 So first of all the podium is really tall and I'm quite short so I'm gonna do my best to move around a little bit and try to appear larger than the podium in real life. So we're gonna switch themes a little bit but we're kind of in a room full of experts and we want to switch teams themes to think about learning. Learning is critical to both of us as people that work with populations that are coming into the field of data visualization. It's something that we think about really deeply. So we're gonna introduce some approaches to think about how to help learners enter this field as they're getting started with working with data, trying to find stories to tell and actually creating visualizations to tell those stories. And hopefully we'll leave you with a few things to think about both as people that use tools as reflective practitioners and people that make visualizations that are intended for novice users. Alright, so a little bit about us. My background is in art design, education, and software development. I've always been interested in data and technology for the purposes of creative communication. So visualization is like a perfect marriage of those things. I've created walking data visualizations about climate change. I've created large red sculptural flowers that sense water quality and sit in creeks. And I've done media analysis with data visualization. I'm a professor at Emerson College and I sit in a journalism department. So I spend a lot of time introducing journalism and communication students to new methods for data driven storytelling. And my background is actually in robotics and education. So if any of you have used the Lego Mindstorms, that's the kind of stuff I used to do. So I come to the world of data storytelling and data visualization from sort of an educator point of view focused on how to help empower people to do what they want to do in new and exciting ways. So that's led me to approach data with the idea that we can bring people into working with it with an arts invitation. The arts is sort of the best tool we know of for empowerment and engagement with people. So we bring people in with activities like drawing on boards, we paint data murals around the world, I help kids come up with abstract data visualizations, and bring craft materials into conferences full of people in suits. So it's a pretty fun way to get people excited about something that isn't really a spreadsheet training. And most of this approach is documented on my datatherapy.org website. And the reason this has been so appealing to the nonprofits, community groups and things like that, that I've been working with for the last, been we're doing workshops for about 10 years now, is this type of stuff. This is, make no mistake, this is something that people are being told is really important. There's a reason things like this sell out. We do any sort of workshop that has data in the name, and there's a 100 person waiting list. Not necessarily because we're brilliant, that may be the case, but actually, I think you could probably just have like a snack table that said data on it and have like, it would have a waiting line of 100 people. So this is the reason, it's because this is so critically important in sort of the public perception of things. And that means there's a responsibility, there's a responsibility to dig past the hype. And I think it's a responsibility, the experts or the sort of the people creating things in this room actually share. And that's the reason that I like to focus on this stuff. How do we help newcomers tell the difference between why they should be making something and how they should be making something? That's a key difference we try to pick apart in our work. So through this talk, we're going to try to give you some of the principles that inform that and some of the ways we think about it in reaction to the tools that we see our users starting to use and learn with. So Wikipedia has this great policy, which you can see stated here for newcomers, which is please do not bite the newcomers. And so I think it's worth asking for data visualization, who are the newcomers and how do they get introduced to concepts of data analysis, storytelling and visualization. And so while there are newcomers from a ton of different fields and non-technical as well as technical fields, we primarily work with newcomers from non-technical backgrounds. And they're excited about data visualization as a new way to communicate insights and tell stories for civic purposes. And they include the list of folks here. So journalists, librarians, non-profit and advocacy organizations, artists, municipal government folks, museums, K through 12 educators. And so I think it's worth asking, is anyone who identifies with one of these domains as their kind of primary domain, raise your hand if you're one of those groups. Okay, great, okay. So I'm glad that we have some of those folks in the room. So all of these folks have heard of big data, experienced, interactive data visualizations and infographics. Journalists want to get started with data journalism. Non-profits and governments feel pressure to be more data driven with their decision making. Librarians see public data as a really amazing new data source for their patrons. But the entry point for data analysis and literacy for many of these newcomers is experimenting with visualization tools. This is the first way that they come into this is through the tools. But the tools that they use don't necessarily do a good job of scaffolding the learning process so that they can take the next step with data. Okay, so increasingly in our work we're working with partner organizations in these different domains to develop activities, toolkits, MOOCs, other learning experiences for these newcomers to working with data both individually and across their organization. So there's a couple of projects which I'm mentioning here which we have underway this year. But the main reason we're here today and if you don't take anything else away from this talk, just look at this slide. I always try to give one slide that is like this is the key thing, is it's not really to talk about the formal learning experiences but to make the case that sense tools are the first entry point for newcomers that data visualization tools should be considered informal learning spaces. So we can try to scaffold better learning experiences in the tools themselves to introduce concepts of data analysis and design. So we're going to talk more about learning but first I want to talk about food. We both love food. I do a lot of cooking and I've been doing some baking recently. This is not one of my cakes. This is an amazing cake that I saw online and it made me think for a second. I'll just ask you. So raise your hand if you're intimidated by the idea of making this amazing three-dimensional planet cake. OK, yeah, that's almost everybody. Raise your hand if you're excited or inspired by it. OK, good. That's actually maybe I'd say a third of the room. That's really interesting. And it's interesting because visualizations like these beautiful things we've been seeing today often serve the same purpose with other populations that cake serves to us sort of non-chefs. The idea that these beautiful things can both intimidate and inspire is a key thing to think about. And at worst, these visualizations that we're making can often scare away these intimidated populations, which is certainly not the intent of most people making them. At best, these visualizations can be a hook that inspires us, like those people that raise their hand for the second thing, to come into the world and want to make things like that, even if we might not have the budget for it. And so this explosion of interest in visualization has led to an explosion of tools for novices. We've actually cataloged like 500 of these tools. But many of these tools prioritize the creation of quick, flashy graphics. And they ignore what is really an opportunity to introduce concepts and terms of data exploration, cleaning, analysis, and storytelling. So the tools become black boxes. And then additionally, just because the tools are proliferating so quickly, this leads to a lot of complexity for newcomers as well. So how do you choose a tool? There are guides to tools, like the DataViz.tools, which is a great site. We have a guide specifically for very non-technical folks at netstories.org. But most of these are targeted towards users, not towards learners, who may not yet have the terminology to describe what they want to do. And so while all tool designers talk about the users of their tools, today the main case that we want to make is that designing for users is really different than designing for learners. So if you design your tool with the idea that people coming into your tool are newcomers, both to visualization, but also to concepts of working with data, just generally speaking, how might that change what your tool does and what kinds of things you build into your tool? So we're academics. So we do things like come up with guidelines and design principles. So we're going to use that as a framework for talking you through a couple of concrete examples of ways that we approach this, the difference between thinking about tools for learners and settings for learners versus tools and settings for users. So just briefly to summarize, and then we'll dig into these principles. And they're not really sort of criteria to judge whether a tool is correct or not. It's really just sort of axes to reflect on and to think about and to then talk about. So the idea that a tool for learners should be focused is really the idea that it should think about focusing on one thing and doing it well, helping someone do that one thing. The idea that a tool should be guided, introduced with activities that make sense and make things fun for the learner. They should be inviting in some way that is appealing to new people coming in that might not even exactly know what it's for. And they should be expandable. They should open up a black box that allows people to offer pads, it offers pads to deeper learning once you're past that knowledge stage. So we write about this stuff in academic papers. This is the latest one. It's about these design principles in one of the tools we've created called Databasic.io that is our playground for trying these things out with users. We'll show some of that later. These papers also share some of the inspirations and where these things came from. And some of those come from people like Edith Ackerman who works in the field of education and child psychology. Others come from names like Seymour Pappert who worked in constructionism and the stuff that I studied. And sometimes they come from different places including the food network. So I want to go back to the food metaphor and think about Good Eats which is a fantastic show. How many of you have seen Good Eats? So yeah, so that's about a third of the room. Great. So I want to use Good Eats as a thing to react to. And it's a TV show that's ran for about 15 years. It's kind of quirky, focused on not just cooking but helping people understand the why and the how and the science behind it. So I want to start with a quick quote video clip from the host of that show, Alton Brown. And I think this embodies some of what we try to bring to our approach. It's like, look, if there's not a why, it doesn't go on the show. There has to be an absolute reason for everything. What we make is not good food. We make sense first. And then the people at home make good food. And if we don't give them the sense part, they won't be able to make good food. This is that key thing. We make sense, not good food, right? So the idea that these tools for novices should try to leave someone with a good sense of why something is happening when it might be useful. That's the key takeaway for us when we think about these informal learning spaces that Catherine was talking about. So like we said, our playground for trying out these principles has been this platform called Databasic.io. It's a free and open source set of four simple tools that's built explicitly for novices and for learning purposes. It's used around the world now. It comes in a couple different languages by journalists, educators, nonprofits, and other folks. So we're gonna circle back to this in a minute when we talk about some tools to reflect on some of the design decisions that we made when we were making this as we show you more examples. So let's run through these principles. Again, we're thinking about sort of this learning experience of Good Eats the TV show as sort of a model to think with. So of course we can get inspired by this precisely because it is a learning experience. These TV shows are set up to help you learn about cooking and learn how to cook and be motivated to cook. So that plays out really nicely in Good Eats. You have these, each TV show, each episode is based on one theme of one ingredient that they use throughout with names like Steak Your Claim, Spice Capades, and Curious Yet Tasty Avocado Experiments. So these are the kinds of things that invite you in with that theme. And we're gonna ignore in these examples some of the kitchen sink tools, the tools that do everything, because in our experience, those are the ones that people don't actually enter with. They enter by like finding some tool that can help them make a visualization, not by opening up Excel and then trying to turn that into some company report. That's the kind of population that we run into in the workshops that we're doing all the time. So the first example of what we mean by focused is in one of our tools. We wanna talk about a tool called WordCounter, and we wanna talk about how it doesn't really present too many options as one of the ways that we stay focused. WordCounter is a simple tool that introduces the idea of thinking about text as quantitative data and the idea of sketching a story that you can see to then play out how you find a story. So sketching is the activity that goes with this. And all it does is count words, bigrams and trigrams. It introduces that vocabulary as well. It has four types of inputs and it's just focused on that activity of sketching a story. So not too many options here. So when you're showing examples, it's always worth showing what's, if you're showing something that's focused, it's always worth knowing what is an example of something that is not focused. So in this sort of categorization schema, we would say that a tool like Tableau, which is again one of these more like kitchen sink, WYSIWYG tools for making data visualizations. That's a tool that's not focused. This is not to say that it's bad. I teach Tableau in my classes. But this is to say that it's not a focus tool because it doesn't actually support people getting up and running with something meaningful quickly. A focus tool helps people do something meaningful quickly. So it's very flexible, but it's difficult for people to get started. You really need to attend a training or something to watch a Lynda.com video. In contrast, and maybe for one of, this kind of similar reason, Tableau released something called Visible, which is more of a constrained playground. It's a visualization exploration tool on mobile with a kind of much more narrow set of things that one can do, a really kind of lovely, beautiful, pleasurable interface to engage with. So that's an example of something that is more focused. And then another example of something, an example of what is a definition of focused is a tool that does one thing well. So something like Timeline.js, very simple, does exactly what its name says. It makes timelines with JavaScript. So it's immediately clear what it does. The homepage of the tool walks the first timer through a series of four steps to make an interactive timeline from a spreadsheet. And while these kind of things are, I mean, I feel like they're way beneath the level of most of the people here, I think it's worth reflecting back to the kind of value of simplicity and thinking about how we can value simplicity in a tool. Making something that is focused is hard. As tool designers, oops, ourselves, we're really familiar with the desire to add features to things. We hate taking out features that we've developed. We hate, we often postpone editorial decisions like, oh, we'll just let, like make it an option and the user can decide, you know. And so, you know, thinking about remembering each time that you delay those editorial decisions or you forego taking something out, you're upping that level of complexity. So in a sense what our tools should aspire to is at some levels, the kind of simplicity. Okay, so that's three principles to think about for focus tools. Next, we wanna talk about guided tools and introduce three principles to think about tools that introduced activities to get the learner involved and engaged. And I'll do that with another short video. The quarters and it fell apart the rest of the way into the processor. One carrot just peeled and snapped into pieces and to tell you the truth, if you wash the carrots, you can skip the peeling part. Three cloves of garlic, no paper please. And about half of a red pepper just torn into chunks. Again, natural and easy in a cooking show because they're set up as these guided walkthrough of recipes quite often. Now there's a quote here that I think that is super important to remember and it's from one of my mentors, Edith Ackerman who just passed away recently. And she said, in a playful environment, you feel safe enough to explore ideas that would otherwise be risky. So in a playful environment, you feel safe to take risks that you otherwise wouldn't take. I think that's critical in these kinds of learning environments and you can see Alton Brown doing that here, setting up this natural joking with the camera and talking about the shortcut around something or the easy way around it. That's a key principle under this sort of guided approach that we wanna talk about. So what does that mean in practice? If you pick it apart, one idea is Graph Commons and they do a wonderful job of having these examples on their homepage which suggests to you the kind of power that their tool can bring. And then with the main invitation to drop into things, you end up with an empty canvas with an invitation to add a node. And there's this gap here that we see in our users when they end up with these empty canvases. They're not sure where to go next. And we end up with this idea that for guided tools that are trying to help that person that doesn't know what's coming next or how they can take advantage of that power, you gotta fill in those blanks. And so that's one of the principles that we walk away with under being a guided tool. So including sample data in the tool itself is a convention that a lot of developers and designers are starting to use that makes tools more guided and which is something I think we should really congratulate folks for because what this means is that learners can quickly try out running the tool with data that works. Rather than spending a bunch of time formatting their own data only to find that it doesn't actually work in the tool. So this is a tool called Chart Builder which was built in-house at Quartz which is a journalism outlet. It's sort of like halfway there to guided. So they have sample data. They have a relatively simple process outlined on their home screen for how one goes about setting up a chart, tweaking some options. And the issue here and the reason it's only sort of guided is the topic of the sample data. So if you look at what they're actually showing for sample data, it's comparing juice and travel. So it may be kind of funny, dummy data for people who are seasoned with this stuff but it totally makes no sense, right? It's unclear like what are the units? How are we actually relating those things? Why are we comparing juice and travel? Who's juice consumption? Who is traveling? Where are they going? And so remember that when newcomers come into something, the mindset is they enter intimidated and so they're unsure about their ability to work with data and so anything that they find like a little bit confusing they're gonna assume that it's their fault. So they're gonna blame themselves for not understanding the juice and travel chart here. And so another way that we can think about guided is that guided tools provide clear contextual documentation in addition maybe to some sample data to start new learners off. So a really nice example of this is Data Wrapper. This is a chart making tool that's used very frequently by journalists. It has its process. So it also has sample data which you can see there on the dropdown menu and it has the process outlined at the top of the page that names the four steps of using this tool. Upload data, check and describe, visualize and publish. So this is simple linear outline. It helps give a shape and names to a process that learners may not have gone through enough times to actually have names yet for. They may not be able to kind of reflect on their own process enough yet to actually name those stages. So it's actually kind of naming a process for them. It communicates that the creation of the visualization is simple, it's finite, it's achievable, and the language is geared towards newcomers. It's saying it all starts with your data. If you just want to try Data Wrapper, here are a couple sample data starts to get started with. So again, it's sort of geared towards that first timer who's coming to their homepage. Okay, so that's a little bit about what we mean by guide is. Let's go to the next one, tools that are inviting. So let me give an example of video here. Temperature is a big factor in fermentation. We don't want to let this get higher than say 75 degrees. So again, Alvin Brown is narrating to the camera here. He's having a conversation with the audience. Very natural and easy to do in a cooking show setting. They're also using the compartment shop, a classic way to have someone feel like they're actually in the setting that the video is being filmed in. You often see it like inside of an oven in a movie or something like that. It sets up a strong sense of being in the space with the person. And the aesthetics really matter. So this idea that they're doing this is actually a guideline that we take away that the way you introduce these tools and the visuals and in this case, the angles that you're doing it with are super important and critical to have a user feel invited in to try the tool out. So the first example I want to give is one of the ones from these kitchen sink tools in Excel, one of our favorite tools to go back to. The pivot table. Who loves pivot tables? Who loves pivot tables? Oh, go, go, go, go. I can, the problem with pivot tables is that nobody knows what the hell a pivot table is. Who named this, right? So when you dig into the history, you find out that it was a computer, a software engineer that named this thing. Pivot table, what the hell does that mean? I go into a room and I show someone what a pivot table does and they're like, holy crap, oh my God, you would have saved me two days last week if I had known what that button meant. Because nobody knows what this means. These things are critical and I'm not gonna get into the UI of how you invite someone in, but the terms and the words and the language that we use matter. When we go into workshops and run them, we're often talking about telling stories with information because if you roll into a room and talk about making data visualizations, half the people walk away because they say they don't have the budget and they don't have the expertise and they don't have valid enough data to do it. So you haven't even, you've lost your time to make an argument about this stuff. So these kinds of words really matter and they matter in the tools that people are running into first of all or might be on their desk already. So what does inviting mean? An inviting tool might present itself with a sense of humor. We might consider inviting to be visual design. It might be somewhat playful. So for example, in our tool in Databasic called what WTF CSV, we invented this to solve a real world problem that people in our space were encountering. So journalists, nonprofits, artists are increasingly making use of data that they're downloading from the web, but that typically comes in CSV form. So first of all, they don't tend to know what a CSV even means. And then when they do get a CSV, they're like, what do I do with this? Like I have a spreadsheet now. Like what's the next step from here? And so if you all are probably all R people and so you know with R you can run the summary command and you kind of quickly get a sense of what is the scope of your data? What are the different columns? How are there variables laid out and so on? But if you're a new learner, WTF do you do with the spreadsheet? And so the other thing here is with WTF CSV, new learners often don't understand that visualization can be used to explore data. So in the exploration stage, not just at the end in the presentation stage of the process. So WTF CSV characterizes your spreadsheet. It's very similar to R summary command, but just in visual form. It starts to show you a picture of what is going on with your CSV. So it helps support the initial data exploration process, but it has an irreverent name to communicate that the process of discovery can be fun. It can be okay to not know what is going on with your CSV file, it has bright colors. And then we also try to use sample data that's fun and culturally localized. So in the US for folks using the English version, they get sample data from UFO sightings, which is fun data. And then for example, for Portuguese speakers, we have sample data about Brazilian soccer results and Portuguese baby names. Okay, and so a final way that tools can be inviting is by demonstrating their ability to be used in professional context that relate to the backgrounds and context of your newcomers. So for instance, Nightlabs story map tool has examples linked from the homepage that show their maps in action. So they show published maps in action for published for professional journalistic outlets. So this helps communicate two things to the learners. First, it gives the tool credibility. So it's robust enough to be used in the field by professionals. And then secondly, it shows high quality examples for what kinds of outputs you can expect from this tool. Because as we were describing previously, there's so much complexity in the tools base for new folks coming in. A lot of times newcomers are simply trying to answer the question, what is this tool good for? Okay, we'll go quickly through the last one as we get some more time. They can both get messed up the same way. If the vessel they're cooked in is dirty, if the mixture itself is impure, if it's agitated at the wrong time, little baby crystals can be formed in the mixture. And as they cook, these little crystals can grow into bigger and bigger crystals. And eventually, your nice clear glass starts looking more like a shower door and your brittle starts looking more like a praling. So he's comparing creating peanut brittle to the manufacturing process for glass. This one especially hit home for me because my wife does stained glass. So I know a lot about both of those processes. The idea here is that Alton Brown, like us in our tools, doesn't shy away from the scientific technical language but doesn't start with it. It's not that we can't tell and introduce a complicated topic. It's that we want to do it in a way that makes sense. We want to do it in a way that's helpful and we want to do it in a way that helps someone that is a novice get started in a language that makes sense to them and then holds their hand as they go into a language that is a deeper and that they'll need to know if they actually want to dig into something. So the first example, a quick one around raw. What they do is they generate some of the more complex visualizations with D3 that you can't do in tools like Excel without coding. They have sample data, it's wonderful. My key thing is at the bottom, they're downloads. The idea that you can get this out in an embeddable form but also grab the SVG to then take that into illustrator with your graphics department and actually tweak it and modify it as you need. So they give you not just the quick and easy way to use it, they also give you the way that lets you use it inside of your existing chain for processing images and graphics and tweak it as you need to. Okay, so whether you love or hate infographics, they are often the first step that a non-technical newcomer takes towards making data visualizations. So we've found that folks in nonprofit organizations, libraries and educations in particular love infographics. They're often using tools like Pictochart, Infogram, Vengage to create visual stories with icons and illustrations. So the challenge with infographic tools as informal learning spaces for data is that they don't introduce any terminology or any process around working with data and they often don't help the learner then graduate to the next step to the more complex tools. So if we're talking about how a tool can be expandable, it's expandable if it can put itself in a pipeline of analysis, help you understand concepts and process and then puts you on a path towards being a creator of more complex and customizable outputs. So related to that idea of putting yourself in a pipeline of analysis, one way to do this in a tool space is to introduce vocabulary in the tool that will help learners take the next step with other more complex tools, sort of like what we're just talking about here with like not hiding the technical language or using cute language to obscure them. I mean, I think that's a frustration I always have with user interfaces is like I know the term but now they've hidden it from me and they've made up a cute new term. And so explaining technical terms in situ. And so we try to do this in data basic by where we put a little quotation, a little question mark next to any technical term. So this is an example from connect the dots which introduces basic principles of network analysis. So if we use terms like nodes or edges or centrality, we, you can hover over those and get a very short definition in non-technical language of what that means. And then learners, ideally, once they understand those terms they have those terms sort of under their belt with this very simple tool, they can then graduate to more complex network analysis tools like Graph Commons or like Gefi or like the tools that John was talking about and have developed some familiarity which is a base that they can take to the next level. And we're weak there just to be self-critical. We're weak there, we're not pointing at those tools. So we're missing, I don't wanna hold up our example as like we've done all of this and you should just do what we did, not at all. Again, as we talked about it, that's our playground for trying this stuff out. So there's lots of gaps that we're still finding and discovering that as people use these tools, we're trying to fill. But if you take a step back from that, those four tools again, the idea that you can be focused around one activity, you can guide the user through, you can be inviting in a way that relates to where they are and meets them where they are and you can be expandable in some way that puts it in sort of a chain of working with a tool to learn something and helps them learn how to graduate from that novice use to the greater use. I'll make it through this without coughing. So in fact, I think we'd argue that designing for learners is in fact more important than designing for learners. For users. Sorry, designing for learners is in fact more important than designing for users. So there's two reasons for this. The first is a simple issue of quantity. So because of exploding interest that's happening across fields and domains, there are far more newcomers to doing data visualization than there are people doing visualization professionally. So probably also all of us who do this work professionally consider ourselves newcomers to various spaces, whether you're a newcomer to D3 or you're a newcomer to map making or to using remote sensing imagery in your work. So maybe keep in mind the new things that you're trying to do or the new communities that you're trying to break into that still intimidate you and then you'll understand some of this mindset that newcomers are bringing to data visualization. And so secondly then, well, often the first question that people ask about a visualization is how did you make that? Designing for learners helps us all become more critically engaged with visualization and shift ourselves and our users towards the question, why did you make that? So this is the much more interesting question. It's a better question to cultivate in newcomers. When is visualization on appropriate mode of communication? What can visualization help do that really can't be done by words and illustrations? We think that learners like ourselves and you all who are a little bit further down the pathway have a responsibility to newcomers to help them ask better questions, form better concepts and derive better insight from data and not just or maybe in addition to making beautiful, wonderful pictures. So I think. So hopefully you can all walk away with the idea of how someone might approach the beautiful visualizations that you're making like that giant cake and we can help people walk away more inspired like we are by those invitations instead of being intimidated by it. Thanks. Thank you.