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Hello and thank you for listening to the teaching math teaching podcast. The teaching math teaching podcast is sponsored by the Association of mathematics teacher educators. The hosts are Eva fan hyzer. Me, dusty Jones and Joel Amidon. But unfortunately, Joel can't be with us today. We're talking today with Stephanie Casey. Stephanie is an associate professor in the Department of Mathematics and Statistics at Eastern Michigan University. We're talking with her today for a number of reasons, including how she helps prospective teachers learn and learn to teach statistics. Welcome, Stephanie, can you tell us a bit about yourself and your background? Sure. And first, I want to say thanks for having me. This is my second podcast, and both are with Ava. So this is I think, a trend. You start another podcast and I get on that one too.
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So I was a kid who grew up in a family of teachers. And so it was natural to me to become a teacher myself. And I fought against it for quite a while. But in the end, succumbed and got a Bachelor's in how to teach mathematics at the high school level and was a high school math teacher in Deerfield, Illinois for 14 years. But I always have my eye on the idea of teaching people how to be teachers. So when I was a teacher, the Illinois State University mathematics department started a program called the Chicago cohort, where they sent professors up to Chicago, we met in the loop one night a week for classes, then we went down there in the summers. So that enabled me to get my PhD while still teaching, I definitely thought it was important for me to have a good number of years under my belt, teaching high school math before I taught people how to teach high school math. So during that time, when I came out of my bachelor's degree, I was all fired up about wanting to teach advanced placement classes, I take an advanced placement math classes, and other classes and high school loved them thought it was awesome. And this is like the pit like that's my goal, I want to be able to teach those classes. So I got some my high school that I was teaching at. And we only offer one math AP course. And that was AP calc, we had both a, b and BC. And it was literally an old boys network, not just the sprays. But for real at my school was the old boys network. And so how is this young whippersnapper, female going to get into an AP course, I had the idea of, well, let's start an AP statistics class at my high school that I was teaching. So the exam had only been given one year before I started teaching. So I only missed one year, that first exam in 97. But so I started the course at my school and started teaching it in 98. And that kind of became my thing. And so I really got into the statistics and statistics education community there. And so I followed that up in my PhD by focusing in on statistics and statistics education, as my specialty area as I went through. So now then I'm out and the mathematics teacher educator at Eastern Michigan University, my focus is on preparing pre service secondary teachers, I also do quite a bit of service work at the secondary level, mostly as well. But my little niche within that, that I really specialize in is preparing people to learn to teach the system. So that's where I'm at in my career. That's cool. So when you think back to when you started teaching the AP statistics course, what was the best advice you received? When you started teaching? statistics? I think the best advice I received is that statistics is not math. And that we need to make sure that we don't teach statistics like it is math, and really focusing in on the differences between the two and staying true to the discipline of statistics in the teaching. This is being recorded audio, so nobody can see me nodding my head. But I agree with that. But can for those of us who are listening who maybe don't know, can you give us some examples of how the statistics is not a mathematics? Sure. One way is just that in the teaching of statistics, particularly now with the big data era that we're in, everything really needs to be tied to data, which are numbers from a context. And we always, always, always want to keep tied to that context, in our work in statistics, that's different from mathematics. Oftentimes, we're working completely context free. Or if we start with the context, we abstract that and get into the mathematics and ignore the context from which that work came from in our work. So that's one big difference. Another one is just how we come to conclusions and the types of conclusions that we come to and statistics are different. We usually can't prove anything for 100% certainty because we're dealing with samples from populations. And so we're making estimates or we're saying, well, we think this to be true, but you are never 100% true for sure that it's true. Whereas in mathematics, we often are coming to definitive answers and things that we've proven to be true no matter what. So how did you move from
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Teaching statistics to teaching people how to teach statistics. Can you describe that? That process? Sure, when I started teaching the AP statistics course, at my school, I was finding that the students coming into my course were ill prepared, they had really not had much statistics in their previous math classes. And although the books that we were using at my school had statistics in them, it was nearly always the very final section of the textbook. And as you know, how often do you get to teach the actual last chapters in your textbook, pretty much never in high school. The reality was that the students were not learning statistics prior to coming to AP statistics. So I dug into why that was a bit. And part of it was because the folks who taught at my school were not comfortable teaching statistics. And so from that, I got really energized into starting first at my school, getting people more comfortable with teaching statistics, and then expanding beyond that to the broader Chicagoland area with work I did with teachers there. And then now my mathematics teacher educator role here, I have two different grants, I have the steam grant and the modules grant. And in both of those, I'm helping write curriculum, which is meant to help pre service teachers learn how to teach statistics at the secondary level. And so these are now going out into the world broadly. But the US probably more specifically as resources that we can use and teacher education to get teachers ready to teach assistance. So if someone was getting ready to teach statistics to teachers, because that's part of the thing that we have to do sometimes, what advice would you give to someone who was starting out, I think, to get familiar with what is important in statistics, and what are the key concepts, get yourself familiar with the Common Core State Standards for mathematics and statistics standards? Those are we know what's in those standards. Also get yourself up to date with technology, technology is driving a lot of what's happening in statistics, not just statistics, education, but statistics in the world. So like I mentioned, the big data era, what is technology making possible now in terms of the graphics that we're making? What are people seeing in the media in terms of statistical representations. Now, what statistical software is out there that we're using, that's new that helps students learn statistics. So for example, I'm in both of the projects I'm on, we're making heavy use of a new software program called code app. And it's free and online, it's kind of successor to tinker plots and fathom if some of the listeners were familiar with those. So you definitely want to get yourself up and familiar with Kodak to be able to teach your future teachers how to use that. So those are some things I would encourage people to look into. So you've mentioned technology has changed. And I'm sure there's other things that have changed, as you think about when you were teaching statistics in the 90s. Versus today. So you mentioned the Big Data era. That's kind of blown up. And there's a lot of interesting stuff there. So what are some of the major differences that you notice, besides stuff you've already talked about with? You know, the development of technology? I'm sure, early on that was pre certainly pre code at pre Tinker plots pre fathom. Is that right? Where you go? Yeah, I mean, we were using graphing calculators, really, for Actually, it was quite quite robust, it can do a lot of statistics. Mm hmm. But in terms of where we are now, I mean, we're just so far beyond that. It's amazing. We need to have students working with big data. And by big data, as you know, k 12 level, it needs to be sizable, not like Google size, sizable, but you know, hundreds upon hundreds of cases, and needs to have multiple variables, they need to be thinking about representations that incorporate beyond more than two variables at once. That's a big difference from 97 to Now, another one that technology has really driven it to be a big change is the predominance of simulation based inference. So that was not, you know, that was just an infancy back when I started teaching statistics, and now it's gone. gung ho. And there's a lot of textbook materials that are out there that support that the Common Core State Standards for mathematics, statistics, standards, encourage that and expect students to be learning, simulation based inference. So that's a really big change too. So I have a few questions. Um, first, when you started talking, you talked about how to context is really an important aspect in statistics. And I was wondering if you could elaborate, especially since you're working on a textbook, what kinds of contexts you're using, and also how you're addressing some of the current issues. For example,
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I know that the statistics classes where I'm at, often have contacts that talk about male and female, which, you know is in its current time, not a great way of using God binary. So those are some of my questions. So I'm wondering if you can talk a little bit about what kinds of contexts you selected and why and how you're addressing some of the limitations? Yeah, great question. So I'd say across both projects, we're really trying to get big data incorporated into the material. So again, like I said, lots, you know, like hundreds of threads upon, you know, as our case size, and lots of attributes that they're looking at in coordination with each other. But in terms of the context that they're coming from, a few considerations we have. So one is that we're trying to get our materials using context that we think both pre service teachers will be interested in, and their future students will be interested. So they could take these same datasets, and use them in their own classrooms once they have them. So some examples I can think of that we have in the materials are things like roller coasters, so things about like, what are the characteristics of roller coasters? And how are they related? Like, you know, how fast they go? how high the biggest drop is? Are they made of wood or steel? Do you go upside down those types of things? and other contexts that students would, you know, have this age to be interested in? Another big consideration that we have taken into account is, what are some meaningful conversations we need to be having with pre service mathematics teachers? And how can we do that by driving it through an analysis of a data set, for example, the new amtv standards for preparing teachers mathematics has a really big emphasis on the social context of mathematics, teaching and learning and implications for that in terms of students experiences in mathematics. And one aspect of that is tracking and what's happening in our mathematics classrooms in the United States. Regarding tracking, for example, we have our pre service teachers analyzing a graphic that's looking at what is the racial breakdown of students taking STEM courses at the high school level, by course, and compare that to the distribution by race overall, in all high schools. Another context that we do this kind of related to that is just looking at societal factors that influence education. So school funding is a big one. And who's getting the money? And why are certain groups getting more money than others are? And what are the different factors of that? And how can you see that through analyzing the data set? So those are two big things that we're looking for in deciding the context and these two curriculum projects. And one another thought I had while you were talking is since you've referred to the Common Core Standards quite a bit, they took a lot of statistics out of the early part of the education. What do you think about that? Definitely, I'm not a fan of that. So Sinclair, remember when that happened, that came out, I distinctly remember seeing my PhD advisor Cindy Lange girl who's big in statistics, education, the AMT III conference that year and going to her and being like, What has happened? What are we going to do and lamenting about this and, you know, trying to figure out something that we can try to do to intervene. But alas, we were not successful at that. So I think for now, the best we can do is try to make their education. Fantastic. Starting sixth grade pretty much on that that's pretty much where the Common Core starts really digging into statistics. Yeah, I'm in Texas. So we don't have the Common Core State Standards. But we have the Texas essential knowledge and skills or No, that's not what I'm sorry, we have something else. We have some other iteration going on the peaks, it's whatever, whatever it is, but we do have some statistics. When I taught fourth grade, we were doing dot plots. So I was glad about that. My daughter is in seventh grade now, and came home and said, Hey, I need to find the mean absolute deviation. And that just made my heart.
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Someone is teaching mean, absolute deviation. That's really cool. And that's something else I think. Sorry. Yeah. There's a little bit of measurement and data in K five, but it's minimal. And there's probability. That's something else. I think that's probably different than 97. If I'm, maybe I'm mistaken, but was mean absolute deviation around back then, or was it being talked about back then? Not really, no, no, I think this idea that we need to help students work up towards these more sophisticated statistics has gotten a lot more leverage and worked his way into our standards. More recently than that, yeah. One thing I like about the esteem materials. I taught with some of these materials over the summer in a statistics class for pre service teachers.
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And Stephanie and the team there had set up some things where they were looking at students working in statistics, like students doing work, or teachers, videos of teachers doing work or also animations in statistics. And that allowed the pre service teachers to kind of get a window on, what would you do next? What sorts of steps would you take and just to cross pollinate that the podcast? I think Stephanie, guys kind of talked about using some of those lessons sketches with Joel Amidon. Yeah, our co host in a issue or an article and math teacher educator, is that right? That's right. Yeah. So that's all wrapped up in this same work. Yeah. When I switched over to become a mathematics teacher educator, it was very clear to me that there's a big gap in representations of the practice of teaching statistics at the secondary level, whether they were videos, animations, documentation, documentation, student work, we didn't have hardly anything compared to what had been done in other content areas, or particularly at the elementary level, there's a lot more there. So through these projects, I hope that we're starting to fill that gap that we've had in education. So in your life as a mathematics teacher, educator, or statistics, teacher, educator, or just a statistics educator, I don't know how you want it, which hat you want to put on. What makes a good day for you? Well, I love teaching, I'm a people person. So I would definitely include teaching statistics and or how teaching people how to teach statistics as a part of that day. I also would include working with colleagues, not only at Eastern Michigan, but across the country and across the world. That's one really exciting part I've enjoyed about being a mathematics teacher educator is the collaboration I've had with fantastic people from literally all over the world. And you don't get that so much as a high school math teacher. So I have loved that kind of kicked off with my participation as in the star Fellow Program near the beginning of my mathematics teacher educator career. And that allowed me to make great connections that I worked with a lot of fantastic people for many years. And then I've gotten plugged into the statistics, Teacher Education crowd, so great colleagues there. So you know, something where I'm working with colleagues about things that we love and are passionate about and trying to make a difference and the education that's happening out there regarding statistics, I think we've seen now in 2020, that it is so important, I'm happy to be in a field that is growing in importance in our world. So besides statistics, what do you do for fun? What do you mean, I don't have time for
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this stuff. I'm just kidding. What do I do for fun? Well, I cheer on my kids and their activities. They're both high schoolers now. But they've done all kinds of activities, sports and theater and things like that. I also enjoy cooking. I also love to be outside as much as possible. So whether I'm hiking, biking, running, playing tennis, you have it, I tried to get outside as much as possible. So in the show notes, I'm going to I'm saying this now i hope i remember to do it. I'll put links to the esteem project. And the modules project. I'll put a link to Kodak. I like Kodak a lot. So I try to tell people about that a lot. So that'll be easy for me to remember that the esteem project s t e m, enhancing statistics, Teacher Education with E modules. Is that what That's right, that stands for these are Can you describe what an E module is? Sure, yeah. Self invented term. So you know, it makes the acronym work. Right, it works with the acronyms so we had to run with it. So II modules is the idea that what the way that we're packaging our curriculum is in a learning management system ready. It's ready to import into learning management systems, you just do an import. So we've got those imports ready for the most common learning management systems like canvas and Blackboard and Moodle. Okay, so I we're calling them electronic is because we're not actually making a hardback textbook or anything, we're not even making like a PDF document of all of our stuff. They're literally electronic in the sense that they're going right into the learning management systems that are going to be used in that setting. And then they're modular, meaning you could take some and leave some others out or an even modify them to fit your particular situation. Yes.
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Okay. We want them to work for whatever situation people find themselves in. We have what we call the foundation module, which is like the core thing that we want, if you're going to do anything you need to start there. But in addition to that, we also have two other modules that focus on areas of statistics that are very important to society important in terms of what the curriculum standards say k 12. Students should be learning and important with respect to we know teachers need help and learning how to teach them
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So those two areas are inferential reasoning, and Statistical Association. And then those three modules, we also have some, like key assessments that we've designed that you can use as well. That's cool. So the other project that you mentioned was modules with the little like, squared on the s. Yeah, I don't know what that means. So, okay, this up to make sure I get this right to you know, how these acronyms are okay.
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Mathematics, of doing understanding, learning and educating for secondary schools. Okay.
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That doesn't sound like it's statistics specific. So you, you are correct, yes. Okay, project that's addressing four different content areas, algebra, geometry, statistics and modeling. So I'm on the statistics writing team. But the idea is these four core areas are ones that secondary pre service math teachers usually have to take an upper level course in. Okay. And oftentimes, those courses aren't meeting the needs of pre service mathematics teachers and developing their medical knowledge for teaching. And so these materials are meant to change them. Oh, I am excited to try out some of those materials. Yeah. I'm glad to hear it. We want you to use it. We certainly don't want it to be like we've made all this stuff. And then it just says, sits on the shelf, metaphorically speaking.
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Well, this is exciting, Stephanie, thanks so much for taking the time with us to Well, I just like to talk about statistics education. So that's good. But thanks for sharing with us today. Thank you for inviting me. This has been great. Well, thanks again for listening to the teaching math teaching podcast. Be sure to subscribe to the podcast, we hope that you're able to implement something you just heard, and take an opportunity to interact with other math teacher educators. Just one more thing before we close out. The 2021 AMT annual conference will take place virtually this year with synchronous presentations and asynchronous poster sessions, please say February 11 through the 13th and February 18, through the 20th for the 2021 virtual AMT annual conference. If you've never attended before, this just might be your opportunity to do so. The meeting will feature shorter days and we would normally program for an in person convening, with schedules designed to accommodate multiple time zones. For more information, check out the AMT website at AMT IE dotnet