Data Science for Everyone!

In this episode, Curtis and Joanie sit down with Mahmoud Harding from Data Science 4 Everyone (www.ds4e.com)  to explore the growing role of data science in K-12 education.

Mahmoud breaks down the key distinction between data science and data literacy — two terms that are often used interchangeably but carry very different meanings for educators and students alike. 

The conversation dives into why data science matters for all educators right now, regardless of subject area or grade level, and why the time to act is today. And taking action doesn’t mean you need math expertise or to steer away from the standards and curriculum your students need to know!

Mahmoud also shares practical, accessible ways teachers can get started with data-centered lessons in their classrooms — regardless of grade level or content area.

Whether you're a curious educator or ready to dive in, this episode will leave you inspired to bring data to life for your students.

Resources:

●      https://www.datascience4everyone.org/about   (DS4E Homepage)

●      https://www.datascience4everyone.org/resources  (DS4E Resources)

●      https://ds4e-org.github.io/CPN_rubric/   (DS4E Content Partner Network)

●      https://ds4e-org.github.io/technologytoolkit/   (DS4E Technology Tools for working with data)

●      https://datasciencelearning.org/ (K12 Data Science Learning Progressions) 

●      https://datasciencelearning.org/blog/five-basic-concepts-for-teachers-new-to-data-science  (DS4E Blog: Five basic concepts for teachers new to data science)

●      https://hkurzweil.github.io/ds4e-teacher-pd/frontmatter.html (DS4E Data Science Starter Kit)

Transcript

Room to Grow Podcast

Season 6 Episode 5: Mahmood Harding interview

00;00;02;00 - 00;00;27;18

Joanie intro

In today's episode of Room to Grow, Curtis and I speak with Mahmood Harding from Data Science for everyone about data science and its role in teaching and learning in our current technological age. Mahmoud helps us differentiate between data literacy and data science, and share some compelling evidence of why this topic is so important for all educators right now.

We learned so much and know you will too. So let's get growing.

00;00;29;21 - 00;00;52;24

Curtis

Well, Joni, I am really excited for today. I've been looking forward to this conversation for quite a while. We've had this guest on our list for quite a while, and we've finally been able to get everything oriented such that we can chat with him. And so I will turn it over to you. But I am so thrilled to have you here today.

00;00;52;27 - 00;01;58;09

Joanie

I’m really excited too Curtis. And I know this is particularly exciting for you because you brought this guest to my attention as well. And since then, I've had the opportunity to meet this person. Fantastic. Interesting. Great work happening. So we are very excited to welcome Mahmood Harding today from Data Science for everyone. So welcome Mahmood. We're so excited to talk with you. Our listeners are going to love this conversation.

Mahmood. When we have a guest, we typically ask them to start with an introduction to themselves, and we ask you to introduce yourself by telling us some of your personal and professional experiences that have brought you to the work that you do now.

00;01;28;15 - 00;02;57;16

Mahmood

All right. Thank you so much for having me.

00;01;30;10 - 00;01;53;23

Unknown

Really excited to have this conversation. Spoke to each one of you individually, and I think the three of us together are going to make for some interesting points, and hopefully we can bring some insight to your listeners about the world of data, data, science and all things, how they intersect with mathematics. So my background is from a mathematics standpoint. I started as an engineering major in college and did an internship and discovered that it wasn't very interesting to me to sit in factories and look at machines, make makeup. I didn't find it to be engaging. And when I went back to campus, I really know what I wanted to do. But I had taken enough math courses and computer science courses at that time.

In the late 80s, early 90s, computer science was largely around programing. And so my advisor suggested that I think about mathematics education as a major, and I had done tutoring for pre-calculus when I was a freshman. And so I decided to switch my major to mathematics education, and I took some additional courses to get a minor in computer science. I taught math straight out of college for a few years. The first job I got was because they needed someone who could teach Pascal programing. So you can see I’m dating myself as I’m telling my back story.

00;02;58;11 - 00;03;01;16

Joanie

We're right there with you. We're right there with you.

00;03;01;18 - 00;05;39;27

Mahmood

And so after teaching for a few years I made the jump into industry as a program and a systems analyst, but it was never as much fun as teaching.

I like playing with computers. The playing with them in a capacity for industry is not the same as playing with them. To learn how to use them, to then show another person to use it, to see their eyes light up and get that “Aha” moment. And so I took a break from industry, and I taught math abroad for 15 years.

And when I came back to the US, I started teaching at the North Carolina School of Science and Mathematics

And this is a place where I was able to experiment and really discover sort of my voice in constructing lessons for students to learn things about math using graphing calculators. I used a lot of GeoGebra at that time, Desmos spreadsheets. And so it was a very open space for creativity. And during that period, one of my colleagues, Taylor Gibson, had attended a conference in California about data science and when he came back, he was like, let's do a data science course, Mahmud. It's like, what is what is data science? And so that summer, we played around with the data eight curriculum out of UC Berkeley, went into the next academic year, and we taught that course to 11th graders. We made some tweaks and some changes, and our idea was to expose 11th grade students who had very little background or no background in statistics and no programing experience to programing, statistics, mathematics and data. And that really changed the way that I thought about creating relevant and engaging instructional context, using publicly available data and mathematics and computers. It was just a game changer.

And in 2022, at the National Council of Teachers of Mathematics conference in LA, I ended up meeting Eric Drozda, who is the executive director of data science for everyone, and I kind of hit it off. We just had this same sort of idea about bringing relevance, computation, engagement, excitement, critical thinking, open in the problem solving into the classroom. And in 2023, I started working at data science for everyone. And that's why I ever since.

00;05;39;29 - 00;07;05;19

Curtis

It's so exciting. That's so exciting. I got to meet you roundabout. I guess this that summer of 2022, right? When we met up at the Exeter conference and I took a data science course from you up there at the Andrea Greer conference for science and mathematics. And I remember that. And it was so exciting to learn a little bit about this concept of data science and, and really just this, these ideas that you had around getting students engaged in both

problem solving through programing, looking at data, thinking about those experiences.

And so we hear this term data science kind of kind of used a lot. It's maybe, I don't like the term buzzword, but it is kind of a buzzword in education, and, but we hear it a bunch kind of mixed in with some other things. And so we've got a question we'd love to ask your, your opinion, your kind of insight on this idea of data science and how it maybe is different from and maybe is similar to other terms that get mixed in with it data literacy, statistics, computer science.

I know that those kind of are components, maybe things that that maybe feed into that, but how is it different

to data science?

00;07;05;22 - 00;07;24;18

Mahmood

That's a that's a really good question. And I get asked this question often, as well as other people in our organization and we do a state of the field of K-12 data science education report each year. And my colleague who works in policy, Sean Suckle And the way he's defined data literacy and data science to me, really sort of emphasizes the differences, but also connects some of the things that are very common.

And if you think about data literacy, you want to develop a student's ability to read, interpret and analyze data. So that's that's very broad. And we believe that data literacy is something that fits across all subject areas. So within your humanities course, within your history course, within your science course, you have data that is organic to the content area and understanding how that data is collected and understanding how that data is analyzed is part of developing your skills in data literacy. And data literacy can also take the form of understanding things like fundamental summary statistics and being able to read a data visualization you can look at a graph, it make sense of it, you can draw some conclusions. Maybe you can explain some results. 

When you go into data science,

what you want to do now is become more involved in the analysis by using a variety of tools of a of techniques. You want to think about things like scientific methods, data sampling, probability. It's not just that you have to use a programing language. What we believe is that there should be tool flexibility.  I'm sure that you and Joanie, throughout your workweek use a variety of tools depending on the job that you’re doing.

00;09;06;21 - 00;09;07;24

Curtis

No joke.

00;09;08;01 - 00;10;28;15

Mahmood

 And so we want students to experience the critical thinking behind selecting the appropriate tool for the task at hand. And the other thing that I think for data science, that may be a little bit different from data literacy.

When you think about data literacy and you think about the component of ethics, governance and privacy, that may seem more like a humanities sort of strand. But in data science, throughout the course, you're going to look at at the collection, privacy, governance, you're going to try to verify your data. Maybe you want to look at the sources, maybe you have to clean it. So you require the use of a tool. Maybe you have to model it. So now you need more statistics or mathematics. So data science would be like another nudge above data literacy where you're a little bit more engaged in a variety of the aspects around data.

And that's that's kind of how I would differentiate the two.

End of Segment 1

00;10;19;28 - 00;10;28;03

Music break

Start of Segment 2

00;10;28;17 - 00;11;12;05

Curtis

The, the intermingling of all of those topics and then the idea that and I love that you brought up the scientific method. And just thinking about that, there's method and modeling and there's it's this expanded thing right beyond just the scope of, of statistics or beyond just the scope of the tool of computer programing or beyond just the scope of the humanities portion of thinking about ethics and governance and privacy and beyond the scope of data literacy, it's really all of those. And then edging yourself further, at least I'm trying to summarize what you just said

00;11;12;07 - 00;13;57;01

Mahmood

Oh no, you're exactly right. And I think another sort of critical part of thinking about data science and data literacy is sort of, if you have a systematic approach of working with data, one of the things that we anchor on is the data investigation process that was done by a group of researchers from NC state, led by Holly Lindley and Jema ? and Emily Thrasher. They have six components, and these components aren't necessarily linear, meaning you don't have to start at one every time and go through each one. It's iterative. And so maybe you didn't collect the data, but you need to analyze. Maybe you didn't process the data, but you need to visualize it. Maybe you need to frame a problem before you even think about collecting data. Maybe you have to think about how you're going to collect data and where you're going to collect it from. And at the end of it, after you iterate, you're going to want to communicate your results. And so now you're bringing back in another component that we value in education, which is being able to write, being able to speak and give up presentation and being able to engage in civil argumentation based on the evidence that you have. 

And if you can do this throughout all of the subject areas, you're developing the student holistically and getting them to understand things like iteration, process, communication, critical thinking. I may have a conjecture that I think is correct, but now I've collected more information, and now I have to reframe because I have new information. And these are things that we've always said that are very important for students.

 But sometimes in our traditional classrooms, we get bogged down by wanting to get the correct answer the first time. Well, data allows us a pathway into that open ended problem that is set up perfectly for iteration and set up perfectly for multiple ways to solve it in multiple solutions, and debate at the end to assess the validity of the solution. And so we think that the use of data as sort of this gateway into this process is extremely exciting  for all subject areas.

00;13;59;04 - 00;16;11;14

Joanie

It really is. It really is. I so appreciate the differentiation that you just made for us around data literacy versus data science. And I could see Kurt's expression, and he's getting as excited as I am about the way you're talking about data science and all. You know, you're I love the explanation of it as iterative, but I think I heard you refer to like the data literacy piece is still really, really important, right? Understanding how to read, interpret and analyze data is is at the heart of data science, but we want to take it to that deeper level. And what I heard you talking about were things like the durable skills. Right. Like the, you know, be making decisions about which tools to to use, making decisions about, do I need to clean this data?

What and how do I decide that. Right. Like there's a level of critical thinking that goes in there. And then there's the the communication. And how do I how do I analyze the validity of my results? And then how do I communicate to convince somebody else around the validity of my results. So it's it's just so much more than, you know, the standards or, you know, the items that we see assessed on standardized testing and that sort of thing.

So I love the the richness that we're talking about here and the way that how you describe data science as different from data literacy taps into the standards for mathematical practice. And, you know, all of the things that we're hearing in the math education space as what's important for students moving forward. And, you know, Mahmud, we wrote a couple of just basic guiding questions for our conversation today.

And I'm looking at this next question going, this is a silly question. Why is this important now? I mean, this of course, data is driving our worlds right now. But yeah, let's talk about why is this important right now. And and I actually would even love to hear more about data science for everyone and how that came to be and how that organization is connecting to how you just described what data science is and why it's important.

00;16;11;17 - 00;21;44;10

Mahmood

Well, I don't I don't think it's a silly question, because sometimes the conversations around the importance of data skills jump straight to career. And those conversations can lead people to think, well, we want to produce the next generation of data scientists, computer engineers, AI engineers and I think some students will go into those careers, you know, but if we want to really grounded in academia. So like grounded in K-12 academia, why is data science so important? So this follow me on this sort of trajectory. So in 2022, the National Assessment of Education Progress Nate the mathematics scores. So in 2019, from 2019 to 2022, there was a ten point drop in data analysis, statistics and probability 2019 The Nape score was 279 2022 it dropped to 269.

second largest decrease was measurement from 280 to 272, and the third largest was algebra from 289 to…So these numbers in and of themselves, they're just numbers, right?

What's the context behind it? Well, most experts believe that anything around a ten point drop is equivalent to a single grade level. Almost a year's worth of academic growth. Most people are shocked that data analysis, statistics and probability is the highest. Algebra doesn't shock people. The data analysis shocks people.

Why do we think the data analysis dropped the most? If you don't work with data in your other subject areas, you can't answer questions about data in context. Data has context. It is a human artifact. People collected it for a reason. They put it together in a certain way for a reason. So until we start having students think about data in all of their subject areas, there are data analysis skills left to just mathematics classrooms prevents them from having the rich experience of context in chemistry, biology, life science, environmental science, history, social studies, civics, economics, business. And the students in 2022. Those are the students in 2026 right now who are going off to college.

They don't have the necessary background in data analysis.

So then why is that important? Well, because a report that was done by the Federal Reserve or an article that was written the idea of college degree in major matching a third of students who graduate work in a job that doesn't require a degree, a third working a job that requires a college degree but not in a specific field, and one third working a job that's aligned with their college degree.

So two thirds of students after going to college may have to work in something that they didn't necessarily study in depth. What's the skill that they need to be able to thrive in those environments? Well, the skill that they need are rooted in data. And in the Burning Glass report, 23%. So Burning Glass Institute did a report where they scraped almost 200,000 job postings worldwide to get a sense of what kinds of skills employers were looking for. And they found that around 23% of job postings in the US are asking for at least one skill from the category of getting, exploring, and analyzing data. Almost a quarter. So our students need it in K-12. They can build on it in post-secondary, and then it becomes even more important in the job, when I say data skills sometimes people get,

their eyes flicker talking about programmers and machine learning experts. The Burning Glass report. CategoryThe 36 data skills across four main categories. Category number one specialized data skills.

There are 23, mostly around machine learning programing big data. Eight work common data skills that getting, exploring, analyzing data for or around statistics and mathematics. And number one. And in the last area was communicating the results. The Burning Glass Institute says communicating results is a data skill.

00;21;44;13 - 00;21;44;23

Joani

Yeah

00;21;45;00 - 00;21;45;19

Curtis

It is.

00;21;45;19 - 00;21;48;19

Mahmood

Story telling with data.

End of Segment 2

00;21;49;00 - 00;21;56;18

Music break

Start of Segment 3

00;21;56;29 - 00;24;39;24

Curtis / Joanie

I love just listening to you, first of all. And I mean, it's I'm for real. I'm sitting here just going, I can't even I can't even interject something right now because I'm just listening and going in. All right, thinking about what the point you are making and the points that you are making, just about the importance of data in these data skills as outlined here. That's that's shocking to me. First of all, just the drop. And I think you said this, right, that that algebra folks aren't too terribly surprised by algebra skills. And but that the data drop being the largest, that's kind of, a little surprising to me, honestly. That said, you make a really good point about data being in context, and it really requires us being able to think in context and analyze in context. And if we don't have that opportunity, it's it's challenging. You know, Joni and I and, and you teaching statistics courses had a lot of opportunity to do those kinds of things in context and, and work in our, in our classrooms, kind of that exploration in context being able to storytelling in context. As a part of our courses. But regular math courses and math teachers tend to be a lot of our audience, math teachers and administrators and coaches and folks in roles in math education. And so I'm wrestling with the idea. Okay. So so what do we do? Where do where do we go from here? Because, you know, we have we have nape scores that that come out and, you know, showcase, you know, declines in math, mathematics.

And that's a really big term mathematics,  you know. Right. There's, there's there's a lot that goes into that term when we say that that we've got these decrease in, in skill sets and, and in our scores and data being a big part of that.So where do we get started?  Because I know in my algebra class, I got 8 million standards that I have been put in front of me and my students and I, you know, I think about my eighth grader who is taking algebra as an eighth grader, and he's getting ready for high school, and he's trying to do some things and be ready to go.

But, you know, they're so focused on all these algebra skills and they got to get things done. What do we do?

Where, where can teachers get started? I mean, that that really is the question that's burning for me.

00;24;39;26 - 00;25;26;21

Mahmood

So I have three things that I would like to point out in response to that question. The first thing I think is we need to adopt and normalize the idea of data enabled instruction across all disciplines.

 So data enabled instruction means that throughout the course it doesn't have to be every day, but it can't be no day. You're using data as a opening to a core concept in your subject area.

Data can be images. It can be maps. It can be sound. It can be text, it can be tables.

00;25;26;24 - 00;26;58;28

Curtis / Joanie

I need to ask a question. So so what you're saying is that data doesn't because math teacher and I just in my head data equals numbers. And I want to I want to I want to I want to make that I want to take that off for a second because it's easy to glaze over that. I want to jump in because our our listeners know my younger son, Logan, is an elementary school music teacher. Right. So when when Mahmud saying all teachers need to use data, my brain goes right to him, like, okay, how does he do that? And I thought about he told me about an activity that he did in in March aligned with March Madness and football brackets and that sort of thing.

And he did something similar with music styles. So he would play like Monday was Classical Day and he would play these different, you know, eight different classical music, you know, snippets. And his students would vote and they would go up head to head against one another, and they would vote and get to a winner of classical music day. And then the next day it would be, you know, R&B. So he would take all these different musical genres and play the snippets of music. That's data. That's kids interacting with the data and analyzing the data. And it allowed him to talk about what's different in the same and what, you know, that analysis. So tell me if I'm way off my mood. But that's how I'm thinking about even an elementary school music teacher can do this.

00;26;59;01 - 00;28;42;23

Mahmood

You could put out a an assortment of instruments and have students write different characteristics about the instruments. Is this held in your hand? Does it have strings? Do you play it by blowing air into it?

Does it have vowels? Does it have a slide? And you can have them make sort of a classification model on an

someone and then have them go and create their own instrument in a drawing of an instrument. What kind of category does it fall in based on the rules that you define. Getting them to think about the idea of defining rules, thinking about looking at characteristics.

And it doesn't have to be many numbers in that it can be. Is the instrument long? Is it short? Do you play it sitting down? Do you play it standing up? All of these different things could be used. And the thing that you said, Joni, that, that, that I really appreciate is the creativity of the lesson fits so well with data.

And you can even have the students start thinking about the human side of data. Why did you say this instead of this? Why did you write down this trait and not this trait? Which one do you think is most important?

Which one do you think sounds the best? That's a very open. And now you're getting. Students are really grapple and think about things, and you're using something that most people may not even consider as data.

I think that's the gateway into the data enabled instruction.

00;28;42;23 - 00;29;49;17

Curtis

And that was the reason I wanted to stop us for a second there. When interrupt your, your stance because I, I, I heard you say music is data. Images are data. And I needed to unpack for a second, at least for my own brain and maybe some of our listeners too. But my just unpacking for a moment that that idea that many things go into the, the, the category data and something that I could start a classroom lesson with.

And it isn't just what immediately comes to my little brain, which is tables and graphs with little dots all over them, you know, bar graphs and bar charts and things like, I those are great data things and those, those have their own context and spaces to go. But I wanted to be able to allow for a second to, to pause there.

That that isn't the only thing that is data.

00;29;49;20 - 00;30;03;07

Mahmood

Oh, no. And so, in the beauty of the data enabled approach to your instruction is now you get a chance to naturally bring relevance into your instruction. So in your history class and I'm a bit of a history buff,

when you start thinking about sources, historical documents, how do historians use data from letters, bills, advertisements, magazines? How do they use that to get a sense of how society was at a particular time.

It's very different than how you may use measurements that you collect in chemistry. Doing an experiment in class,

Which may be very different from, say, in economics, how we measure things like the economy. So the data literacy part, using data as the gateway for data enabled instruction, really connects those two together. But it also brings you into this space where you haven't sidetracked your subject area. You’re trying to enhance your subject area. And that requires you to be very thoughtful. And it doesn't mean you bring in a table of numbers or just a sheet of paper with stuff written on it, just for the sake of doing it. It's no, you're a teacher. You're a professional. Be intentional and do it when it’s appropriate. The thing that I like to point out to people for the second thing, is a data enabled instructional activity doesn't have to be done in one period. You can start because the content you're learning fits the beginning of that cycle, but you get to a point where you're like, maybe we need to learn more science before we continue. Maybe we need to learn more math content. And when you learn that math content, you come back to that problem showing iteration. We thought this in January. Now we're in March. We know more math. Can we think of something different? And that’s how we all work. 

00;32;16;07 - 00;32;19;18

Joanie / Curtis

That is how learning happens, right? That is how learning happens.

00;32;23;08 - 00;32;37;20

Mahmood

And when you have information you can update.. Then you don't have to repeat going through and learning about a new data set all over. You can use the same data set and and deepen your content knowledge because you learn more things in the subject area. 

00;32;39;16 - 00;34;12;11

Curtis

You know, I love this. You know why I love this. I love this because I, I often when, when folks are making a point, I try to hear in my head whether I agree with them or not, I try to hear the, the bats. And listening to you, I feel so disarmed. My yeah but’s are all disarmed. And because. Because. No, listen, listen. Like the the question I wanted to follow up with was okay. Yeah, but how do I balance? But you did it right there. You said it was so cool because you said, okay, when we get to a point in our in our data exploration and I'm thinking as a math teacher, because I'm not a history teacher, you know, I can't can't do that.

So I'm going to I'm going to stick to my subject area. I'm thinking as a math teacher, we get to a point where we haven't learned that math yet, okay, we'll set our our exploration of the side for a second. We'll go off and we'll learn the math that we need to. And then in a couple of months, when we're ready to go. We'll come back to that data thing and pick it up and say, now. And I loved your disk. Like we thought this in January. Now what can we do different with the knowledge we have in March? Let's go. I'm so excited. I've got goosebumps. I'm telling you, I'm. You guys can't see it on the zoom, but I I've actually got goosebumps right here.

00;34;12;14 - 00;34;17;09

Joanie

I love that okay I want. I'm, I'm hanging on for number three. What's number three.

00;34;17;15 - 00;37;37;05

Mahmood

So number three. What what I would recommend that people start doing is we are not at data science for everyone. We recognize that to create these lessons that are data enabled across all subject areas is a lift. So when you're creating a data enable lesson is not quick right?

It's not. Sometimes it takes time. It takes thought. It takes iteration to get it right and to help the field. What we're doing is we're building a platform that has data enabled lessons across all subject areas, across grade levels. And the reason that we're doing it is because you and I, we all know that there are great teachers everywhere.

There's somebody right now teaching algebra in Iowa who's like a superstar. We just don't know who they are. How do we find these people? Well, we recruited some teachers to build up a set of lessons in biology, pre-algebra and social studies. We're going to test those lessons with teachers to get feedback, to see how well they work, will refine them, and then we'll open up this platform for others to use those lessons, but to also contribute.

And as we identify people who are great at doing this, we're going to give them space, an opportunity to do this and create for others. Sort of like the rising tide lifts all ships. And in the end, what we would hope is that we have a space where if I'm teaching mathematics and I want to talk about scientific notation in the seventh grade, then I can use that data lesson about the moons and Saturn to give context to scientific notation. So now I've talked about my standard in scientific notation. I've introduced some data. I've also brought in things like space and moons. And if I want to continue by comparing moons at one planet to moons and another planet to stars, then the teacher in science or astronomy can take it up from there. So as students go through class and class to class, they sort of see these things as being more connected.

And when that platform comes out and we advertise it, we hope that people use it and give us feedback, because the what we want to make sure is that inevitably, if it's not useful, then there's no need for it to exist. So we're designing it for use, and hopefully this can help teachers activate that data, enable gene inside of them, and start doing things on their own. But we give them a place where they can start.

00;37;37;07 - 00;38;00;16

Joanie

I do too. I'm sort of hearing the data science iterative cycle in your description of what you're actually doing. Right. We're going to collect these data. We're going to collect these lessons. And then we're going to measure how helpful they are and how effective they are. And then we're going to iterate. And we're going to do it again. And I love it. You're just doing data science on data science. It's perfect.

00;38;00;18 - 00;38;08;16

Curtis

And now there's a fractal.

00;38;08;19 - 00;40;17;09

Mahmood

just to be very clear about like, our stance and our approach and what we hope we're designing the platform so that it's tool agnostic, there are certain tools that are appropriate at certain grade levels for certain things. It is not going to be that every lesson requires you to write computer code. It may be lessons that don't even contain data, but are data literacy adjacent through the idea of governance or privacy.

It may be a lesson that talks about the history of AI, but from the perspective of data. So we're trying to make sure that resources we provide for teachers in their subject area resonates with their subject area. And to do this we started off working with the National Council of Social Studies teachers and working with them and trying to get an understanding of what they use for instruction and how they view data is shaping the way that the we design those lessons with the people that we've working with to to do that work for us. And the statement that I always like to say is that we like to be helpful, not just help. 

And being helpful means that you meet people where they are and understand their paradigm in their context, and then work with them to build something together. Help is I did it and I think is great. So now you take it.

That's not what we're doing. And we're trying to make sure that we have these partnerships across the subject areas to make sure that we're speaking the language of those teachers in that subject area, to make sure that we're giving them something that is useful.

00;40;17;18 - 00;41;07;29

Joanie

That is fantastic. I'm really excited. I know I'm speaking for Curtis because I can see it in his face. But for all of our listeners to I just want to say thank you to you and to all of the team at Data Science for everyone, because I think, you know, when I imagine what you're describing, if we can see that realized in all classrooms in our country, across all content area, if we can get kids engaging in this way and thinking in this way, man, I want to see those Nape scores. I think we were really going to see the difference that all of us are. The whole reason all three of us went into this work is for the outcomes that you all are really helping us make progress towards. So a big shout out and thank you to to you, Mahmood and everybody at Diaz, free for all the work you're doing.

00;41;08;01 - 00;41;25;14

Mahmood

And thank you again for having me and I appreciate it and we’ll cross paths again I’m sure. And we’ll continue our conversation in private and we’ll keep learning from one another and thank you for all the great work you’re doing to help math teachers across the country be better at teaching math.  

00;41;28;20 - 00;41;48;10

Joanie

Well, that's it for this time. Be sure to check the show notes for the resources we mentioned and others you might want to explore. We would love to hear your feedback and your suggestions for future topics. And if you're enjoying learning with us, consider leaving a review to help others find us and share the podcast with a fellow math educator.

See you next time!