>> Great.
So first of all, the podium is really tall and I'm quite short.
I'm going to do my best to move around and try to appear larger than the podium in real
life.
So we're going to switch themes a little bit.
But we're kind inform a room full of experts.
We want to switch themes to learning.
It's critical to us as people who are working with populations coming into the field of
visualization.
We are going to talk about how to help learners enter the field.
Working with data and trying to find stories to tell and how to create visualizations to
tell those stories.
And we want you to know both as people who use tools and who make visualizations that
are intended for novice users.
>> All right.
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 skull churl flowers that sense water quality in creeks.
I'm a professor at Emerson College and I sit in a journalism department.
I spend a lot of time introducing journalism and communication students to new methods
for datadriven storytelling.
>> And my background is in robotics and education.
The Lego mind storms, that's the stuff I used to do.
I come to the world of data storytelling and visualization from an educator point of view.
Focused on how to help empower people to do what they want to do in new and exciting ways.
That's led me to approach data that we can bring people in with an arts invitation.
The arts are the best tool we know of for empowerment and engagement.
Bring people in with activities like drawing on boards.
We paint data murals around the world.
Help come up with abstract visualizations and bring craft materials into conferences
with people in suits.
It's a great way to get people excited without spreadsheet.
This is on my Website.
And the reason this is so appealing to the nonprofits, community groups that I have been
working with� doing workshops for about ten 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.
Like we do any sort of workshop that has data in the name, there's a hundred-person waiting
list.
Not necessarily because we're brilliant, that may be the case, but I think you could have
a snack table that said data and it would have a waiting line of a hundred people.
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 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 are going to give the principles and inform that and some of
the ways we think about it and the reaction to the tools we see our users starting to
learn and use with.
All right.
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?
How do they get introduced to concepts of storytelling and data visualization?
There are people from technical and nontechnical fields.
We work with new comers from nontechnical backgrounds.
They're excited as data visualization as a way to tell story for civic purposes.
And they include the list of folks here.
Journalists, librarians, nonprofit and advocacy organizations, artists, municipal government
folks, museums, K12 educators.
So I think it's worth asking.
Is anyone who identifies with one of these domains as their primary domain?
Raise your hand if you're one of those groups.
Okay.
Great.
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 infographic.
Journalists want to get started with data journalism.
Nonprofits and governments feel pressured to be more datadriven with decision making.
Librarians see public data as an amazing new data source for their patrons.
But the entry point for data analysis and literacy for newcomers is experimenting with
the tools.
This is the first way they get to this.
But the tools don't do a good job of scaffolding the learning process so they can take the
next step with data.
So increasingly we're working with partner organizations in these different domains to
develop activities, toolkits, MOOCs, to work with data individually and across the organization.
So there's a couple of projects which I mentioned here which we have under way 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.
It's not really to talk about the formal learning experiences, but to make the case that since
tools are the first entry point for newcomers, the data visualization tools should be considered
informal learning spaces.
So we can try to scaffold better learning experiences in the tools themselves to introduce
con cements 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 have been doing baking recently.
This is not one of my cakes.
This is an amazing cake that I saw online.
And made me think for a second� I'll ask you.
Raise your hand if you're intimidated by the idea of making this amazing, three-dimensional
planet cake?
Almost everybody.
Raise your hand if you're excited or inspired by it.
Good.
That's actually maybe a third of the room.
That's really interesting.
And it's interesting because visualizations like these beautiful things we have been seeing
today often serve the same purpose with other populations as that cake serves to us sort
of nonchefs.
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 the people who raised
their hand for the second thing to come into the world and want to make thing like that,
even if we might not have the budgets for it.
>> And so this explosion of interest in visualization has led to an explosion of tools for novices.
We have actually cataloged like 500 of these tools.
But many of these tools prioritize the creation of quick, flashy graphics and ignore an opportunity
to introduce concepts and terms of data, story stemming.
Just because the tools are proliferating so quickly, leads to complexity for newcomers
as well.
How do you choose a tool?
There are guides to tools, the dataviz.tools.
We have a side for nontechnical folks at the netstories.org.
But they are targeted towards users, not learners who may not 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 in 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 about thinking about tools and settings for learners.
Versus tools and settings for users.
So just briefly to summarize and then dig into these.
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 axis to reflect on and think about and then talk about.
So the idea that a tool for learners should be focused.
It's really the idea that it should be focused on fun thing and doing it well.
Helping them do that one thing.
Guided and introduced with opportunities that make sense and should be fun for the learner.
Should be inviting that's appealing to new people coming in that might not know what
it's exactly for.
And be expandable.
Open up a black box that offers paths to deeper learning once you're past that novice stage.
We write about this stuff in academic papers.
This is the latest one.
It's about design principles and the tool suite we created, data basic.io.
That's the playground for trying it out.
We'll show that later.
These papers show some of the inspirations and where these things came from.
Some come from Ackerman who works in the field of education and child psychology.
And Seymour who worked in constructionism and the stuff they studied.
And sometimes they come from different places.
Including the Food Network.
I want to think about the food metaphor, and Good Eats.
How many of you have seen it?
A third of the room.
I want to use Good Eats as a thing to react to.
It ran for 15 years.
It was quirky.
Focused on not just cooking, but helping people understand the why and the how and the science
behind it.
I'm going to start with a quick video clip from the host of that show, Alton Brown.
And this bodies what we try to bring.
>> Look, if there's not a why, it doesn't go on the show.
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.
>> So this is� >> If we don't give the sense part, they won't
make the good food.
>> We make sense, not good food.
That's the key thing.
So the idea that the tools for novices should leave with a good sense of when it might be
useful.
That's a takeaway for us when we are talking about the informal learning spaces that Catherine
was talking about.
>> So our playground for trying out these principles has been the platform, data basic.io.
It's four simple tools built explicitly for novices and learning purposes.
It's used around the world now.
Comes in a couple different languages by journalists, educators, nonprofits and other folks.
We're going to circle back in a minute when we talk about some tools to reflect on some
of the design decisions we made when we were making this as we show you more examples.
>> So let's run through the principles.
Again, thinking about the learning experience of Good Eats, the TV show, as a model to think
with.
So, of course, we can get inspired by this because it's a learning experience.
Right?
These TV shows are set up to help you learn about cooking and be motivated to cook.
That plays out nicely in Good Eats you have each episode is based on one theme of one
ingredient that they use throughout with names like "Steak your claim," "Spice Capades."
And these invite you in with the theme.
We are going to ignore the kitchen sink tools.
Those that do everything.
We see that's not what people enter with.
They enter finding a tool that makes a visualization.
Not by opening up Excel and turning that into a company report.
That's the kind of population 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 want to talk about a tool columned Word Counter and doesn't present many options to
stay focused.
It introduces the idea of thinking about text as quantitative data.
And the idea of sketching a story that you can see to play out how you find a story.
Sketching is the activity that goes with this.
And all it does is count words.
Bigrams and trigrams, introduces that vocabulary.
Four types of input and focused on the activity of sketching a story.
Not too many options here is.
>> When you're showing examples, it's� if you're showing something focused, it's worth
knowing an example of something that is not focused.
So in this sort of categorization schema, a tool like Tableau, which is like a kitchen
sink, WYSIWYG, it's not bad.
I teach Tableau in my classes.
It's not a focus tool.
It doesn't support people getting up and running with something meaningful quickly.
A focused tool helps them do something meaningful quickly.
It's flexible, but difficult to get started.
You need to attend a training or watch a Linda.com video.
In contrast, and maybe for a similar reason, Tableau released something called Visible,
which is more of a constrained playground.
It's a visualization exploration tool on mobile with a much more narrow set of things that
one can do.
Really kind of lovely, beautiful, pleasurable interface to engage with.
That's an example of something that is more focused.
And then another example is something� an example of what is the 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 home page of the tool walks the first timer through a series of four steps to make
an interactive timeline from a spreadsheet.
And while these kinds of things, I feel like, are way beneath the level of most people here,
I think it's worth reflecting back to the value of simplicity and thinking about how
we can simplicity in a tool.
Making it� oops.
As tool designers, we're filled with the desire to add features to things.
We hate taking out features that we have developed.
We hate� we often postpone editorial decisions like, oh, we'll just 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 tool should aspire to at some level is a kind of simplicity.
>> So that's three principles to think about for focus tools.
Next, 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.
>> And fell apart the rest of the way.
Into the processer.
One carrot, just peeled and snapped into pieces.
And to tell you the truth, if you wash the carrots, skip the peeling.
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 walkthroughs
of recipes quite often.
Now a quote that I think is superimportant to remember.
From a mentor, Edith Ackerman who passed away recently.
She said in a playful environment you feel safe enough that would otherwise be risky.
You feel safe.
That's critical in these kinds of learning environments.
And you see Alton Brown setting up the natural joking and the shortcut or easy way around.
That's a key principle under the guided approach we want to talk about.
When does that mean in practice?
If you pick it apart, one idea is graphic comments.
And they do a wonderful job of having these examples on their home page which suggests
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 with our users with the empty canvass, they're not sure where to go next.
And we have the idea for the guided tools that is helping the person who doesn't know
what's coming next or how to take advantage of the power, fill in the blanks.
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 which is something I think we
should really congratulate folks for.
What this means, learners can try out running the tool with data that works rather than
formatting their own data to find it doesn't work in the tool.
This is Chartbuilder.
Built at home at quartz.
A journalism.
They have a relatively simple process on the home screen for setting up a chart, tweaking
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 maybe kind of funny, dummy data for people who, like, 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 whose juice consumption?
Who is traveling and where are they going?
So remember that when newcomers come into something, the mindset is they enter intimidated.
They're unsure about their ability to work with data.
So anything they find a little bit confusing, they are going to assume that it's their fault.
So they're going to 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 to sample data to start new learners off.
So a really nice example of this is data wrapper.
This is a chartmaking tool used frequently by journalists.
It has its process.
It also has sample data which you can see there on the drop-down menu.
And 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 might not be able to kind of reflect on their own process enough yet to actually
name those stages.
So it's 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 newcomer.
It says it all starts with your data.
If you want to try a data wrapper, here are a couple of samples to get started with.
It's geared toward the first timer coming to the home page.
>> Okay.
So that's a little bit by what we mean by guided.
Next one, tools that are inviting.
So an example video.
>> Temperature is a big factor in fermentation.
We don't want to let this get higher than say 75degrees.
>> So, again, Brown is narrating to the camera here, 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 in the setting.
That the video is filmed in.
You see it in an oven in a movie.
It sets up a strong sense of being in the space with the person.
And the esthetics really matter.
The way you introduce the tools and the visuals and the angles you're doing it with are super
important and critical to have the user feel invited in to try the tool out.
So the first example I want to give is one of the ones from the kitchen sink tools.
In Excel, one of our favorite tools to go back to.
The pivot table.
>> Who loves pivot tables?
Oh, a couple.
Okay.
Good.
>> The problem with pivot tables is that nobody knows what the hell a pivot table is.
[ Laughter ] Who named this?
Right?
So when you dig into the history, you find out that, like, 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 many�two days last week if I had known what that button
meant.
Nobody knows what that means.
And I'm not going into the UI of how to 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.
If you roll into a room and talk about making data visualizations.
People say they don't have the budget and the expertise and they don't have valid enough
data to do it.
So 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.
Might consider inviting to be visual design.
It might be somewhat playful.
So, for example, in our tool in data basic, what TFCSV.
It was to solve a real-world problem.
Journalists, nonprofits, artists, are increasingly making use of data from the Web.
That comes in CSV form.
They don't know what a CSV even means.
When they do get a CSV, they're like, what do I do with this?
I have a spreadsheet now.
What's the next step?
You are probably all R people.
You know with R you can run the summary command and quickly get a sense of what are the scope
of the data, the variables laid out and so on.
But if you're a new learner, WTF do you do with the spreadsheet?
So the other thing here is with WTFCSV, 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 WTFCSV characterizes your spreadsheet.
Similar to the summary command, but in visual form.
And starts to show you a picture of what is going on with your CSV.
Starts to support that initial data exploration process.
But it has an irreverent name to communicate that the process of discovery can be fun,
it's okay no not know what's going on with the CSV file.
And data that's fun and culturally utilized.
In the U.S., sample data from UFO sightings.
Which is fun data.
And then, for example, for Portuguese speakers, Brazilian soccer results and Portuguese baby
names.
So a final way tools are inviting, they can relate to the backgrounds and context of your
newcomers.
So, for instance, night Labs story map tool has examples linked from the home page that
show their maps in action.
They show published maps in action, published for professional journalistic outlets.
Communicates two things to the learners.
First, gives the tool credibility.
It's robust enough to be used in the field by professionals.
And secondly, shows highquality 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.
Quickly through the last one.
>> They can both get messed up the same way.
If the vessel they're cooked in is dirty, if the mixture is impure, 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 eventual your nice clear glass looks like a shower door.
>> Comparing peanut brittle to the manufacturing process for glass.
This hit home for me, my wife does stained glasses.
So I know a lot about both processes.
Alton Brown, like us and 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, but do it in a way that
makes sense, that's helpful, and in a way that helps someone that's a novice get started
with the knowledge that makes sense.
And hold their hand to know the language that's deeper and need to know if they want to learn
something.
So a quick example.
Quick one around raw.
They generate more complex visualizations with D3 that you can't do in Excel without
coding.
They have sample data, it's wonderful.
My key thing, at the bottom.
Downloads.
You can get this out in an embeddable form.
But also grab the SVG.
Take that into illustrator with the graphics department and tweak and modify as you need.
Not just the quick and easy way to use it, they also give you the way that lets you use
it inside your existing chain for images and graphics and tweak it as you need to.
>> Okay.
So whether you love or hate infographic, they are often the first step that a nontechnical
newcomer takes towards making data visualizations.
So we found that folks in nonprofit organizations, libraries and education in particular love
info graphics.
They're often using tools like Piktochart, Venn gram, to create illustrations.
So the challenge with an infographic tool for data, is they don't introduce terminology
or process around working with data and 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 were talking about here with not hiding the technical language.
Or using cute language to obscure them.
That's the first problem I have with user interfaces.
I know the term, but they have hidden it from me and made up a cute new term.
So explaining technical terms in situ.
We do this in basic by putting a question mark next to any technical terms.
This is an example from connect the dots.
Introduces basic principles of network analysis.
Use terms like nodes or edges or centrality, you can hover over these and get a very short
definition in nontechnical language of what that means.
And then learners, ideally, once they understand those terms, they have those terms sort of
under their belt with the simple tool, they can then graduate to more complex network
analysis tools like graph commons or GEFFE or 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 selfcritical.
We're weak there.
We're not pointing at those tools.
I don't want to hold up our example as we have done all of this and do what we did.
Not at all.
As we talked about, that's the playground for trying it out.
There's lots of gaps we're finding and discovering as people are using the tools, we're trying
to fill.
But as you take a step back, 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 a way that you get sort of a chain of working with a tool
to learn something and helps them learn how to graduate from this novice use to the greater
use.
>> I hope to get through this without coughing.
In fact, we would argue that designing for learners is more important than designing
for learners� >> users
>> Designing for learners is in fact more important than designing for users.
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 doing visualization professionally.
All of us who do this work Professionally consider ourselves newcomers to spaces.
Whether a newcomer to D3, map making, 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 your newcomers.
When is visualization an 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 the 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 the visualizations instead
of being intimidated by it.
Thanks.
>> Thanks.
[ Applause ]
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