Welcome to the PBL Simplified podcast for administrators brought to you by Magnify Learning, your customized PBL partner. From over a decade of experience with you in the trenches, we are bringing you this top rated educational podcast designed for visionary school administrators seeking to transform their schools with project-based learning. Launch your vision, live your why, and lead inspired. Here's your host, Ryan Steuer. Welcome to the PBL Simplified podcast. I'm your host Ryan Steuer with Magnify Learning. And if you were tuning in just for the first time, I want you to actually go to whatisp.com. Go to whatisp.com and you'll see a place to log in for administrators and you get a whole set of tools to get you started in launching your vision. Today is an episode where we bring in an expert guest to kind of maybe broaden that vision, maybe give you some new ideas, sometimes hone your craft. Today I think it's going to be to kind of broaden your vision and see how you can maybe draw some things out. So, I'm super excited for today's guest. Uh John Schwabish, an economist in training over the last 10 years. He's worked hard to communicate his research in better ways and to help others do the same. So, I I have to admit, John, I'm I'm a little I was a little selfish actually in this outreach because I think the data viz and you'll have to explain that to our audience and kind of what that is, what you do, but I'm super intrigued and I think there's a place for this in school. So, we'll get into it, but John, thanks for being here today. Appreciate it.
Yeah, Ryan, thanks so much for having me. Excited. Excited for it.
Hey, before we dive into kind of what it is you do, even we have to get into the why. So, every guest on the the podcast gets this question. So, what is your why for the work that you do?
Yeah, it's a great question. Uh, you know, you mentioned, uh, I'm an economist, so that's my background. Um, so I spent, uh, about 10 years working for the federal government and then the last 10 years working for a nonprofit. Uh, uh here in Washington DC and then have my own sort of side hustle on everybody has a side hustle these days. Yep. That's right.
Um so so I spent a lot of time in public policy and the sort of the public policy sphere and so for me for my core the core people that I work with nonprofits, researchers, government agencies, I mean my why is to help them do a better job communicating their data, their research, their analysis so that we can improve public policy so that we can improve you know the world around us, right? Um, I spent a good chunk of the early part of my career working in social security, but the last several years, especially since the pandemic, been working more in the nutrition space and the nutrition policy space, which includes a lot of how do we feed kids? I mean, this was I mean, I think this is a this is a key thing for for probably every one of your listeners in the education space, like, you know, during the height of the pandemic, how do we feed kids who especially kids from low-income families, how do we get them breakfast and lunch when they are not in the school system, right? So, like this is a huge problem. Um, and so we uh myself and a lot of my colleagues at at Urban, we spent a lot of time, you know, working with nonprofits on the ground doing evaluations, figuring out how, you know, how do you set up drop spots so people can stop by and pick up food for their kids, right? When we're all messed up and, you know, the very beginning when when we were all really scared. Um, and so some of that some of my work is on the evaluation side of things when doing the analysis, how do we evaluate it and how do we look more carefully and more closely and more strategically at the data? But then also, how do we provide feedback to the schools, back to the food banks, back to the nonprofit so that they know, for example, should they be setting up a a a food pickup or drop off point at this location rather than at this location? And there's a lot of ways that you can use visual analytics. Um, I think most people, the example I just gave would think of maps, but there's lots of others. But how do you sort of use use those v visual analytics to help people make the best decisions for themselves and their schools and and and their families and and their workplaces.
Yeah. So, great example. Maybe we can stay there, but whatever you want to go. So, technically your your group's called Policy Viz, right? So, you're into policy and the viz is to make it visual, right? So, I'm a data viz newbie. I would say I love spreadsheets and but I know that our our learner our listeners are going to be some of them don't like spreadsheets, some of them love spreadsheets. But I would say maybe their data is hidden in these spreadsheets and these databases. So I think that's where your work comes in. Can you give us kind of an overview or how you would help with data viz?
Yeah. So there's so there's a broad range of what data visual data visualization entails and what it sort of what you can do with it. So I think when a lot of people think of data visualization they either think of just Ryan like you mentioned it's like I have this spreadsheet and I kind of don't know where to start or they see stuff on like the New York Times or the Washington Post websites and they're like there's this great like thing that clicks and moves and filters and all this and like I don't know how to do any of that stuff, right? Like I don't know how to make that thing. And so I think a lot of people sort of get overwhelmed with what is obviously possible but they don't know how to do it. And I would say um that most people don't need to do that fancy thing with all the motion and the swoopiness and all the really cool stuff. I mean what we're trying to do with our data is to understand and try to answer a question. So if you think about you're a school administrator, you're a superintendent, you're a school principal, you have this big data set of say, you know, your faculty or your students. You don't necessarily need a big interactive thing. You just need to figure out a better way to understand those data. And and and a spreadsheet forces you uh as the reader, as the user to go essentially cell by cell to look at all those numbers to read all those numbers one at a time. And what visualization helps you do is get to the both prompting you to ask new questions, but also to get to the answer much more quickly. So, um I'm guessing a lot of the um folks listening to this show are using tools like Excel and Google Sheets. Um and there are other tools as well that I can I can share with you so folks can sort of poke around. Um there's nothing wrong with those tools. You know, if you get sort of deeper and deeper into the data viz world, people are like, "Oh, no, you should only use this programming language or this you know fancy thing and like you know I find that that most places don't actually need that right I have a lot of potential clients who come to me it's a nonprofit with six people right and they say can you help us build this like big website I'm like well what do you what's a question you're trying to answer
yeah there you go
well we want to be able to track how many people are going to the you know seven food banks that we're that we're managing across the city okay you just need a map and you need a bar chart you don't need fanciness right so there There are lots of ways even within relatively simpler tools like Excel, like Google Sheets where you can more quickly understand your data uh than I think many people sort of give those tools credit for. Um and then you can push the boundaries of those things where you can actually start to um get outside what's sort of in that standard dropown menu. And that's where you know requires a little bit of education, a little bit of you know uh uh practicing and trying and succeeding and failing. But I think at its core, what we're trying to do with data visualization is to get answers into people's hands more quickly. And what this whole the reason why graphs and charts and diagrams are so powerful is because we as human beings are visual creatures, right? We are more likely to recognize information and remember information later on if we see it more as visual content rather than as you know uh a table or a list of bullet points on a screen or slide. I mean, it's the reason why when you go in to a second grade classroom, uh, there are pictures and posters all over the wall, right? It's not it's not our slides that we we adults present to each other with a million bullet points and everything. It is it is images. It is maps. It is diagrams. And I I guess I I'll just say one other thing. In my experience working with educators, particularly sort of K12, there there seems to be this kind of gap. It's interesting because there seems to this kind of gap of well when I teach my fourth graders how to read a bar chart or a line chart or a histogram. Um you know there's not a human gene that is like the bar chart gene right the the way you have to teach your fourth graders it's the same way in some cases we have to teach other adults how to read other chart types not every adult knows how to read every chart type and so there is some education that that needs to happen so I often run into the case of okay so you know this group in this organization or in this school wants to provide this data to this other group and you know it tends to be like there's the analysis or the IT group and they're you know they're maybe more like data visualization literate than another group. It doesn't mean the that other group can't learn how to read
whatever graph you want. They just you know you just need to educate them. So it is so I love that this podcast is all about education because the education is not just adults to kids. It's also adults to adults.
Oh yeah 100%. Right. 100%. And we don't want to we also have a growth mindset on this podcast too, right? So we don't be like I don't do charts and
right and and I was a former engineer uh that became an English teacher. So I was always the I was in charge of the spreadsheet, right? Because I was surrounded by other Englishy folks, if you will, in quotes. It's like I I feel like you can do it. You you drag the the box. I think you can do it.
Yeah.
Um so there might be
it's a skill. It can be learned. Right.
Yeah.
And and and like you said, I mean a lot of the places that I work with, again, these smaller groups smaller nonprofits, it's the same story. There is like a person who's the data person because they demonstrated some affinity or some ability to make something and everyone's like, "Okay, you're now the data person and that person's like, whoa, whoa, whoa, whoa, whoa." And again, you just learn and you grow and experience and then successes and failures helps you, you know, become better at it. Um, but it is a learned skill that I mean, again, I'm not I'm an economist by training. I'm not a designer. Um,
okay. And so I don't approach my work from that sort of design perspective, which is a little bit different, right? Because from a designer's perspective, you are really trying to engage and bring people in and attract their attention. I'm coming at more from the data side. Um, and and both sides are both sides are are are very important. Um, my perspective is more on how do we make the data easier for people to see, find, and then utilize in their work.
Yeah. Which is a good point. And I don't know if I was listening to maybe a podcast you were on or or your book, but is somewhere in there you you were making the point that you also can't get stuck on one type of graph that you just love, right? Because it might depend on the data set and how you need to communicate that, right? If I'm using a bar graph to show where these drop off locations are for food and you're like, "No, dude, you just need a map."
Yeah.
Oh, I thought I needed Right.
Right. Yeah. I mean, and and and there's nothing wrong with using the tried and true. You know, I kind of always say like why does everybody know line bar pie chart, right? It's because and actually I would add to that interestingly line bar pie and histogram. I'm not sure why, but my kids uh who are now in high school like those are the graphs that they were taught in school and of course they come home to me and I'm like no no we're going to do a lot more. Um but there are a lot of graphic types out there and yeah sometimes sometimes those graphics are better at at showing the data, telling your story or making your argument. Sometimes those graphs are more engaging and engagement sort of depends on what your goals are and crucially who your audience is. Right? If you work for the New York Times, you want to get people on the on the website uh buying the newspaper, right? If you are an educator or in any organization and you're trying to work with your team to better understand your data, you don't really need to get people in the room. Like they kind of have to be in the room. So then it's It's a, you know, your goals are very different. So the first thing I tell people when I when I work with them or when I teach is identify your audience. Who is your audience? What do they need? Right? And I think for teachers in particular, this is fairly easy when you're in the classroom, right? You know, like you have a syllabus to follow, right? You have state guidelines, you have school guidelines, you know what you need to follow. And then when we talk to our peers as adults, we sort of forget that like, yeah, we have goal or we want our audience or reader to do something with the data and so it is the first step is to identify the audience what they need and I would also say the familiarity with the data uh the content and the visualization like I said not everybody knows how to read a scatter plot so you know Ryan is Ryan is the engineer now now uh English teacher maybe like scatter plots are second nature uh but maybe not to the rest of the department. And so in that case, you sort of need to educate people on how to read the graph before you can tell them, you know, what the story is or what the point is of the graph.
Yeah, that's a great point. And so let's let me give you another scenario because we have a ton of data in education.
Um it's not always great data, right? Sometimes it's a standardized test that the kids took seven months ago, right? And it's like what do we do with it now,
right?
We also have like some formative assessments where,
you know, we're testing kids, you know, at a a a program and they it spits out what they need right away, right? And we can give them
So, we got a ton of data. Sometimes we have too much data and but maybe that's not true. Maybe we just don't know how to use the data. Which what's your hunch on that? Do you think we have too much data or we don't know how to use it? Present it.
Well, it's it's funny because I sort of like split myself a little bit here like personalitywise because as an economist, I'm like more data is always better, right? Like the more you have like you just want to analyze the hell out of it and like I got it all so I'm going to use it.
Yeah.
But it does require more time and effort and thought into what is useful. Um I I think maybe so I have I have a story that maybe might be useful. Um so I worked with a community college uh over the summer and this is a community college that had a very high uh a very large percentage of their student body were Hispanic or Latino uh ethnicity and also very large share of their uh student body were commuters. And what they were finding was that amongst their Hispanic Latino students, the success rates, which they had a particular metric, I'm sure there's many people listening to this are like, I know exactly what they're what you're talking about, but
I kind of don't. But anyway, they have some success rate and they basically were making these big bar charts across all the different classes and the different types of majors and all this of the success rates. And what they were finding is that Hispanic, Latino students were were lagging behind all other student groups. And so what I set about doing was to say first off, what you're doing here, the way you're just looking at this, if you imagine this a big bar chart essentially with either the the faculty, the teachers name alphabetical or the class number sort of sorted. I said sort there there's two things to consider here. One is the way you're looking at the data saying there's something about Hispanic students that make them sort of lag behind other students, right? Because you're just looking at the percentage of the success rate and you're not looking deeper. So why don't we instead of just sorting it alphabetically or by course number, why don't we look at some other attributes? Because what they also had, what the school also had was um they knew certain attributes of each of the each of the educators, each of the professors, right? So they knew some professors were providing uh Spanish uh translation services. some of the professors were providing long, you know, sort of extending the time for assignments to be handed in. So, what we did was sort of instead of one big bar chart, we just broke it up into smaller multiple chunks. Um, and so we looked at, okay, let's break it down by professor strategy and homework. Let's break it down by percent of students who are taking up translation services. Uh, let's break it down by other things the the instructors or professors were doing to provide services for um commuting students for students who had children at home. We sort of just looked at by demographic and what you saw pretty quickly once you took this big data set and broke it down not just by what the raw success rate is but by this second variable you saw that and I've sort of already set this up not going to surprise you professors that were giving more access to trans uh translation services English translation or I guess English Spanish translation and and professors that were giving uh longer time or more ability or more flexibility I guess on assignments those Hispanic Latino students in those classes were doing better right so sort of looking a little bit deeper into the data beyond just this is a raw 47% you know where why is that why is that where is that number coming from and it's not just maybe a student's you know ability or not or some attribute of the person uh Maybe there's a broader institution, broader structures around it to think about. And again, this is pretty simple. We still ended up with bar charts
instead of one big bar chart, right? We had like, you know, we had like a dozen bar charts. And once you see that, then you start to see very different stories.
Yeah. So, I think there's a great see if this lesson's a decent one to pull out is, you know, we we get some data. We say, "Hey, here's this marked change that we need to pay attention to."
Yeah.
Then we stop and we guess at what we should do. And but right maybe next step is to you know a five wise protocol or some kind or but at least go one or two levels deeper
data wise before we start to bring in solutions
right I mean if you are if if you are the expert I don't even say expert data only can get you so far data can only do so much and they can only get you so far and the data that we collect have lots of things circling around them right lots of bias in how we collect them and who answers and for for you know especially for education you know a student might just be having a bad day or there was a fire alarm right and so yeah maybe you know you could say oh all the grades in this class on this day were way lower and say oh it must be the professor's fault or the teacher's fault or the students are just not that smart in that class but oh wait that was a day where it was you know it was pouring rain and everybody showed up late to class right so like
you know as I guess I would say this um sort of maybe sort of pull back a little bit. There is um if you think of like quantitative data and qualitative data, there is often a bias against qualitative data. But what we get from the qualitative stuff is people's stories and people's experiences, right? There's um a great book um on education data uh called street data. And the whole thing about street data is you learn about and it's learning about students experiences, right? Understanding that if a student is living in their car or their family is is is unhoused, that student's experience, their potential performance is going may be worse than other students, not because they can't do the work,
but because they're spending most of their day out of school trying to find some place to sleep that night, right? So, like
you get to know that as especially educators get to know that because they work with their work with their students and that doesn't show up in the raw scores. Um, and so I think that is a a hugely important part of of working with data and then how do you incorporate that into your you know ultimately to what what I do a lot which is incorporating that into the data viz itself.
Yeah. And so I like to go another direction too. Sometimes we have schools that suddenly perform really well one year they're like wow our stand because standardized tests are they're not important. We don't teach to those when they're going poorly and then suddenly you get like oh great
we're all about this.
But then the question is well what did you do differently?
And a lot of times they don't really have any data to show this is what we did differently, right? So again, we're guessing again.
Yeah.
So data to identify problems, but also data to identify solutions, right? Or like what we're doing well. Is there a difference there or
I mean I don't know if there's a difference in terms of the data analysis part. I guess that the from a visualization perspective, I guess what we could what what I tend to talk to people about is when you are looking at some data series and you're looking at some break kind of what we're getting as some break in the series, right? Yeah. Your your school's performance was blah bl was middling and then all of a sudden it's really good. Okay. Yeah. That may be great and you're super h happy about that.
Um
and a lot of people think like, oh, if I just winnow my data all the way down to the one, two, three things I want to look at, then like that's enough. But another way to think about it is let's include, this is kind of back to your earlier question about more data or less data. Let's include more data on that chart. So, for example, let's think about uh standardized tests over the last decade. And you might look at one school or two schools. And you have this line chart and you're like, "Okay, here's these two lines." But I could I could put, you know, lines for all the schools in the state. So, I've got thousands of lines on this graph. And if I told you I have a graph with thousands of lines, you'd be like, "Oh man, that sounds like a terrible graph." But the way I would create my graph with thousands of lines is I might make, you know, n 995 of those lines, a thin light gray line. And then the five schools that I want to look at in my district, they're darker, they're thicker, they're on top. So what I see is,
yeah, the five schools in my district spiked up and doing really well now, but so did all the other schools in the state, right? And so I see this more relative story. So I think one thing that happens a lot when people work with data and try to visualize it is they say, "I need to win. down to the one, two, three things. And it's not necessarily about that. It's about understanding how we can use visual elements such as color, such as say the thickness of a line, such as um you know maybe drawing a circle or a box around a particular thing to direct people's attention to the thing that's most important. So um so it's not so much about kind of the size of the data or complexity of the data, it's about in some ways looking at the relative data and then understanding kind of to the core of your question. Okay, so we had this big spike upward. Did we do something in our school? And that we I think this is the other part about the quantitative versus qualitative. The Wii could be we made a new teachers lounge that made the experience for teachers better and they just productivity and they're just better teachers now because they feel more comfortable at the school. I mean it can be some it doesn't have to be like you know, our teachers spent 80 hours at the school instead of what they do now, which is 78 hours at the school, right? Like, um, yeah. So, so I I guess there's a lot of answers in there, but, um, I would say the qualitative is can be, if not as important, more important than sort of quantitative, you know, you know, hard data that we get.
Yeah. Wow. That's that's a great nugget to pull out. Um, because I do think qualitative data gets a bad rap if you will right and
100%.
But but that makes a lot of sense.
I was just going to say it also gets a bad rap in the qualitative in the data visualization world because I think in in in my experience working with researchers, right? The qualitative researchers I know which tend to be kind of the sociologists, political scientists, they're a little frustrated when it comes to visualizing data because they're like, well, I can't just make a bar chart, right? Like I've got transcripts from focus groups and from interviews and it's like I can't just like make a bar chart. Whereas if you're a quantitative person, you can just aggregate be done. Um, but there are ways to visualize qualitative data which we we don't need to get into. But I'll just say the advantage, one of the big advantages of qualitative work is that you get people's stories, you get people's experiences, and you get the richness, and that's the stuff that grabs people's attention, right? That's the stuff that we care about, right? Like, so so to your your point earlier about standardized tests, you might see that, you know, the standardized test went, you know, jumped up or jumped down and I could look at that line chart and say, "Okay, yeah, fine." But then if you tell me the reason our test scores went up is because you know our school social workers, we added a social worker and we're able to address the mental health crisis coming out of the pandemic like and and you know we worked with students ABC and D and did this thing and that's what really inspired us to do better. That's when people start to care.
Yeah. And maybe we can go there with a qualitative visual piece if we if we can. So we've got a school that we work with and their attendance went up 10 to 12%. Right? So that's a huge number, right? And for a school district and it's it's almost exactly what you said. They actually hired an attendance counselor specifically to go, hey, you know, you haven't been at school for 20 days. What's going on? And you know,
she gets all the all the spiel of what the family's going through. And they did a great job of getting interviews of the kids,
right? Having this new attendance counselor, you know, really figure out like they crunched some of the data but a lot of it was qualitative interviews right with kids and testimonials from kids
and it was super powerful
and then I feel like if they went to write a grant you know to reup this position
like well where's the data like
yeah
I think it was on the news right like the data was on the news so how do schools capture that you know if they're not on the news
yeah yeah I mean I think I so so I think there's there's a lot of ways to sort of try to capture that I mean I think the impact measuring impact is really tricky, right? I mean everybody sort of wants to measure impact and one thing that that um in my role in the communications department at at the urban institute we talk about with our researchers is is your goal to get 5,000 clicks on the page or is it to get the right five people clicking on the page?
Right? So what what is what is your goal? We and we want impact, right? So if you can reach the members of the city council of whatever city you're working in and can convince them. That's a bigger impact than having 5,000 people read your, you know, average people reading your your blog post. Um, so, so what works really well? I mean, obviously, um, those stories that you can pick out, those, you know, the the sort of standard way that qualitative work is done is, you know, quote quotes. And there's nothing wrong with quotes because you really can get that story. Um, there are other ways to sort of uh visualize qualitative data um where you can show trends in words or sentiment and I'll just say um and this is not my field so I won't say too much but like
the artificial intelligence tools are in in inside existing qualitative tools I will say so like not just chat GPT but there are like qualitative data tools out there they're getting so much more powerful that they are getting better and better at picking out sentiment from your transcript or for from your focus group and once you get that, you're better able to sort of winnow in and see, okay, this is the theme that we can literally see in the words that people have used over the last four semesters. And so we can actually create a graph using those actual words or we could use um we can use uh you know certain types of graphs where we can actually um show uh the relationship of how words are being used within phrasing, right? And so so The tools are getting better and better. Again, not my certainly not I'm certainly not an expert in this in this area, but there are tools like Envivo and Deduce and Atlas.ai. Um, all of which are enabling people to work with qualitative data in more sophisticated and better ways that I think what we will see in the next few years is the data visualizations coming out of those tools are going to be even better.
Yeah, I love that. So, But we're going to we're going to come to a wrap here pretty soon. Um want people to know where they can how they can reach out and how they can kind of magnify your work and get get maybe a copy of your book and if they really one if they're like yes I want more of this or two if they're like I don't know how to do this both of those groups should reach out. Right. So yes u but we've got busy principles listening
and sometimes all they can do is throw up the data up on a screen.
Yeah.
Um hopefully we've convinced them that you need to take the time it's worth it to take the time, create some kind of a visual so that you can actually like bring a goal to that.
Can you talk to that principle a little bit? Like what's that first step maybe or maybe even just reiterate the importance of
get get away from the giant spreadsheet that most of your teachers don't understand?
Yeah.
And get into some kind of data visual visualization. Where do they start?
So I would start again by knowing who's your audience and also knowing wh what your platform is. So I'll speak as a uh not as an educator but as a parent, right? When I go to these parent meetings at the school, right? I'm sitting in this classroom and there's this projector screen where the text is like a 12point font, right? And I can't read that. I can't see that. Now, maybe the kids can when they're in school cuz they have young people eyes. I don't know. Um but like I would think about, you know, even if you want to have this detailed information, you want to provide it to parents, how can you get it to them in a better way? So, I'll just I'll just give like a very quick example instead of or strategy. Instead of having, for example, five bullet points up on one screen on one slide, think about just using five slides separately and just making the text a lot bigger or replacing the text with an image, right? And and then and then have people focus on you as the as the speaker
for for the sort of scenario that you you suggested. Um, again, like I think everybody sort of knows how to read a bar chart, but if you're going to go a little bit further, you're going to use a graph that maybe they're not famili amiliar with or there's going to be the data is going to be sort of complex or or you need to walk people through walk them through it like I think this is a thing that we all forget as we get up in front of an audience start talking is that we forget that we are the expert or we have all this experience we've been working with the data for days or weeks or months and we just get up there and start you know start going at it and instead literally walk people through what you want them to show. So that might mean if you're showing a bar chart let's just say of success rates across your, you know, 10 schools in your district. You know, maybe you just show the bar chart, the first bar for one school, and you explain, okay, this is what we're going to look at. You can see that uh, you know, MLAN High School, which is where my kids go in in Northern Virginia, and MLAN High School, the success rate is X%. Now, we're going to look at, you know, Falls Church High School. Uh, and now we're going to look at Arlington High School. And so, you don't just have to throw everything up in one slide. Uh, computer memory is super cheap. Uh, so don't worry. having more slides, like it's okay to have more slides. Um, and just walk people through through your argument one step at a time. And then to your point about sort of the next step, if you're sharing data, you're sharing visualizations. Um, figure out what your story is, what your argument is, what your point is, instead of just doing what we do to each other all the time, which is here's a table, here's a graph, go figure it out. You're on your own. I'm making an argument. I'm I'm trying to tell you this is why our scores have gone up. and and here's when you can see it and here's why I'm going to tell you that story in the graph um and not just let you sort of try to figure out what my point is, right? Most of us when we make charts and graphs, we're making an argument. And so let's let's do that rather than forcing people just guess what the argument is.
