The MCIULearns Podcast

Transforming Math Education: Integrating Data Science with Dr. Tanya LaMar

• Montgomery County Intermediate Unit • Season 6 • Episode 11

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Dr. Tanya Lamar joins us to discuss the transformative power of data science in mathematics education, emphasizing the need for students to see math as relevant and engaging. Join us as we learn how integrating data science into high school curricula can shift perceptions of math and empower students to embrace their mathematical capabilities while preparing them for quantitative fields.

Dr. Tanya LaMar:

So students being engaged in a math class where why they're learning it is immediately clear, changes things for them. College was the first time I saw math as being about logic and reasoning and sense making, and I could really do it when I tried hard and sat down and focused and made connections. At the same time, I was really frustrated by the fact that I didn't see math that way until college. The why that's driven me has always been around supporting students to see themselves as math people and to be empowered by their own ability to make sense of mathematics.

Brandon Langer:

Hello Montgomery County and welcome to the MCIU Learns podcast. My name is Brandon Langer, I'm the Director of Innovation and Strategic Partnerships at the Montgomery County Intermediate Unit in Norristown, pennsylvania, and today we are diving into another data and math conversation with one of our project consultants, who you've heard recently from Kirpa, so I'm going to hand it off to her.

Kirpa Chohan:

Let her introduce herself and then she can introduce our special guest hand it off to her, let her introduce herself and then she can introduce our special guest. Thank you, Brandon, for hosting us today. I'm Kirpa Chohan, an educational consultant at the Montgomery County Intermediate Unit, and I'm really excited to introduce Tanya Lamar. Dr Lamar holds a PhD in mathematics education from Stanford University, where she researched the role of data science in fostering STEM career interest among high school students. She has published extensively on equity in K-12 data science education and previously served as a curriculum director for women in data science. A former high school math teacher in South Central Los Angeles, she co-authored the U-Cubed Explorations in Data Science curriculum and has trained educators nationwide. As a first-generation college graduate, dr Lamar is now the CEO of Struggling, an online math learning platform. Thank you for joining us here today, dr Lamar.

Dr. Tanya LaMar:

Thank you, thanks so much for having me.

Kirpa Chohan:

So, first, because you have such a creative journey I would say math education from classroom to non-classroom. I'd love to hear more about your journey. What inspired you to get into data science and education in the first place?

Dr. Tanya LaMar:

At the risk of going a little bit too far back, but I'll make it quick. So, as you said in my intro, I am a first generation college student and I majored in math, but I didn't. It was a very roundabout journey getting there, so, and so now we'll have to go back to high school for just a quick second. I was enrolled in AP Calculus in high school, but I for some reason got placed in a accelerated program that squished all of trigonometry into one semester. And then we got into AP Calc and I didn't understand any of the trig and so it was not the right fit for me at the time, and so I dropped it. Then I got to college and I thought you know, I'll be a business major. I still really wanted to be good at math and be seen as smart at math, but I didn't really think that I was. So I did all the math classes for my business major, which included business calculus Side note business calculus has no trigonometry. So I did great. And then I thought you know what, maybe I do have, what it takes to major in math. Let me try one more time. And I enrolled in college level calculus and I did not have the trig. So I actually started my math degree in pre-calculus, which is really not typical for a math major to start at that early stage of a class. But it ended up being the best thing for me because it gave me a solid foundation in the sort of fundamentals of the calculus series and then I was able to build from there and feel really confident. And so college was the first time I saw math as being about logic and reasoning and sense making, and I could really do it when I tried hard and sat down and focused and made connections. And at the same time I was really frustrated by the fact that I didn't see math that way until college. So I went to get a master's in credential at Stanford with the goal to support as many students as possible to understand math as this beautiful, connective, creative, useful topic that it really is, instead of this topic where you memorize and reproduce materials or reproduce procedures. And so I taught in LA Unified for five years and at this point this is, like you know, 2012. So data science as a class doesn't exist yet.

Dr. Tanya LaMar:

Then I decided I'd like to go back to Stanford for a PhD in math education, and the why that's driven me has always been around supporting students to see themselves as math people and to be empowered by their own ability to make sense of mathematics. To make sense of mathematics and throughout my time, you know, doing research and working as a PhD student this little thing was kind of itching at me, and that was I majored in math and didn't know what to do with my degree other than teach. And I had students when I was teaching ask me all the time when am I ever going to use this? And now I'm training teachers to be math teachers and I know they're going to get that same question and I've never been able to give a satisfying answer to that of when am I ever going to use this, other than you need it for the next class?

Dr. Tanya LaMar:

Um and so when belonging and identity was such an important driving factor for me yet I'm still promoting mathematics that I can't give a good reason why you're learning it made me start to kind of look in other places of like what can we do in mathematics to make it so that more students feel like they can bring their full selves and make sense of things that matter to them At the same time?

Dr. Tanya LaMar:

So my PhD advisor is Dr Jo Bowler and she was starting to investigate, you know, similar questions around data science and actually and she was kind of coming across data sciences, maybe the answer to that. So I started working with her and creating materials and then the final kind of leap was for my dissertation study to actually look at a high school data science class. Luckily enough, the timing worked out right, for as I was getting ready to do my dissertation, data science courses were popping up around the US, but especially in the Bay Area, so I was actually ready to do my dissertation. Data science courses were popping up around the US, but especially in the Bay Area, so I was actually able to study a classroom implementing a data science curriculum.

Brandon Langer:

I'm really jealous because Mathematical Mindsets is one of my favorite books. It's on the shelf right behind me here, so that's an awesome story and really probably speaks to a lot of people's journey that have progressed through any subject area that, academically speaking, through K-12, maybe didn't always make a lot of sense or the way in which we approach and teach it didn't anchor them, but they still had a passion and drive through there. I'm curious you know, how do you define data science and what makes it such an important, you know topic and field to start looking into in today's educational landscape?

Kirpa Chohan:

And I'm going to add to that because I get this question a lot what is the difference between data science and statistics?

Dr. Tanya LaMar:

Oh, I love that. So my answer actually addresses that. So I'm actually going to use a definition that comes from a different author. I'd have to get you the citation, but I think it's Fincer.

Dr. Tanya LaMar:

The data science sits at the intersection of mathematics and statistics, computer science and then substantive expertise. So the way that data science came about as a subject area is the people working in well, back it up a little bit. It's not until recently that we've been able to record all of the massive amounts of data that we create, you know, simply by existing. But the ability to capture that data and store it is something that you, you know, technology is more recently caught up to. So, okay, great, now we have all this data that we know that we could you know mine and find a lot of interesting answers or important answers to important questions from that data.

Dr. Tanya LaMar:

But those with the computer science skills that, so the ability to wrangle that data through computer science or through programming didn't necessarily have the math and statistics skills to make sense of what can you say from that data. And then, vice versa, those with the math and statistics skills didn't necessarily have the computer science skills to understand how to wrangle the data. So there's that overlap of these two skill sets need to kind of supplement each other. And then the third of that kind of three circle Venn diagram is around substantive expertise, and what that means is having contextual understanding around the data that you're investigating. So we can't just like pull a data set that we know nothing about and start to make sense of it. You need to understand the context that it comes from. You know some of the complexities of what's going on in the way that data was collected or the you know scenario in which it was collected, and so it's those three spaces coming together. Is data science?

Kirpa Chohan:

This is very new because we never had these offerings and this space to explore all this data, because our world wasn't as data focused before right, so it's cool to have this new math that does focus on it. So you're one of the big advocates of having it in high school. Why high school? Why should it not be offered in college? Why is high school where they should have that offering?

Dr. Tanya LaMar:

I think actually I know from my dissertation study that Data science has the power to capture students' attention, their intrigue of why go and pursue any quantitative topic. A whole giant group of students that could be incredible data scientists but never got the opportunity to see what this subject is and instead their K-12 math experience just told them they weren't a math person. No-transcript, it provides these opportunities for students to see. Oh, that's why I would want to you know work with numbers. That's why I would want to understand statistics, that's why I would want to understand linear models, because you can use it to analyze data that can answer questions about spaces you care about. It is immediately clear when you're learning data science topics why you're learning it and where it applies in the real world. And right now we don't really have authentic real world applications of mathematics. They're almost always oversimplified or really boring oversimplified or really boring. And so I think you know bringing data science into K-12 can widen the pool of who becomes interested in pursuing quantitative fields in college and beyond.

Brandon Langer:

So I can see that being kind of a challenge, though, because, based on how you described it, for us us it feels like you have to have these other skills, like need to be more solidified, before you can really approach data. Like, because you said mathematic statistics, you know coding and and what was the third?

Dr. Tanya LaMar:

I'm sorry substantive expertise, like understanding the context which.

Brandon Langer:

Which makes perfect sense, right? So it almost feels like you need a team to almost bring that trinity together. Your likelihood of somebody having all three of those all the time might vary. Is that one of the challenges of doing this in K-12 or in general, because you have to have that kind of broad scale skill set?

Dr. Tanya LaMar:

Yeah, so I should clarify my definition of data science is what does data science look like in industry? So the peak level of I'm a data scientist, that's what that definition is talking about. But when we're interweaving data science into K-12, of course we're not all going to be expert coders and it is a challenge. It's a challenge not only for finding teachers to teach data science, because it's a skill set that a teacher with a math credential probably doesn't know much coding and maybe vice versa. Maybe vice versa.

Dr. Tanya LaMar:

The way that data science curriculum looks in the younger grades leading up to the high school course is really it's a little bit more focused on data literacy, making sense of data, understanding the kinds of questions that you can ask of data, what questions cannot be answered by your data, and so we're not necessarily getting into the actual calculations, but increasing students' skill set in data analysis and data sense making. So on the YouCubed website, youcubedorg, there's a data science section where we have free materials that are for use in classrooms and we have them organized by grade bands so we have ways to engage with data science topics that are grade level appropriate and then when we get to the high school course is where we get into a little bit more of those skills around computer science, coding, statistics, but I would say that the overlap of those three spaces is something that makes it challenging and maybe even intimidating for a teacher to opt into teaching the course.

Brandon Langer:

I think it makes a lot of sense what you said in a more simple form building skills of data literacy. That is something that feels more not appropriate it's probably the wrong word but more able to be infused into, not just math courses, science courses, obviously, but I think social studies, I think a number of other topics could be infusing data literacy skills into their practice. So that makes a lot of sense data literacy skills into their practice.

Dr. Tanya LaMar:

So that makes a lot of sense. Oh, I was just gonna say one of the really fun ways that we have supported teachers to kind of just dip their toe is by doing data talks.

Dr. Tanya LaMar:

I don't know if you're familiar with a data talk, so you've heard of it, but please feel free to display a data visual, you know whether it's like from a newspaper or you know there's a lot of websites that make you know, fun data visuals, like data is beautiful or information is beautiful.

Dr. Tanya LaMar:

So displaying the data visual and asking students what do you notice, what do you wonder, and then, eventually, what story does this data tell? And then you can, you know, vary the complexity of that data visual, but that's a great way to get students flexing their data literacy. When you see a data visual, let's really take some time to study it. What is it trying to say, to say, and then we can kind of go a layer deeper of what might have been the motivation of the person that made this data visual. What message are they trying to send? And do we believe it based on the source? Did they even tell us the source of the data that they're using to make the visual? And so you can kind of start to be a critical consumer of data and that's something that you could implement into any subject areas.

Kirpa Chohan:

A data talk I like that math is not taught in isolation. It's something you're only doing in math class and then outside of math class you're not really going to use it. It's bringing real life things that you see around yourself. And yeah, talking to that point, I love slow reveal graphs because they do exactly that. They make them think, oh, what is happening, what is this going to be about? Do exactly that. They make them think, oh, what is happening, what is this going to be about? And then they make them think about data in such a different way and it's much more engaging for students as well I think that that's something that makes it's really powerful.

Dr. Tanya LaMar:

I mean not to like go for 20 minutes on data talks, but another thing that makes them so wonderful and so I studied the implementation of data talks as part of my dissertation and it allowed students who sometimes were a little bit less willing participants to engage right. There's no being wrong about a data talk. There's nothing wrong that you could notice or wonder. And so it really low. It makes for a low floor that all students can engage in this, and it's really about being curious, and so it's a great way to sort of grab all students to be interested in what we're about to talk about and for students to all have the opportunity to kind of shine, as you know, saying something of meaning or something that other people are going to build on my one final question was you have core in the ucube curriculum and um California data science curriculum, and my question is what are your hopes for the future?

Kirpa Chohan:

Like how do you see data science evolving and becoming part of math education?

Dr. Tanya LaMar:

So, first and foremost, I would like to see data science, like we've been discussing, woven throughout K-12, where it's not this sort of one-off special opportunity that only some students get Data science, like we said, it's a wonderful way to bring together multiple subject areas and we know how meaningful those cross-subject area experiences are for students. So I think data science poses a wonderful opportunity to bring together multiple subject areas and real-world skills across K-12. In the high school space, from my dissertation work, I know that students being engaged in a math class where why they're learning it is immediately clear, changes things for them. There were so many students at the beginning of the course that were like I am not going to major in anything having to do with math and I'm definitely not choosing a career around it, and at the end of the course that completely changed the course. That completely changed. It was all but one student that was saying I would like to go and pursue something at least quantitative, and the thing that changed for them is data science was the first time students saw themselves as capable of doing math, and so if we know that we have this coursework that's got the potential to shift students' beliefs about themselves and their own potential.

Dr. Tanya LaMar:

We absolutely need to do all that we can to get it into classrooms and, you know, and get that opportunity to students. So having data science being offered as a high school course and now part of the challenge around that is the space in a student's schedule, and so I think that that's kind of one of the biggest challenges that we face is, you know, there are more courses in the math sequence than there are years in high school, so that is sort of something that I've seen states and districts treat in different ways. I think one of the more successful ways is moving to an integrated curriculum, so an integrated one and two and then a data science course. We definitely need to spend our energy on solving because of the potential data science has for influencing students and their futures.

Brandon Langer:

One we. For those that are unfamiliar with integrated math, kip and I did a conversation with that that. That podcast actually launched late October, so you can go learn about that in two of our districts that are taking that integrated math approach. But to your point, tanya, I've been doing presentations for years, more on the marketing side, more on the communication side of my job.

Brandon Langer:

But the analogy I use in that presentation is that data is very similar to oil in the fact that it's very powerful, it's extremely valuable, but out of the ground, in its raw form, it's almost useless and it needs to go through this process of mining and refining and it's got to be packaged up and delivered in a commodity that someone can actually utilize.

Brandon Langer:

And in addition to that, it's also scattered you said this earlier everywhere. Much like oil, it's just everywhere and it's buried Unearthing. That quantifying its potential and making it meaningful is something that can be completely interdisciplinary. I mean like there is data everywhere, across every single topic that any teacher would hope to teach about. And I go back to one of the areas where it might seem like not so much, but I think about a social studies class how much more alive the stories of history could come through data and through teaching, you know, through data representation and analyses and data talks, as you said, seems like it's a no brainer and I could totally see how that is a direction that we need to go. I could also see some of the limitations and challenges you're talking about in terms of where teachers skill sets are and how comfortable people are engaging in the instruction through that manner and through that vehicle.

Dr. Tanya LaMar:

There's actually a really cool book, um. It's from it's web, du bois's. He made data visuals um, and so it's like this really incredible crossover and into you know history but also data and then seeing you know pretty old data visualizations. But anyway, it's just kind of a plug to go check those out. Yeah.

Brandon Langer:

Well, and also data can be really fun. I think that people miss that boat when they think it's just like numbers in a spreadsheet, it's like no, it can actually. It can be artistic, I mean, it can be something. That's beautiful when you take a look at it and get and really not change perspective to like try and change one's mind, but change perspective because you feel you have a fuller picture of what it is you're looking at and studying. I don't know. To wrap up here, you know what are the next steps for you on this journey of of make, of enacting this, bringing it to life and getting schools thinking this way.

Dr. Tanya LaMar:

Enacting this, bringing it to life and getting schools thinking this way. I think that the major effort right now well, there's two. One is policy. You know, addressing policies. It's typically at the state level to have data science be an acceptable high school course and so supporting states and districts in getting that approved. Joe had done quite a bit of work, kind of in the communication between K-12 and universities, so that universities also are on board that this is an acceptable high school math class and you're still going to be a competitive applicant to college with this on your transcript. So I think policy around data science is something we're still working on. But the Data Science for Everyone group and the Messy Data Coalition are two spaces where you've got people across the US meeting monthly and coordinating efforts around that. And the other is teacher education.

Dr. Tanya LaMar:

Like I said, it can be really overwhelming, I think, or intimidating for a teacher to take on something like teaching data science.

Dr. Tanya LaMar:

But the courses, all the courses that are designed like I'm familiar with almost all of them are designed in a way to support the teacher alongside the student, and I can speak to the U-Cubed curriculum specifically. It's written assuming the teacher doesn't have any data science experience and then supports the teacher in kind of learning alongside the student. So it does take, you know, putting on your growth mindset hat and being willing to problem solve and make sense alongside your students. But from witnessing a classroom where that happened, it was that much more powerful to the students to see that, to see their teacher problem solving alongside them, to see them actually being able to collaborate with one another and work through these units together. So I think, yes, it might feel intimidating at first to take on a course like this, but the vast majority of the curriculums are written with the assumption that the teacher is not a data scientist and are very carefully scaffolded to support not just the students but also the teacher.

Kirpa Chohan:

Yeah, I think that that is the big part of it. Right, we're bringing in and I know a lot of people are doing the work of policy changes, talking to college, asking them oh, can this be an admission requirement? Can we have this as one of our math pathways and count it as a rigorous class? So there's a lot of behind the work that's going on, because there is an importance to this, the way the world's changing. We need to bring this in. We need to make our kids stay illiterate.

Dr. Tanya LaMar:

Yeah, that is not going anywhere. So we support students in making sense of it and wielding its power.

Brandon Langer:

Yeah, Well and you not going anywhere. So we support students in building its power on, and that's going to drive us forward and hopefully drive solutions to problems that we have yet to be able to solve. I think you're spot on and I love, kirpa, this conversation, as you said, about thinking about math differently, thinking about how we make math meaningful for young people. Both of you said that today, so this was really enlightening. So thank you, tanya, for joining us today and sharing your background, your story, but also just these ideas and resources. I would look forward to diving into them and learning more.

Dr. Tanya LaMar:

Definitely yeah. Thank you so much for having me. I love the opportunity to talk data science.

Kirpa Chohan:

Thank you for being willing to talk to us about it Always.

Brandon Langer:

For those that haven't heard our podcast before, I mentioned we have quite a few kirpa's been bringing a lot of guests around mathematics this year, so if that's a topic of interest to you, be sure to look at our past conversations. Particularly that integrated math conversation that just went live in october was a good one. But this is the mcu learns podcast. We cover a lot of topics around education. We also have more information up on our learning network, learnmciuorg, so be sure to check that out for blogs and other podcasts coming out of the Office of Organizational Professional Learning and the Montgomery County Intermediate Unit. Be sure to follow us on all social networks at MCIU Learns and we'll see you on the next episode. Thank you both.

Kirpa Chohan:

Thank you.