now, to answer those questions.

Will students engage?

They didn’t at first – in that disastrous failure only 10% completed the first assignment. But I certainly learned from that experience, regrouped and restructured my approach. And then they engaged. My data show that 100% of my students engaged with the class, process, and content at some point and that 90% engaged consistently by the end of the term.

Will students learn what I am attempting to teach?

I was attempting to teach my students how to apply the claim, evidence, reasoning process that they had previously learned in humanities and science to statistics. Reviewing work against the rubric helped to build an understanding of what quality looks like. It also kept us focused on the goal of claim, evidence, reasoning. By then end of the class, 95% of students were able to review statements through this lens and identify whether or not they were on target.

Will students produce quality work?

This is the big question, right? It’s great if they will engage – that’s the first step – but if they aren’t working to producing quality work then what have they actually learned? Here are some representative examples of student work.

Analyzing movie data This assignment followed the best actor/actress investigation.

Education vs unemployment  Vinyl vs digital album sales  Juvenile incarceration rates This was the final assignment of the univariate data unit. Students had their choice of data to analyze.

Analyzing cars This assignment followed the class data investigation and included the opportunity for students to revise their work following feedback.

Fast food nutrition  1919 World Series This was the final assignment of the bivariate data unit. Students had their choice of data to analyze.

I will leave the question of whether these examples represent quality work to you, the reader. I hope you will let me know what you think.

 

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Claim, Evidence, Reasoning: About the data

In the last post, I shared the general process that I developed to teach statistics through the lens of Claim, Evidence, Reasoning. This process was tested and refined through several iterations. The data that I chose for these assignments & iterations was critical to student engagement and learning.

How do I know what kind of data is going to be interesting to students? Well, I ask them. I’ve been asking them for a lot of years. Every data set isn’t going to be interesting to every student, but overall, I have been able to identify and collect pretty good data sets.

In the spring term I used these data sets (and the associated class devised claims):

  • Minutes between blast times for Old Faithful (Claim: The time between blasts will be 90 minutes plus or minus 20 minutes.)
  • Ages of Best Actress and Best Actor Oscar winners (Claim: The ages of the Best Actress Oscar winners is typically less than the ages of the Best Actor Oscar winners.)
  • Box office (opening weekend, domestic, worldwide), critics & audience ratings for “original” movies and their sequels (Claim: Original movies are better than sequels.)
  • Juvenile detention/incarceration rates for various types of crimes by sex and race (Claim: African-American males are incarcerated at a higher rate than any other subgroup.)
  • Education level and unemployment rates (Claim: People with a higher level of education have lower unemployment rates.)
  • Sales of vinyl records and digital album downloads (Claim: Sales of vinyl records will soon overtake digital album downloads.)
  • Class measurements such as height, arm span, kneeling height, forearm length, hand span, etc (Claim: Human body measurements are related in a predictable way.)
  • Car data including curb weight, highway mpg, fuel type, and engine size (Claim: Highway mpg depends the most on fuel type.)
  • Fast food burger nutrition including calories, fat, protein, carbohydrates, etc (Claim: Fast food burgers are unhealthy.)
  • Baseball data from the 1919 Chicago White Sox (Claim: The evidence supports the decisions made about the accused players in the 1919 World Series.)

Even with all of these options, students added their own:

  • Skateboarding data including ages and birthplaces of known skaters and number of skate parks in a state (Claim: Professional skateboarders are most likely to come from California.)
  • Olympic male swimming data (Claim: Michael Phelps is the best Olympic swimmer of all time.)

What’s important about all of these data sets?

They all provide multiple variables and opportunities for comparison. They offer students multiple ways to investigate the claims. They allow students to create different representations to support their reasoning. So, the lesson here is that the data sets used much be robust enough for students to really dig into.

Imagine what could happen if the course were integrated with science or social studies.

Next post: The results

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Claim, Evidence, Reasoning: Starting fresh

I’m not an AP Stats teacher. I did that once. What’s important to me is that all students who graduate from high school have an opportunity to think and reason about real data in a deep and meaningful way. AP Stats is typically reserved for a few juniors or seniors. Maybe they don’t want to do AP Calc, or maybe they’ve already done it. Both of these reasons are unacceptable to me. Data literacy needs to have a higher profile – it needs to be more important than being able to simplify rational expressions. Our students need to be able to reason about data that’s presented to them in the press, or on social media, or by our elected officials. That’s my personal crusade.

Since last December I’ve been on this journey to improve my statistics teaching and the learning of my students. I shared my catastrophic failing first attempt and progress made with that group.  One of the beautiful things about our trimester schedule is that it allows me to immediately apply new learning to a new group – assuming that I am teaching a new section of the same course, I don’t have to wait a whole year to apply what I’ve learned. Luckily, this was the case this year. So, in late March I was able to begin anew, armed with what I learned during the previous term.

My spring term class was also a small group, but quite different from the winter class. This new class had more than 50% who struggled with writing. Since the focus of our work would be “claim, evidence, reasoning,” I would have to find alternative ways for these students to share their learning and their arguments. I wrote my new PDSA form and jumped in, hoping that I had learned enough from the winter term to be somewhat successful this time. (For information about PDSA cycles, see here and here.)

In general, I used this process to introduce concepts:

  • Tell students about the data, usually on paper and verbally, and give them time to make predictions about what they expect from the data. Students do not have access to the data yet. Have some discussion about those predictions. Write them on the board (or some other medium). These predictions become claims to investigate.
  • Give students access to the data & some graphical representations, usually on paper, and have them think about how the data might or might not support the claims that they made. Then ask them to discuss the data with a partner and determine whether or not the data support their claim.
  • Ask them to write a statement about whether or not the data support the claim and why. The why is important – it’s the evidence and the reasoning piece of the “claim, evidence, reasoning” approach.
  • Collect students’ statements, collate them into one document, then have students assess the statements according to the rubric. The focus here is on formulating an argument, not on calculating statistics or representing data. That comes later.

I completed this cycle twice, with two sets of data: minutes between blast times for Old Faithful and ages of winners of Best Actor and Best Actress Oscars.

These are the scaffolds that I provided for the first couple of steps for the Oscar data: predictions & analysis. Remember, the objective at this point is on making an argument, not calculating statistics or creating representations. Taking that piece out of the mix allowed students to focus on finding evidence and formulating reasoning for the claim that we had produced as a class. The next step is to collectively look at the statements that the students produced and assess where they fall on the rubric. This was the second time that we reviewed student work against the rubric. All of this introduction was treated as formative, so although the assignment (and whether or not it was completed) went into the grade book, no grade was attached.

The process for practicing was similar, but included less scaffolding and did not include the step of reviewing student statements. It generally went like this:

  • Tell students about the data, usually on paper and verbally, and give them time to make predictions about what they expect from the data. Students do not have access to the data yet. These predictions become claims to investigate.
  • Give students access to the data, generally in digital form, and a template to help them organize their thinking.
  • Have students calculate statistics and create representations to provide evidence to support or refute their claims.
  • Have students paste their representations into the template and write a statement or paragraph explaining the evidence (this is the reasoning step).

I did this cycle twice for our unit on univariate data: once using data about movies and their sequels and again using a variety of data from which students could choose. By the 4th cycle this is what the assignment directions and template looked like. This was the end of unit assignment for the spring term.

At the beginning of this post I mentioned that more than 50% of this particular class had been identified as having difficulties with writing. So, what did I do? I pushed them to write something – at least one statement (or, in some cases, two) – and then offered to let them talk through their evidence and reasoning with me. I knew that there was good reasoning happening, and I wasn’t assessing their writing anyway. So, why not make the necessary accommodations?

Next post: The importance of data choices.

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Making Progress

My class made some predictions about car data, without seeing it, and came up with 3 claims:

  1. The heavier the car, the lower the MPG.
  2. Electric cars will have a lower curb weight (than non-electric cars).
  3. Gas powered vehicles will have higher highway MPG than electric or hybrid vehicles. (We think this was written incorrectly, but didn’t catch the error, so decided to go with it.)

We focused on claim 1 first. Students easily produced the scatter plot …

03-15-2017 Image002

and concluded that there didn’t appear to be much of a relationship between highway MPG and curb weight. But they wanted to quantify it – evidence has to be clear, after all.

03-15-2017 Image001

Because of the viewing window, the line looks kind of steep. But the slope of the line is -0.01 (highway mpg / pound), so it’s really not very steep at all. And the correlation coefficient is -0.164, so that’s a pretty weak relationship when we group cars of all fuel types together.

Are there different relationships for the different fuel types?

03-15-2017 Image003

Turns out, yeah.

After some individual analysis, some discussion, and a scaffold to help organize their work, students shared their claim-evidence-reasoning (CER) paragraphs refuting claim 1.

Working on the quality

Step one was getting my students to write these CER paragraphs. (I’ve written about this before and how disastrous my efforts were.) Step two is improving the quality. I shared a rubric with my students.

rubric

We all sat around a table (it’s a small class) and reviewed all of the paragraphs together. They talked, I listened and asked clarifying questions. They assessed each paragraph. They decided that most of their paragraphs were below target. They said things like:

  • “That’s some good reasoning, but there’s no evidence to support it.”
  • “I’d like to see some actual numbers to support the claim.”
  • “I really like how clearly this is stated.”

Even though it took time to review, it was worth it.

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“I can’t wait to find out!”

As stated in the last post, Learning from Failures, I decided to adjust my approach to having students analyze and discuss data. We’d put a lot of time into working out many of the kinks, but it was really time to move on to scatterplot representations of data. My students already knew a lot about scatterplots and best-fit lines, so this allowed me to dive right in with some data.

Rather than stating a claim, I started with a statement and four questions:

I have the following measures (in cm) about 54 students: height, arm span, kneeling height, hand span, forearm length, and wrist circumference.

  1. Which pair(s) of variables do you think might show the strongest correlation? (And what would a strong correlation look like in a scatterplot?)
  2. Which pair(s) of variables do you think might show the weakest correlation? (And what would a weak correlation look like in a scatterplot?)
  3. Which variable (from the list above) do you think would be the best predictor of a person’s height (in cm)?
  4. Write one claim statement about the class data variables.

These questions forced them to think about the data and make some predictions about what they might see once they were able to access it. We hadn’t really talked much about correlation, so I was really interested in their responses to what strong and weak correlations look like on a scatterplot.

Generally speaking, they said that strong correlations

  • look like a line
  • can almost see a line
  • looks like a more defined line
  • looks pretty linear

and weak correlations

  • look like randomly placed dots
  • have points that are far from the line
  • looks more spread out and scattered
  • has dots all over the place

As for question 3, there was quite a debate between whether arm span or kneeling height would be the best predictor of a student’s height. One side (6 students) argued that arm span would be the best predictor because “everyone knows that your arm span is about the same as your height.” The other two students claimed that kneeling height would be a better predictor because “it’s part of your height.” Both sides stuck to their convictions – neither could be swayed, not even by what I thought was the astute observation that kneeling height is probably about 3/4 of height. This prediction was made by a student in the arm span camp!

Students each received their own copy of the data and investigated their claims. During the next class, we took a look at a couple of those claims, together. The plot on the left is height vs arm span, with the line y = x (height = arm span). The plot on the right is height vs kneeling height, with the line y = (4/3)x (kneeling height = 3/4 height).

More debate ensued, though most admitted that kneeling height had a stronger correlation to height than arm span did (for this data, at least). And maybe the 3/4 wasn’t the best estimate, but it was pretty close. They also talked about those outliers, which led to a conversation about outliers and influential points.

Moving from Class Data to Cars

I took a similar approach with the next data set.

I have some data about cars, including highway mpg (quantitative), curb weight (quantitative), and fuel type (categorical: gas, hybrid, electric). Think about how these variables might be related and make some predictions.

  1. How might the highway mpg and curb weight be related?
  2. how might the curb weight and fuel type be related?
  3. how might highway mpg and fuel type be related?
  4. Do you think there might be any outliers or influential points? If so, what might they be?

Through some class discussion, we came up with the following claims and predictions.

img_20170215_134600196_hdr

Students still had not seen the data and one of them said, “I really can’t wait to see what this looks like!” Another said, “Yeah, I’m not usually all that interested in cars, but I really want to know.”

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