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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