 Hello everyone and thank you for joining us at this Wednesday webinar from the Public Interest Technology University Network at New America. Today we're going to talk about defining data literacy for first year college students. My name is Alberto Rodriguez, I'm the Senior Program Manager at the New America team in the Pitt UN. And this conversation about the Pitt Initiative, panelists from Georgia State University and other inter-university partners will provide an overview of her program goals and gather feedback from panelists and you as to what constitutes data literacy for the public good. To get it started, let me introduce you to Mandy Swigat-Hoba, team leader for research data services and co-director of the Pitt Initiative at Georgia State University. Mandy, it's all yours. Thank you Alberto and we're going to have a couple slides come up here so I'm going to wait till the screen gets shared before we begin, but welcome. Yes, thank you. Thank you Nomada. All right, so welcome everyone to this webinar. We're so glad you came. Before we get to the actual panel focused on data literacy for first year college students, we'd like to give a little bit of background on how we got to PIDLIT. So PIDLIT stands for Public Interest Data Literacy and this is our newest initiative. As Alberto already said, I'm Mandy Swigat-Hoba. I'm one of the co-directors of PIDLIT and I'm the leader of the research data services team at Georgia State University Library. So I'm going to talk a little bit about that team in a moment and then I'm going to turn it over to my colleague, Brian Sinclair, who is also co-director of PIDLIT and is associate dean for public services at the Georgia State University Library. Next slide, please. So PIDLIT, this initiative, oops, can we go back one? We just skip one. Thank you. So the initiative is generously funded by New America as part of its Public Interest Technology University Network, or PITUN, for short, of which our University, Georgia State University is a member. So to tell you a little bit about PITUN, if you are not familiar with it, they foster collaboration between universities and colleges that are committed to the field of public interest technology and growing a new generation of civic-minded technologists. So through the development of curricula, which is kind of our initiative focus, research agendas and experiential learning programs in this public interest technology space, PITUN universities are trying innovative tactics to produce graduates with these multiple fluencies at the intersection of technology and policy and with the aim of using these fluencies for the public good. Next slide, please. So the Georgia State University Library's Research Day Service team, of which I am lead, was formed in 2016 in response to a perceived gap in cross-campus data services support. Next slide, please. The Research Data Services team offers data support services across the entire research lifecycle, as we call it. So some of the areas that we support are including finding existing data and statistics, original data collection, data analysis tools and methods, mapping and data visualization, and data management. Next slide, please. So this slide view, it looks like it got a little messed up in the view, but that's okay. It was supposed to be an easier view of the support areas and specific services that we offer. The previous slide had a PDF flyer that was tiny and hard to see, but generally speaking, we wanted to highlight that of particular popularity is our support for quantitative and qualitative data analysis and visualizations tools, including proprietary software such as SPSS for statistical analysis and Vivo for qualitative analysis, Tableau for data visualization, but also open source tools like R and Python. Next slide, please. So generally speaking, the way our support kind of takes form is we have open workshops that anyone can attend. We also do course embedded sessions. We have custom sessions with research teams, and we do one-on-one or group consultations. Next slide, please. So this slide has a lot of information on it, but basically it's information about kind of breakdown of students using our data support services, graduates versus undergraduates, and if you look to the left, you'll see that we have no problem reaching graduate students as evidenced by this data on data consultations, workshop attendance, and course embedded sessions. And this is not surprising given that graduate students and their faculty recognize this immediate need that graduate students have for data literacy skills, because they have feces, they have dissertations that have to complete. But if you look to the right-hand side, the undergraduate data on our data support services and the use by that group, you can see that we have a lower representation of undergraduates, and we would like to increase undergraduate interest. So this goal of just wanting to increase undergraduate interest in our data services in part has put us on this path to piddlet, because we want to get undergraduates engaged in developing more data literacy skills and particularly helping them recognize the value of these skills for their career marketability. Next slide, and I'll turn it over to Brian at this point as well. So thank you, Mandy. This is Brian Sinclair, the co-director of piddlet and associate dean for public services again at Georgia State University Library, and we're very pleased to have you all here today to learn. So I'm going to continue you on your journey to piddlet. We do these things that Mandy has described because we seek to empower students to be better researchers and critical thinkers to be successful in their academic pursuits and in their later careers. Our efforts around data literacy and research data services are aligned with a broader campus initiative called College to Career. A College to Career is a campus-wide effort that encourages students to recognize the career competencies they are gaining through curricular and co-curricular activities and to both document and demonstrate those competencies before they graduate. Ultimately, the idea is to expand students' career horizons and make them more marketable to future employers. Next slide, please. So this got cut off a little bit, but no problem. The outcomes are cut off there, but no worries. I just mentioned that students are to become aware, including in the first year of their college career, to make those connections between co-curricular activities and what they're learning and then ultimately demonstrate what they can do. And on the right column, if they had been there, it's fine. The competencies are listed and one of the chief among them, I just mentioned it, is critical thinking and in that competency, working with data, visualizing data, analyzing data, understanding data is a critical part of that and we see ourselves as aligning with that. Next slide, please. So another way we are increasing awareness of data careers and helping to build connections between students and our local community is through this data in the ATL speaker series. We've been doing it for four years now. We'll be starting our fourth year. It's hosted by the library and we bring in professionals from the community and the objectives are right there on the right. Introduce students to possible careers of data science, increase those connections, promote our efforts in supporting research and data and to just share knowledge and get to know each other. Next slide, please. So we are fortunate to be based in Atlanta and downtown Atlanta actually, home to many Fortune 500 companies, entertainment news organizations, and major nonprofit governmental and health organizations. The American Cancer Society is our neighbor here in downtown Atlanta and of course the CDC is here. Next slide. We've welcomed numerous data scientists, data analysts and other professionals from the Atlanta area. If the columns are showing it would show all the nonprofits we've welcomed into the campus and those have included major educational, nonprofit, data analysts and the whole idea is to introduce students to careers that might be in the nonprofit sector as well. Next slide, please. So here's just a few examples before I wrap up with this. This is Mike Carnathan from the Atlanta Regional Commission, our regional planning agency for Metro Atlanta. Through their research and his research in data analytics, they inform various intergovernmental partners on things like new transportation options, developing healthy livable communities, managing water resources, providing services for older adults and individuals with disabilities. This speaker series with Mike was a part of is very popular. Students are very interested in learning how they can apply what they're learning in the classroom around data in this case or around making it, making a map or GIS, two possible career options that may not be, they may not be aware of. So next slide, please. And this is Hannah Goldberg, director of research for the Georgia Early Education Alliance for Ready Students. Her team is developing a suite of online tools that empower citizens and decision makers to explore data around early childhood and school readiness. They've done some interesting things with interactive maps that show demanding gaps related to childcare in our metro area. Again, very well attended, not only by our College of Education students, but from students from various disciplines who are interested in learning, again, how can they apply what they're learning to real life or outside of college work? So next slide. And students were able to learn a network with data professionals from different backgrounds in a wide array of contexts and in different settings. This is Thomas Price, data strategist for the Atlanta Public Schools who spoke with us about his work with data visualization and statistical modeling for our local school district. Next slide. So the data literacy and skills training we provide through the library's research data services, plus the data in the ATL speaker series connecting students with real world application of data science, plus our alignment with the efforts of GSU's College to Career Initiative and our university's overarching commitment to preparing students for careers that can make a difference in the world is what brought us to PIDLIT today. We are committed to preparing thoughtful, critical thinking students for possible data careers by expanding programs to reach our first year students. We hope to encourage a career pipeline that is stronger and more diverse in terms of race, ethnicity, ability, gender, and socioeconomic status. And that's where you, our attendees today and our panelists come in. Our panelists from GSU, NC State, University of Cincinnati will help us today as we gather feedback from you all as what constitutes data literacy for the public good. Next slide, please. And before we begin, I'd like to recognize a few of our other partners and collaborators thus far, in addition to Pitt UN and New America. They're listed here today, and I believe we have representation from NC State and UC Libraries today, and we'll be doing more workshops and webinars with our other partners as well. Next slide. And I'd like to introduce our host for today's main event, Ashley Rockwell, our inaugural Pitt LIT fellow. Ashley, let me tell you a little bit about Ashley before she gets to the questions for the panel, but she's originally from Washington State. She has undergraduate degrees in neuroscience and psychology from Washington State University. From there, she made her way to Atlanta, where she earned an MA in Sociology and is presently completing her PhD in Sociology here at Georgia State. Her background includes working in public radio, among other things, which we think may be helpful to us as we work to promote Pitt LIT. Her research interests are wide and varied. She has interest in gender and work, gender in the media, empathy, race, gender and class inequality, social problems, and community and urban sociology, just to name a few. Next slide. So, I will turn it over to Ashley and thank you all for attending today and thank you so much for our panelists and our colleagues at New America and Pitt UN. We really appreciate it. Thank you, Brian. And thank you to everyone who's joined us today. I want to start out with a chance to give our panelists the opportunity to briefly introduce themselves. And so, I'm going to go ahead and start in alphabetical order by last name, but can starting with Akansha, then Tiffany, Natalia and Chad, can you tell me about your position and how or why you got into data? Hi, everyone. Thank you for joining us today. My name is Akansha Angra and I am an academic professional in the Department of Biological Sciences at Georgia State University. And I got into data and data literacy because of my graduate school work. And, you know, I sort of started with when I was a undergraduate TA and I was teaching a biology laboratory course and I realized that the students really struggled with making graphs and really thinking deeply about their data. And so, I went on to graduate school to study this exact topic, how undergraduate students, graduate students and expert professors think about data and how they create graphs on pen and paper modality. Hi, everyone. My name is Tiffany Grant. I work at the Health Sciences Library of the University of Cincinnati as the Assistant Director for Research and Informatics. I'm in the libraries, but I have to say that I'm not a typical or traditional librarian. My background is molecular biology, infectious diseases. Mainly, a lot of that came about because of my interest. I came of age during the HIV AIDS time in the 80s and 90s. And so, which actually led me to pursue a doctorate in pathobiology and molecular medicine. And so, I worked on that in a microbiology lab and went on to do a couple of postdocs and also worked as a microbiologist. I came full circle back to the University of Cincinnati, which is where I got my doctorate, to work as what was called at that point a research informationist. The idea behind that was that they needed someone who would be able to translate the needs of the biomedical professionals within our academic health center into services that the library could offer. And so, in doing that, for a couple of years, I actually became the Assistant Director where I now co-lead our research and data services. The services that we offer are very similar to what you heard Mandy mention earlier. And so, we do a lot of things that basically covers the entire gamut of the researcher data life cycle from creating data management plans as individuals are applying for grant funding from different federal funding agencies all the way down to or up to everyone look at it to archiving the information for further and future use. And so, that's in large part how I came into the data sphere all set. My name is Natalia Lopez. I am lead librarian for data instruction at North Carolina State University based in Raleigh. And so prior to actually becoming a librarian, I was a nonprofit professional in New York City. I worked in a number of organizations primarily focused on immigrant rights and education. And so, it was during that time, honestly, that I kind of had to manage a lot of data and sort of collect a lot of data. And found that I had a knack for it and also interest in it. It was really appealing to me to figure out how to sort of work on on sort of tech problems and issues that really helped ultimately like the work we were doing. And so I when I went to library school, I ended up working at UNC Chapel Hills Davis Libraries Research Hub, which is focused on data services. And I really got sort of this like, I was like immersed in all these like methods and tools that I suddenly had learned. And I was so excited by this the potential of it. And thinking back to like my former colleagues and sort of what what would have happened if I had been introduced to all these tools and methods when I was an undergrad. And that really just kind of what became sort of a passion and how I then approached like librarianship. Greetings, everyone. My name is Chad Marchong. I am the assistant director of learning analytics in the Center for Excellence Teaching and Learning and Online Education at Seatlow at Georgia State University. So just a quick just to get a little bit personal. When I was a younger in high school, junior high school and things like that, I was a fairly decent math student. And I was really excited about attending some math classes sounds like there's a lot of passion without our panelists around data and things like that. Just to kind of quickly touch on some of the experiences that I had when I was a bit younger was that while I was I was good at math, I was constantly being asked by my classmates to help them with some of the math subjects and help them tutor and things like that. But I was always frustrated by being able to transfer some of that knowledge and tutor some of my my classmates. So ever since then, I always felt like I needed to become better at supporting my classmates and learners and things like that around around math. And this is kind of you know, what I'm so passionate and excited about with this pit lead initiative is where we do start to bring in some of those ideas and get some of these students, you know, changing their mindset around some of the math and, you know, stem subject matter areas. But when I went to college, I decided to major in computer science. There's still a lot of math classes there. There was still a lot of abilities for me to to use analytical skills and logical skills and things like that. After graduating college, I had numerous jobs before I eventually started into it. And I've been at Georgia State University for 10 years now had a number of it related positions since starting here and mostly around learning technology. And more recently, about three years ago, starting off with working without learning analytics team and has been with this team ever since then. This team was was was was born or conceived from some of the tremendous work that Georgia State has been doing around student success and the use of data and just kind of extending on that with this learning analytics team. And so our team supports and works with our faculty on using some of the data that's coming out about teaching and learning systems. As you probably know, we're we're a lot online now and there's a lot of data that's coming out of some of our teaching learning systems. So we're using that to help and provide services for our faculty. So that way they can take a look at some of the data and see where they can support our students. Thank you. I want to start out with before before we can really get into defining what data literacy is. Data itself is just is a very broad and encompassing term. And like Chad mentioned, data can when some people think of data, they think of just math, you know, they think of numbers that can be it can be a quantitative aspect of data, we can be doing things like doing exact measurements in a bio or chemical lab, or it can be something like observation. So we can be observing folks, how they interact in school or how they interact in a mall or how people are interacting on Zoom and how that's changed. And so we can observe those things. And that's a form of data as well. Or how I constantly have to remind my students is that they are constantly creating data themselves through their tweets or Instagram posts. And when we think about how we analyze and visualize and interpret data, that also changes our definition of how we define data. But no matter what, if it's, you know, quantitative data, and we're focusing on trying to answer questions about what, when, where, how often, or qualitative data that allows us to get at more of the how and why questions. In short, I think that we can define data as information, as bits of information, some some bits more complicated than other bits. But thinking of data as, as information, and including that really broad, encompassing way we define data and how we analyze and visualize and interpret data. I want to start a discussion by asking Akansha and then Natalia, and our audience as well. So if you would like to join us in this conversation, feel free to leave your comments and or questions in the Q&A. And they'll get that that way they can be directed to me. But starting with Akansha and Natalia, what do you think it means to be data literate? And what type of data literacy skills should students have? Specifically, what do you think are the key components for data literacy for those first year students? Thanks Ashley. So I think being data literate means understanding, creating, critiquing, and communicating with data. And to me, it also means being willing to be curious and brave and asking questions to truly understand the underlying meaning of a data visualization or claims that are being made by the authors. And data is a powerful tool. So it's important to know when to use data to make decisions and communicate the meaning with others. And in terms of the data literacy skills that I think students should have, definitely I think programming, statistical knowledge are things skills that students should have by the time that they graduate from college. But the ability to critically read papers or critique data and figures in the media, and not being afraid to again ask questions about the data and the methods on how the data were collected or how the data were analyzed with the software, right? It's not just like this black box, like you should know the algorithms behind that. And also, again, forming their own interpretations of the data that are independent of the interpretations perforced by the authors of the papers. So specifically for first year students, what they should be exposed to, definitely they should be able to tell what is and what is not a valid resource. They should definitely make use of the library services and use search engines to find credible resources and cite papers. And if possible, in their first year, definitely they should have practiced reading papers. And math I think also plays an important role in data literacy. So having math requirements, pre-algebra, algebra, really understanding what the descriptive statistics are, like the mean, median mode, how those things are visualized, and how those data are communicated to the public. And then also understanding the limitations of each of those things. And then basics of graphing data on just like XY axis, just like a 2D graph. And then their science classes in first year, having a good understanding of how to write a null and an alternative hypothesis, how to collect data, being aware of confounding variables, and then also understanding why having controls are so important in studies. And again, how to present and communicate with data. So I think those things are like my big, long answer of what data literacy is and how students should get data literacy in their first year. Yeah, thanks Akansha. I feel like you covered so much of what is important. But what I loved about your answer in particular, sort of your focus on that, the critical lens and sort of being able to see figures or being able to see numbers and recognize like where that's coming from. And I you talked about this, but I think we do sort of like critiques and look at things, but oftentimes like we don't, we don't, I think we can do a better job at sort of helping our students recognize that it comes from like a particular raw data source, like right, like this, this point that you're looking at has gone through some sort of process, through some sort of analysis, through some sort of manipulation, and being able to understand and break down like that that step, right, like so when they look at something like immediately understanding and asking those questions of where did this data source come from, who collected it, what questions were included, like what, what was excluded, what design choices were made. And I think, you know, to your point about both being a consumer and being a creator designer, to actually like work with data and not being a part of data literacy. I think that's important. I also, there's, I feel like there's a little bit of attention here that because it's, it's, they're both like really large sort of skills. And I think sometimes we focus on the creation or we focus on one or the other, right. And I think, I think what I love about your answer is sort of your emphasis on both are really critical and that like, that really like it's not enough to have students who can open a CSV file, who can create a pivot table with their Excel file and create a graph. It's really like also the decisions that they're making and the choices to really think through that. So with you, agree with all those points. With, with everything that you said about, I mean, what data literacy should be and what people should be, what specifically what students should be getting out of data literacy. Is there one skill that if you could have students come away with one particular skill that you want students to get out of it the most, what would that be? So Ashley, so you're asking in terms of like creating, interpreting and critiquing your graph or like a visualization, what is like a, what is like the most important skill? Yes. Yes, like if you could have students only come away, if they could only because getting them to you understand all the intricacies that you had laid out, that could be that's almost, you know, an entire, entire methods class or retires methods and statistics course. But if we're trying to reach just first year students and get them exposed, what's something that you think that we can capture their attention with and then that's really critical for them moving on? Yeah. In terms of, you know, catching their attention, I think it's really important to like contextualize data literacy into like the students hobbies or like current events. So maybe like, I know TidTalk is like the trend these days. So maybe, you know, getting students to talk to one another, maybe like, you know, creating like a survey. So they can like practice collecting data and asking questions. And then, you know, they can start, you know, maybe like graphing data. So, you know, like, does the number of cats you have dependent how many cat videos you watch on TidTalk, right? Is there a correlation? But then does that imply causation? So I think by getting students to just like, ask questions really immerse themselves into the data will naturally bring out the construction, the interpretation and the critique. So I don't know if there's just like one like specific important thing. I think they're all important because they're, they all sort of like tie together. That totally makes sense. And it looked like Natalia also wanted to contribute. Oh, I was going to say, I feel like the survey design assignment is one that I see a lot of instructors use. And I've seen it so successful. Because it just opens up a lot of questions. Students are so much more engaged. They're designing the questions. It's very hands on learning, which also means there's a lot of like the trial and error. You'll have students really understand like why it matters what kinds of questions you ask and how to work with messy data. And they're finding those questions, right, as they're working, because they're like, I want to know the answer to this question. I want to know how to visualize it and present it. And so they like have to go through this like very tedious process that like oftentimes can be really boring to just learn about. Like if you're just learning a lecture, it's like, when am I going to clean the data set? But it's so much more engaging for them. So I don't know that that's like the, I agree. Like I don't know that there's like this one takeaway, but I will say like there are these there are certain assignments that are so engaging that I think they end up having students grapple with a lot of major questions. And I've seen that in the comments that their attendees also agree that it needs to be something engaging. And they're also supporting the idea of making sure that students can be critical. And so I think that's a theme is that making sure that students understand data and how it can be used, but also that they can be critical of those sources of data, how it was collected, and who does that data actually represent. So talking about data literacy, one of the things that is important as we start with this initiative is to put into context of why, why data literacy is important and why is it something that we should be focusing on. And I want to go to Tiffany. Coming from the biomedical field, how has the pandemic highlighted the importance of data related, sorry, importance of data literacy in regards to medicine. And in particular, are there things that you think are important for students to understand when it comes to biomedical data? That's a good question. I think to start off, we just need to understand just a very basic idea that we're dealing with the intersection between biology and medicine and how we might respond to diseases and medications, environmental onslaughts and things of that nature. It's important to keep that into context. And as we consider what's going on right now, one of the basic things that I think that is necessary is just a very fundamental understanding of what health literacy is and an understanding of what that means and how that ushers in aspects of our own health, various social determinants of health as well and thinking about that. I know one of the things that at the very beginning of the pandemic that struck me was the fact that health disparities exist. And they exist sometimes through no fault of an individual's own and then also just because of equity issues that may happen or occur. And I won't get into the weeds of any of that, but obviously right now we have a disease that sort of takes advantage, takes opportunity of those that do have certain health issues. And so those things are important and highlight the importance of having good health literacy and understanding how we can take the information that's out there and use it to make better health decisions for ourselves and for our families. And so whether it's just some basic information on how to better control our blood pressure, blood sugar, things of that nature on down to some of the more important critical things when we talk about heart disease and things like that. So it does become very important to keep those things in mind because we're dealing unfortunately with something that right now there's no cure for. And we just have some very basic mitigation efforts of distancing and masking at this point that we can all do to sort of help. But I think when it comes down to sort of some things that people can sort of keep in mind when it comes to this is the idea that everyone is different. There's a lot of overlap in what our genetic makeup looks like, but the differences really do pinpoint and highlight how we respond to certain things. And so right now we have a disease where people can be asymptomatic but still spread. And that asymptomatic spread is a large part of why we have the high numbers of cases that we do when unfortunately also the high number of deaths that we do as a result. And so there is this thought I know early on that it didn't impact the younger generation. And so it was just something that was more prevalent and more deadly in the elderly population. But the more information we found and the more the time went on we found that that's not necessarily the case. And so I know a lot of people were saying well if I get it I'll just you'll be okay but that was a very very bad mentality to go in with it because again everyone is different so you don't know how one person is going to respond versus the next one. And it is just these basic very basic things when it comes down to our makeup and how we're dealing with that how our bodies react and respond to that. And so also in keeping in mind that the environment plays a big role in things as well and how our genes are impacted and how our health is impacted. And so all of these things play a huge part in our susceptibility and our risk for developing severe disease versus non-symptomatic or asymptomatic disease. Just in general whether we're talking about coronavirus or we're talking about things like asthma or things that are prevalent every day that we're around and will be around after coronavirus is hopefully a thing of the past. But these things are kind of what come to the forefront just some very basic knowledge of what health literacy can do, what it means to be able to have a better understanding of the material that's out there and how we can use it to make better choices for our own health and the health of our loved ones. Yes, thank you. And I think one of the things that you're getting at is how data literacy can impact how people have interpreted what information that's been coming out about the pandemic. And whether they have, you know, whether they believe the data or how they're interpreting that information is influenced by their data literacy. With the pandemic and what Tiffany has discussed, we've often, we've been hearing this phrase now more than ever. This is not a new phrase, but we have been hearing it more with the pandemic. And so when I'm thinking about data literacy, I kind of want to lean into that trope for a moment and now more than ever trope. And so I want to go to Chad. And I want to direct this question to Chad first, but it's also open again, as always, to all the other panelists and to our audience. Why is data literacy important right now more than ever? Great. Thank you, Ashley. I guess this is picking up on something that Tiffany just discussed and the idea that there needs to be more of an understanding of data, where data is coming from. I think most of the panelists here talked about the origins of data being critical and questioning how the data is being generated, collected, who is it being collected from, and so on and so forth. There was a joke, but there is a bunch of reality. And what the show meant was the kind of the social data that's being generated as well. Data is being collected and generated, you know, second by second. There is just terabytes of data that is being, I think terabytes might be the wrong terminology at this point, but there's just a lot of data that is being collected on a lot of these social media sites and then being analyzed where machine learning or artificial intelligence algorithms are being ran on us, right? And decisions are being made and marketing advertisements are being created to target directly to us. And what tends to happen in these types of situations is we view things that is geared towards us. We do not see what's on the other side of that. The other people see what's on the other side of that. And what tends to happen is we have groupings of peoples, we have tribes that are being created, where people believe the things that they are being told. And this is where the idea and the importance of data literacy comes in. It's being able to question some of the data that's being presented to you, whether it's being masked as an advertisement or whether it's a graph that is directly in front of you. I remember back in my junior high school and even high school when we used to analyze graphs in our history classes and we used to look at the bias in those graphs, right? This has been an existence for a very, very long time. Today, it's coming faster. It's more rapid. They could be a lot more biased. There's a lot of money to be made from advertisers when they know exactly what we like and we no longer have to question where we can get things from. That is a serious issue. Even though there's a lot of convenience with that, that's a serious issue. There needs to be a continuation of criticism and critical thinking around what data is being presented to you. I'll just attach that as someone who worked at smaller nonprofits. The flip side of that, too, is the students that we're working with and the people that we're training up are people who are going to be at that point of having to make decisions and having to collect data. Introducing those students, being able to understand bias in data collection and data security issues is really important, too, from the perspective of if they're going to be the people creating and managing and collecting it, even from a really well-intentioned place, there may be ways that they are contributing to ongoing bias data collection or just collecting data and saving it in places where people who are already in really vulnerable situations are put in even further, more vulnerable situations. That is another piece to what Chad just shared. That brings us up to a question that Muhammad Kosskin has written in about ethics and algorithms. In particularly, they mentioned the book Weapons of Math Destruction. How should we go about and should we be including ethics as we build piglet programs? How should we be including ethics around algorithms and ethics around data as we build these data literacy programs? Chad, did you want to answer that? Sure. I just finished reading that book for a second time, so I'm familiar with some of the ideas there. Certainly, absolutely. I like to think that I'm an ethical person. I like to think that a lot of my ideologies and my ideas lead to me making the best decisions around supporting and analyzing data that is being created by people. At the same time, I think that there needs to be this continuation of education. I think that ethics should lay the foundation of what we do with data and data analysis. I think in some of the classes, we sometimes get pristine data, a pristine data set where we don't have to question where it comes from. We don't have to wrangle the data and clean the data, so there's a reduced number of questions that are being asked of that data set. If there can be an introduction of, hey, this data is coming from students and younger students in a particular community, there are some rules, there are some laws around how we collect this data, why we collect this data, and how to protect students of this data. We need to always be thinking about how we think about ethics when it comes to not only the use of the data, but also how we go and analyze the data to then present it to people that just may not be aware of how the analysis was completed. Thank you. I want to go on to talking about what you just mentioned about visualization of the data, how people are viewing the data, and I want to go back to a concept. Your research has been focused on how people use and understand data through graphs, and I wondered what are some key components that you've noticed that students struggle with, since graphs tend to be one of our number one ways that we visualize information or visualize data. What are some key concepts that you think that students tend to struggle with, and how do you think data visualizations can be used as a way to introduce data literacy? And of course, I'm directing this to a conscious first, but any other panelists can wait in on this as well as well as our attendees. Thanks, Ashley. So I think, so thinking about difficulties with graphing, the literature goes back as far as 30 years, documenting difficulties with students in K through 12, pre-service teachers, undergraduate students, and even professionals about, you know, areas that they struggle in with construction and interpretation. And the literature is full of everything from, you know, lack of knowledge on graph mechanics to not understanding that, you know, you can't use a bar graph to plot every single type of data, right? So understanding of different types of graphs that are out there. Understanding statistics, that's a big one. I've had students, when I put error bars on like a bar graph, they asked me what the capital I is. So they just, they don't know what that means. So the descriptive stats. And then there are also challenges with just software in general. So the one finding that I found from my dissertation work that we can actually help students in the classroom was, and again, my dissertation work was with experts and novices is that, you know, students tend to struggle with just the general thought process that experts have down from their, you know, years of experience working with data making graphs. And also I'm just defining experts in the study. They were professors who have had extensive research and teaching experience. So I did this think aloud study that I'll just quickly tell you about. And it was just pen and paper study. So, you know, having the technology component that just adds layers of more complexity. I just sort of wanted to focus on how were they thinking about data. And so I just gave them a table of raw values. And what the students did is just with your instincts, they just automatically jumped to creating a graph. Without even reading the prompt or establishing what the purpose or what the question were in the first place. The experts, however, they spent a few minutes really thinking about what the general question was and what they really wanted to portray from the data. The students also tended to plot all of the raw data, because they felt like if they missed, if they left out some data points that they would lose points. The experts have where they tended to plot only the data that aligned with their purpose. And although students didn't really have a well thought out structure or process to translating data from the raw form into a graph, the graph they did create had all of the access labels, they had titles. And students also gave a lot of attention to the aesthetics. They were really worried about the color and the design of the graph and not so much the content of the graph and the data that the graph was portraying. The expert graphs were very minimalistic and they were just bare sketches, because for experts, sketching a graph is the first step in their thought process. And this is what they do before they translate their graph over into a graphing software, which is their final product. And so to help students segue their thinking to that of an expert, we developed and published a step-by-step guide to graph construction. And they actually guide students across three phases. So first is like a planning phase. And students are asked probing questions like, you know, what are you trying to do? What are your variables? What type of graphs do you want to make? Do you want to manipulate your data? And then the second part of this step-by-step guide is the walking students through the actual construction portion of the graph. So making sure that, you know, your graph has the appropriate scale, the units, the labels, the title, the key, the sample size. And then the very last portion of this guide is a reflection piece. So having students, you know, just pause and think about what did they just make? Does it align with their intended purpose? And then also, you know, what are other ways that they could have grasped these data? And, you know, asking students to really think about what the advantages are of their graphs and what the disadvantages are. You know, what information does their graph not portray? And I think asking these reflective questions will give students the confidence they need so that when they see a graph in media or, you know, in a textbook, then they can, you know, just have these questions and they can just, you know, have that automatic process that experts have with years of experience. That's, I think that's very interesting. And I really like that you point out that although we think of, and one of the things that we have to kind of overcome is figuring out where students are at when we and how to meet them. But the fact that students were already, even if they weren't, you know, didn't know about the actual data and that's not what their focus was on. They knew they wanted to make the graph look good and they wanted to include all the amount of all of the information as they could. All the experts are just honing in on what was important to them. They're already starting with this open perspective of I want, you know, to include everything. And so I think that's a really, I think that's a really positive way to look at student as students is not being, not that they don't know something, but they're already bringing something there. And building off of that, I wanted to ask Natalia and about are there types of, and so I know that Natalia has been working at North Carolina State University to expand data literacy programming specifically to undergraduate students. And so I wanted to hear from you about the strategies that you've been using to gain student involvement. And also if there are particular types of data, data analysis or data visualizations that are more appealing to first year students. Yeah. So NC State, similar to what we see, what Brian shared about and sort of, or not Brian, sorry, Mandy, shared about sort of our numbers, right? Like so we about two and a half years ago, I came on board and one of the things that was like sort of amazing was we had this like high demand for data and visualization workshops and sort of consultations. And unsurprisingly like the vast majority of that was coming from graduate students and from faculty. And we already heard from Mandy like sort of the reasons why there's they're working on research there. There's a very clear need or point of need there. And so with our undergrads, we saw weren't necessarily attending workshops, weren't really coming to the library for data services. But we know that there's a need, right? And so for us, it was about trying to figure out what the strategy is to tap into those students who as we all know, undergrads are incredibly busy. I mean, all of our students are incredibly busy, but undergraduates sort of are overly committed even if regardless of where they're at, right? Like whether it's because they have jobs, whether it's because they're in extracurricular activities, whether it's because of family sort of situations, undergrads are have a ton to take care of. And so coming to the library for an additional workshop is just not high priority. And that's understandable. And so for us, like the approach that I took was really to build out our embedded instruction. And so I reached out to working with the subject specialists at our library reached out to a number of faculty sort of found different key departments and courses that I thought would really benefit from data support and sort of one shot or two shot instructions, and started just meeting with faculty to learn more about their students, learn more about their needs, and just started getting a lot of instruction requests. And so over the last two and a half years, like our instruction requests, particularly with undergrads has grown significantly. And the approach for this, though, is like that's not the end, right? Like for me, in some ways, building out this course embedded instruction was really kind of attached to like an informal exploratory needs assessment. I wanted to be able to be in front of a classroom and engage with students. My teaching approach is definitely very participatory and active, does a lot of active learning in it. And so I do a lot of informal assessment. I hear a lot from students. And so it's been great because I get to hear a lot of feedback and then respond to that and tweak sort of my whatever instruction we offer based on those needs. And so that's been really great in terms of getting a lot of just like feedback about what students need. And overwhelmingly, students really need these services and need the support. They're always like, I never learned this. I had like, we were never taught how to do this. And they're always just like really surprised. Even just like working with like juniors or seniors are just like, oh my gosh, like I didn't know this existed. And so that's always, you know, unfortunate, but always exciting to sort of build off of that. And so from there, there's like wanting to build out a couple of different kinds of programs. I have a number of things that I have like on the back of my mind, like backburners of programs where like we get to go really to where the students are at. And so this is this is a little bit of like my community organizing training coming to light. I was very much trained that like we go to our constituents, we go to our community, we don't necessarily ask them at first to come to us because it's harder. And so I'm really focusing on building out relationships with existing sort of campus partners. So McNair scholars Office of Undergraduate Research, Women and Minorities in Engineering, we've built out some partnerships with all of these groups and sort of get asked now to come do sessions with them and students now are like really starting to understand and know that they can come to the library for this help. And so we're really, I think, trying to to build off of that. And then I think you you sort of ask a little bit about like specific activities. So I think, you know, I was going to say like the survey activity isn't necessarily something we design, but it's something we support. And I think it's been very successful at engaging students. I think there are other activities that I can think of. So we have access, we have a subscription to this tool called Social Explore, which is a essentially a mapping tool that gives students access to a lot of data, but like census data is like their core thing, but they make it really easy and user friendly. So it is a paid subscription tool. So I feel like it would be interesting to adopt this activity. But one way we approach that is we actually have students download data about schools in their counties that they're from in a different tool. And so then they're able to talk a little bit more about like how to find data in the wild and like learning about metadata, learning about file formats. It's always fascinating to me how students don't know. Like I'll ask like, do have you used a CSV file before? Like do you know what a CSV file? And they'll like generally be like, no, and I'll be like, you probably have, right? And then we open it and they're like, oh, this is so familiar. And so we talk through that. And then we'll go a little bit over like very basic spatial data, right? Having latitude and longitudes. And then they're able to upload it onto this tool where they can then create this two layer map of data that's like pretty personal to them, right? Like they know where these schools are. And then they're able to see sort of the demographic data in the background and personalize it. And so I feel like that's activities like that have been pretty successful. And we've already, I think the various panelists have talked about like personalized data, data that's like real. Because sometimes I think when people think about data, it just looks like numbers on a table, right? Like that's what their instinct is. But when you really bring it to life, that I think is what often really draws our students in. Yes, I think those are, those are very good points. And then just last where the question was, but I know that one of someone, someone of the attendees had mentioned how, how much pushback or if anyone has gone pushback, trying to get faculty members to incorporate more data visualizations into their teaching. And I think that's one of a great way to do it, especially, you know, through something like social Explorer, where you're using mapping. And I know that that's something that I've done, I teach social problems. And it's a great way to get students engaged with data to show them a map of, you know, the United States and then zooming for, since I'm in Georgia, to Georgia and then specifically into Atlanta and then have them go look at, you know, their hometown and how does this social problem affect, you know, where they're from. That can be a great, great way to get engagement with students. So another question from the audience, this is from Julia. They say, of course, it's important to question data data visualizations, etc. But too much emphasis on being critical of data can lead to students deciding that it's all lies, all lies, damn lies and statistics to quote that, and to just writing off quantitative information. And so how do you find a balance between encouraging critical thought about data while still encouraging students to trust and use data that is trustworthy? I think that kind of also goes back to what Tiffany was talking about with data related to COVID-19, and how we want people to trust the data and information so that they follow the healthcare advice related to it. But we also want to teach our students to be critical of data. So how do we balance that? Tiffany, did you want to start out that open to any of the panel members? Sure, I'll jump in and get started. I think, you know, it is important. And one of the things that in graduate school that we learned is just the ability to think critically, to be able to look at information and to be able to ask the right questions to better understand it, and also to convey what it says. So these things are very important, and these are skills that cross so many different areas of knowledge. And so it is important. And I mean, even just some very basic things as a child growing up to be able to be independent and not have someone have to kind of walking through life in essence. And so those things are very essential to life and understanding. But at the same time, like the individual said, there has to be a balance, a necessary balance. Because, you know, we live in a time right now, unfortunately, where there's a lot of misinformation. There's a lot of information that is being put out that that is just untrue. And that's the nicest way I can put it. It is very untrue. And so the thing is, though, is we need to really be able to understand and know the sources of credible information. As we start to look for information and start to do these studies and put out this data, my go to resource a lot of times are some of the databases within the NCBI or the National Center for Biotechnology and Information, that suite of databases that they have. So from literature sources, published peer reviewed articles to data sets that have been collected from a lot of high throughput studies and things of that nature. One of the things that Patty Brennan, who was the director of the National Library of Medicine that houses the NCBI suite of databases, has said is that they want NCBI, the National Library of Medicine to become the trusted source of information. And so what I'm getting at is that we need to know what those trusted sources of information are. And we also need to be able to understand that unfortunately some people are going to put out information that suits their own purpose, that make them look good or take away from other things. And so we need to be able to understand that in essence, and really be able to gauge and vet the information that we're looking at from a critical lens of understanding how it was derived and being able to really be able to say this study is a solid study. They've done the homework to be able to figure out the necessary tools and necessary basis to be able to get the right measures to get the outcomes and be able to say that this is a true and valid outcome that we've gotten. Based on the analysis that we've done, also understanding that there needs to be some very basic things going in at the front of the study when it comes to deciding the study design and things of certain critical numbers that need to be reached to be able to say that this study was a valid study, inclusivity within the study and understanding, like I said, very fundamental idea that everyone is different. And so making sure that that sample population that you're using or whatever you're using does take that into account as well. There does need to be a balance, absolutely. But at the same time, also knowing and understanding those sources of good and quality information. I think some of us may be more or less prone to question some data more than others, but at the same time, just being able to have that skill set is important in going forward, especially in this time where there's so much information, like Chad said, just being put out. And it's just a ridiculous amount of information that's being put out. And so being able to vet it and being able to fully be able to say that this is good quality information is important. But at the same time, realizing that there are sources of good quality information out there that you don't have to really question, you just can trust it. And I think that gets us. Sorry, Akansha, go ahead. Sorry, I was just going to add to what Tiffany said and also what Chad said earlier about marketing, that I think that it's really important to make sure that students know the graphs that focus only on aesthetics for marketing purposes and trying to sell a product as opposed to graphs that are focused solely on the data and just trying to separate those out. And then also, I think the question was, how would students, if they're being overly critical, how can you sort of like balance that out with their interest in learning? What I do is I tell students, okay, well, you gave your critique, now it's your turn, how would you improve this graph? I want you to create it and I want you to provide justifications for your thought process. And students really like that. And based on what we've talked about so far in regards to data literacy, I think that we could argue that data literacy by itself is in the public interest. It is part of the public good. So fostering data literacy is fostering an informed public and therefore just data literacy and fostering that in a way is part of the public good. But I want to think about other ways that are other ways that equipping students with data literacy tools can be used to further public interest projects. And I think this kind of also links with our underlining goal of wanting to make sure that equity diversity inclusion and inclusion underline our goals for creating these data literacy programs. And so I think that there's a good intersection between how do we make sure that equipping students with data literacy can be used as a tool for public interest projects, but also that we're making sure that we're incorporating diversity, equity and inclusion through these programs. And I first wanted to go to Natalia, but this is also open to everyone on the panel, as well as those who are attending. You can leave your questions or comments in the Q&A section. Yeah. So how do we make sure that sort of equity diversity and inclusion are underlined in these efforts? I think, and I'll be interested in hearing everyone else's input, but I think there's this sort of two things that I think about in particular are, and I don't know if this phrasing is the best, but we need more people at the table. We've been talking about this for a really long time. And I feel like that's sort of our focus. And I think that's just, there is no question around that. I agree. We need more people at the table. We need diverse sort of perspectives and broader perspectives and voices. And we need to make a real effort to sort of invite and bring people in and lower barriers to entry. I think particularly within data science and the data world, I think we create a lot of barriers and sort of pre-rex that we ask before people can come in and sort of engage. I know my own personal experience is sort of, it can be really daunting and challenging sometimes because data is such a big world. There's so much expertise and so much layers and different ways that you can approach it that it is challenging, I think. And because I think it comes with this sense of, I think it can feel very strict and sort of objective and not, and I don't mean objective. It can feel like there's not a lot of space for sort of creativity and sort of critical thinking, which is why I think this panel is really exciting to talk about sort of, that's a really critical part of working with data. So definitely like more people at the table. I think one thing we don't talk about enough is this idea of keeping people engaged and retention, right? And sort of how do we ensure that the folks who are invited or who come into the space or who want to be in the space and like haven't been able to gain access into it? Like how do we ensure that they are able to participate in our herd? How do we sort of ensure that the spaces we create are open to different kinds of thinking and open to experimenting? And I think, you know, within specifically around teaching, like I feel like this is a really core reason of why I believe in active learning, why I believe in participatory environments and why I believe in like informal assessment, having students really come in and sort of drive their learning. And I think it helps with removing sort of these assumptions we make. And so I've been in rooms where, and well-intentioned kind people have a lot of assumptions about the kinds of things that first year college students should know, right? So I've been in rooms where people say like, well, they learned how to use spreadsheets and Excel in high school. Like that's something they go over. And so there's this assumption and already there's this like barrier to entry, right? So you have students come in and then they feel like they feel like they can't participate. They feel like they are already behind and then they don't want to say it. And then it's just like this door that's being closed instead of sort of an invitation and being open to people being at different levels and sort of different understanding and still being able to contribute and bring their own perspective and view. So I think that's a very long-winded answer. But like one thing in terms of teaching, like I really truly believe that we need to make space for our teaching to be really engaging and sort of and adapt to like our students' needs and where they're at. Natalia, you hit on some things that are very near and dear to my heart. And a lot of why I was attracted to the PIDLIT initiative to start with. The idea that the underlying goal is to focus on underrepresented students and to create that pipeline into those careers is something that is very, very prominent and important for me. Being a minority in this space, it's not always the easiest to navigate, to be honest. We hear the idea and the phrase that representation matters and it's not trivial. It's not trivial by any means. Just being able to have a seat at the table, it opens up new worlds, new possibilities, new ideologies, new cultures and things to be considered when it comes to whatever's being discussed at the table. And so having individuals with this different mind frame, different life experience, be able to sit down and converse with the majority and usher these ideas into the conversation and make them prevalent and relevant to the conversation is important. And so when you think about that in the context of diversity, equity and inclusion, what happens is you start to create that kind of mind frame where people that look like me were not the exception. It's the norm. And so the ability to be able to do this and to be able to utilize the skill set that PIDLIT is going to offer and those of us in this career field that surrounds data to be able to do that and to be able to bring in and usher in the next generation of scientists, the next generation of data competent individuals. It's an honor and it's also a necessity when you think about it. I think it was already talked about in looking at the misinformation or when we look at data science and AI, artificial intelligence and how there's often a misrepresentation or just a lack of representation of individuals of color or minorities in certain studies and just how the computer is being able to see these ideas and these images and be able to make these guesses and bring forward this data. And then when you come to clinical trials and the lack of representation of minorities in clinical trials, all of these play a part when we think about diversity, equity inclusion issues and we think about how it surrounds data for the public good. There's the program, all of us program that's being facilitated by the NIH, which the purpose of that is to be able to bring in a million individuals to be able to look at their genomic makeup and various other components of their health and being able to create this large data set that is very diverse that can actually be able to pinpoint and identify risk for disease for individuals and using that data to be able to make predictions for individuals on that basis. But for this to work for the general population, it needs more minorities, it needs more of those that are not typically represented for it to really be able to do what it needs to do. And so when we think about things like that, when we think about these projects, we think about having the seat at the table, representation, all of this plays a part in it. And so I just, I love that question. Obviously, I've got a passion of centered around that, but, you know, because I do, I've had enough experiences for better or worse, having been being the only at the table. And so I think it is important for programs like this to move forward and to try to usher in newer generations of people into the data field so that it is not the exception, but more still the norm. I just wanted to, if it's okay if I can just continue the conversation around that question. I love this idea of having more diversity at the table, being able to not only help inform decisions that are being made, but also get into this idea or this process of how algorithms are being developed as well, right? We talked earlier about, you know, places like Facebook and Twitter and social media sites that leverage a lot of these algorithms for their good, for their purposes. But sometimes the folks that are developing these algorithms and running the analysis are people that look a certain way from a certain community. The more people that we can get literate, data literate, more people that we can get into these fields, get these types of experiences, the more people that will be sitting at that table, the more people that can help inform better decision making on how these algorithms are developed and how they're being deployed to the population, right? Sometimes the people that work with it don't represent the population. Another thing too and something that I wanted to kind of circle back to is how I got to be a part of this wonderful conversation is that one of my colleague Jackie Slayton who is a director or who leads our learning community and runs our digital learners leaders program kind of introduced me to this group here. And what the digital learners leaders program is, it's a grant funded program. It's interdisciplinary. It's experiential learning. It's to enhance the skills and the abilities of our Georgia State students who is a diverse group of students. And a lot of things that these students are doing that they're getting real world examples, people that are coming from the field similar to data and ATL with what Brian referenced earlier is we're getting people from the field to come teach some of these classes. You know, some of these students are getting some some questions and problems that are coming from the field and they're working on these and they're experimenting and they're testing ideas and they're creating products that they will then take to the community, their communities, and then make changes, hopefully make changes within their communities, but they're more knowledgeable about things that impact their communities and things that they're developing and now they're going to have a say at, you know, starting at a young age, but as they enter the field, they know how to have these conversations. They know how to create change. They know how to create products that will influence and impact their community. And, you know, having more people that represent a diverse background, particularly in data and data science, I think will then change the way that communities look in future. Thank you. I think everything that everyone has said so far and based on what the comments we've had specifically from Kalila and Catherine about not not only is it, you know, diversifying and bringing more people to the table, getting more people educated about data literacy and therefore, you know, building that pipeline to these to these careers, but also taking those skills and while students are learning those skills to the community. So both Kalila and Catherine mentioned the importance of one of the importance of addressing diversity and inclusion is making sure that you're using those critical understanding of data and bringing it to folks in the community as well. Unfortunately, we are running out of time. There are so many more great questions and comments that the attendees have given us and I know there's more that the panel would love to discuss. So I want to thank everyone for their feedback and I'm going to hand it over to Brian. He's going to tell you how to get in contact with us so we can continue these conversations. And thank you so much for joining us today. Yes, thank you, everyone. This was a this was our first conversation and the chat and the QA has been Q&A has really been very lively. So sorry, we didn't get to all your questions, but we do want you to reach out to us. I want to just a real quick about again, Kalila's comment about connecting it with community. She mentioned the Civic Switch Board and Neighborhood Nexus and some of these portals that are empowering communities. We are very much interested in connecting, as Ashley mentioned, data literacy to the community and also K through 12. That's down the road, but we are interested in all that. So for a later webinar. So thank you, Ashley. Thank you, New America. Thank you, Pitt UN. Thank you, everyone. If you enjoyed our conversation today, please go to newamerica.org slash events and sign up for other ones. They're all great. We're all like-minded here. But as far as us, we want to hear from you. We did not respond to every one of you. And again, sorry about that, but Google us, Pidlit, P-I-D-L-I-T. I think it'll be your first hit besides Pedialyte, the the beverage. You'll be the second one. Pidlit Google us, please, and reach out to us. We are on Twitter. So get on that bandwagon. Twitter can be used for good, not just for bad. Twitter is good sometimes. Get on Twitter. We are at getpidlit. So reach out to us that way. And you can Google any one of us and find our contact information at Georgia State. This will be available on YouTube. And so and while you're at it, follow New America, P-I-T as well. We're all we're all it's all good stuff. So thank you all so much. And is there anything I forgot to say, Ashley? Is there anything in closing? We have another minute. I know. Thank you. You can I'll leave it to Alberta if Alberta wanted to add anything to end it. No, this was a great conversation. And we look forward to continuing it. So hit us up on Twitter. We can get the conversation going. And please also reach us on our website and keep an eye for future conversations. Thank you. Thank you all. Thank you.