We reflect on our experiences arising from a recent computer science graduate class about data feminism, during which we explored the idea of being data detectives. In this report, we explain what we mean by Data Detective as an active approach where we, as individuals, could approach the underlying questions, as suggested by D’Ignazio and Klein “Data science by whom? Data science for whom? Data science with whose interests in mind?”. By connecting individually through personal reflection, data literacy, and critical engagement, our goal is to inform and inspire those who are interested in integrating similar methods into their classes.

As our society continues to evolve, more and more of the information we need is stored as data, and many of these repositories are growing and becoming what we refer to as Big Data. In this process, data becomes more challenging and less accessible to us as individuals. We, as visualization researchers, work on the creation of visualizations as at least part of the solution to this problem. However, much of our data is still not visualized, and even when it is, individuals still often find it challenging to understand. How do we cope with this? How do we teach our students to cope with this continually expanding problem? 

In our data feminism class, we introduced concepts such as visual variables, physicalizations, assumptions about knowledge development (e.g., Positivism and Interpretivism), along with reflection and discussion on reading the book Data feminism. We then explored developing an active practice through which we would document our investigations of both qualitative and quantitative data under various themes. We now term this active practice as being a Data Detective. 

Our concept of Data Detective is modeled on detective work in a more general sense, where a person uses coherent, time-based, record-keeping of their activities to gain a better understanding of that which they initially do not know but want to understand. Thus, to act as a Data Detective is to discover and conduct purposeful, documented, and reflective actions needed to gain access to the desired data. This detective work ideally results in access to the desired data, an understanding of the data, and the detective process involved. 

The term Data Detective appears in various contexts, making it important to clarify our specific approach. Unlike children’s books that suggest counting objects (like red cars versus white cars), or Harford’s statistical literacy guide with its ten rules for making sense of statistics, or visualization workshops for children by providing a gamified sense of accomplishment. Our approach also differs from Inselberg’s multidimensional data detective work, which focuses on analyzing existing visualizations, and from data activism approaches that emphasize community engagement.

We visualized our investigative approaches as journeys: beginning by defining a critical problem or question, identifying gaps (“What’s missing in the picture?”), searching for data, and confronting barriers—missing, partial, or deliberately obscured datasets. Each step provided unique insights into the relationships between data, society, and power dynamics.

Examples

Throughout the semester, students undertook diverse projects with strong societal relevance, including topics such as gender bias in politics, barriers faced by women in entrepreneurship, the functions and ideology of pockets constrained by historical gender roles, and gender representation within STEM academia. 

One student examining women’s representation in political institutions vividly illustrated the practical challenges of data detective work. Initial exploration quickly highlighted systemic data gaps as key datasets were fragmented or unavailable. The student navigated through a frustrating landscape marked by opaque official sources, partial records, and silences. Despite challenges, this data detective journey offered significant emotional and intellectual rewards. The student discovered patterns of marginalization, for instance, women are frequently relegated to peripheral roles rather than core decision-making positions. Each painstakingly gathered dataset provided clarity about structural inequalities. Ultimately, the effort became a tangible act of resistance against invisibility and marginalization.

Another student explored the challenges women encounter in achieving tenure in Canadian academia. Initially optimistic, the student encountered considerable barriers, including incomplete or outdated datasets and inconsistent categorization across institutions. Interviews became essential to fill these gaps, highlighting how data detective work can require alternative methods beyond computational data collection. The journey revealed systemic biases: women disproportionately assigned tasks correlated with lower job satisfaction and hindered career progression.

Working with both big data & personal data

As we moved through the process, we found ourselves blending two approaches to data visualization that are often kept separate: working with big data and working with personal data. Big data showed up in the external datasets we chose to investigate, such as government records, institutional statistics, or public health databases. These are the kinds of large-scale, structured data commonly associated with the term big data.

On the other hand, personal data and visualization came into play as we reflected on our own experiences navigating these data landscapes. By documenting our paths through note-taking, diagramming, and visualizing our steps, we deepened our understanding of the datasets themselves and uncovered what was missing, what was hard to access, and where our questions should lead next.  

Central to our pedagogy was encouraging students to critically reflect on their data practices. We structured reflective exercises to surface the implicit power dynamics in data collection and usage. Students were prompted regularly to question: Whose data are we using? Who collected it, and for whose benefit? Who controls access, and how does that affect analysis?

This reflexivity deepened our critical engagement, enabling us to overcome technical challenges and interpret the implications of their findings from the data.

We suggest one possible pathway to actively take on the role of being a Data Detective:

• Initially clarify what one is looking for—this is before one has the data.
• Develop a timeline starting from the current moment, which will track the process by which one gains or loses access to the data.
• Choose a currently promising direction to find more information (could be: ask a person, search on the web, go to an institution, etc.)
• Collect and reflect on the information collected, filling in one’s timeline, with data, facts, responses, including emotional and frustration level responses.

Actively conducting Data Detective projects in our class, where we used personal visualization of our detective process to teach us about both institutional and personal data, whilst revealing many factors about our society. Each data point gathered and each visualization created represents a small act of making the invisible visible, contributing to more equitable and inclusive understandings of our complex social world.

Acknowledgement

We thank our colleagues and reviewers for their thoughtful comments. This research was funded in part by NFRFR-2022-00570 (A Co-Design Exploration), NSERC Discovery Grant: Interactive Visualization RGPIN-2019-07192, and Canada Research Chair in Data Visualization CRC-2019-00368.

The post Exploring Data Detective Practices as a Class Activity appeared first on Nightingale.

“I’m an Engineer,” one person shared.
“I’m a sophomore,” said another.
“I’m a teacher,” added the next.

Then there was a brief pause before the next participant spoke.

She said, “I’m a Homemaker…but I’d like to learn data.”

That moment of hesitation likely held the weight of self-doubt, vulnerability, and courage. And as it turns out, she became the star of the class.

I wanted to share one of the projects we did, along with her thoughtful take. It might inspire others stepping into data for the first time.

Making data relatable

When I was learning data science, I often struggled with datasets to which I couldn’t relate. I still remember the Iris flower dataset—used to teach clustering. I had to Google what “petal” and “sepal” even meant!

It felt like I was learning data science and botany at the same time.

So, when I teach, I make it a point to choose datasets rooted in the learners’ everyday lives. Since this course was held in a mosque, we used Prayer Time data.

Here’s the snapshot of the Islamic Prayer time from Fremont, California for the month of May.

Problem formulation

Problem formulation is a very important step in analysis. We need to identify what we want to solve before starting to churn the data. This helps us understand if the data we have in our hand is useful or if there’s an additional one, we need to add to tell the story.

I find the below template to be useful

I want to do (this)_______________________
in order to achieve (this)_________________

Everyone came up with their own direction. But one response stood out.

Our homemaker student said: “I want to understand how prayer times change so I can guide younger kids on which days they might try their first fast during Ramadan.”

Beautiful.

Adding a layer: Weather

Once you have defined the Problem Statement, it can further help to understand if and what additional dataset could be useful in answering the question.

She went a step further and asked, “Wouldn’t temperature matter too? Maybe kids can start with cooler days?”

She merged prayer times with temperature data—a thoughtful touch that made the analysis more interesting and useful. You would want to avoid hot days.

Data transformation

Next, we calculated the fasting duration for each day, then layered it with weather data.

Our goal? Identify the optimal days—shorter fasts, milder weather—for kids who are fasting for the first time. Something like below:

This simple analysis helped us identify the best day we could suggest for a beginner to fast.

The goal was to make data feel accessible—and using a dataset the audience was already familiar with made a big difference. It sparked their interest, and it was amazing to see them not only define their own problem statement but also identify additional datasets they could use to explore it further.

Your turn to play

I hope this gave you a glimpse into how a homemaker became a data storyteller—simply by using everyday moments and data from our daily lives.

In my book Drawing Data with Kids, you’ll find many more simple, playful activities that families can do together to explore data, ask questions, and learn in a fun, hands-on way.

Because learning data doesn’t always need a screen—just a little curiosity and a lot of crayons.

The post From Homemaker to Data Storyteller: Lessons from a Local Analytics Class appeared first on Nightingale.

The assignment

Students had their choice of subjects, but we explored several articles on racing bar charts, including “Bar Chart Races Are Everywhere. Here’s Why Some Data Viz Experts Hate Them.” and “Bar Chart Races: Short on Analysis, But Fun to Watch” discussed whether racing bar charts could successfully visualize serious data stories or if racing bar charts should primarily be viewed as entertainment or a “sugar rush.”

I don’t think we ever came to a definitive agreement, and students selected a range of subjects—from popular television shows to the location of forest fires to book publishing trends. Of course, what is a “serious” subject depends on perspective. To someone who just likes to read a novel now and again, publishing trends might not be a serious subject. To someone planning a career in publishing, book trends and sales are serious subjects.

I learned a lot from watching the racing bar charts and found the information presented easy to digest and remember. I had no idea how much The Lion King had dominated Broadway. I was reminded we had bad taste in television in the 80s (seriously, why was The Dukes of Hazzard so popular?). I learned a lot about the evolution of video games as well.

The technology

We used Excel and Flourish, and I told students that the technology would be the easy part of the assignment; finding the story would be the challenging part. In the end, not all students agreed with me, but I think students did a great job exploring both platforms.  No one had previous experience with Flourish, and three or four students were familiar with Excel. While not everyone enjoyed creating the spreadsheet, most students found the technologies relatively easy to use. Flourish’s tutorials are top-notch (and include one on creating cumulative data in Excel), and I created some of my own as well.

Some specifics

Students had a week to research and complete their racing bar chart. Everyone had to include a minimum combined 20 rows and columns of data (some went well beyond this—one project included over 240 rows of data). Everyone needed to include categories—to make the chart interactive—and everyone needed to include captions. Captions were the biggest issue, I think. Some students were frustrated with the options for placing the captions (and ended up removing them), and I’m not certain they always added much to the chart. Everyone was also asked to make their data cumulative—just to learn how to make data cumulative in Excel. Finally, everyone had to write a couple paragraphs about the story they were trying to tell and about their process.

Most students said they would use Flourish again if they had a project that required charts or other types of data visualizations. Overall, I think most of the racing bar charts were well done, and I was particularly pleased with students’ creativity and adaptability. Many students had to adjust their topics based on the data they could find. 

Two students volunteered (or perhaps more accurately said yes when I asked) to share their work. Kayla Bogan’s chart explores Broadway theater and shows that since 2000 The Lion King has made more money than any other Broadway show. Both Hamilton and Wicked have impressive numbers as well (particularly given that they debuted after The Lion King). Lily VanMiddlesworth’s chart takes a look at some of the best-selling books from 2017 – 2023 and provides insight into how much BookTok and other online social platforms have influenced book sales.

“Highest Grossing Broadway Shows” by Kayla Bogan

Exploring the evolution of Broadway over the last 20 years brings a sense of nostalgia. Through the years, cultural changes have impacted Broadway themes and narratives showcasing the differences in time. In the early 2000s, classic Broadway shows like The Producers were the powerhouses of Broadway, along with Disney classics like The Lion King and Beauty and the Beast. When the mega-musical Wicked premiered in 2003, it quickly became the top competition for The Lion King and remained one of the highest-grossing shows of all time.

In the 2010s, there was an uptick in jukebox and movie musicals, bringing a new era to Broadway. When Hamilton, an innovative hip-hop musical about American history featuring a diverse cast, premiered in 2015, it quickly became a cultural phenomenon. Hamilton then became a favorite behind Wicked and The Lion King.

However, when the 2020 pandemic hit, Broadway went dark for the longest time in history, losing billions of dollars in revenue. Fortunately, by 2022, Broadway was back in full force, with The Lion King still leading.

Overall, exploring the different categories of Broadway shows, compared to their respective years and grosses, helps tell a nostalgic and informative story of shifting preferences, innovation, and resilience over the last 20 years of Broadway.

Some of my biggest challenges of the project were making the data cumulative and ensuring everything fit in the Flourish chart. At first, it was a little confusing, but after troubleshooting, I overcame the challenges and completed my racing bar chart. Overall, I’m happy with how my chart turned out. I think another approach could have been exploring overall tickets sold instead of total grosses. I’m curious to know if the results would have been different. The Flourish platform was fun to play around and work in, and I would love to incorporate it into future projects.

“Best-Selling Books in the U.S. 2017-2023″ by Lily Vanmiddlesworth

The American book market has changed dramatically over the past decade, evolving from a business once heavily dependent on personal relationships into a game of digital savvy and nimble marketing. The 2000s saw books and book series such as Harry Potter, The Hunger Games, and Twilight rise to fame, delighting fans of fantasy, action, adventure, and romance. Though technology changed the publishing game, readers’ obsessions with well-crafted fantasy and romance continue to strike the upper end of the charts. Books are still flowing off the shelves, whether said “shelves” are in physical, Kindle, or audio form, and a good part of it now has to do with TikTok and Instagram where users can find and pitch to their niche communities in BookTok and Bookstagram.

While this chart only follows a select number of books from 2017 – 2023, it is obvious that, with the exception of Dr. Seuss’ Oh, The Places You’ll Go and Prince Harry’s Spare, women are leading the charge. Colleen Hoover topped the charts in 2022 and 2023 with her romance-packed novels It Starts with Us and It Ends with Us, showing how the modern market is a place of possibility where almost anyone with a voice can garner success and find a group that will eagerly await for the next page. 

As the chart visualizes, adult fiction, romance, and fantasy took readers by storm. Well-crafted fiction that follows empowering women along with authors who are not afraid to break the rules and explore what it means to be human through adventurous and fantastical worlds and circumstances seem to be the keys to success in today’s marketplace. Fantastic fiction is also here to stay, and these risk-taking authors are proof that anyone can be a bestseller if they engage with modern tools.

The Excel set-up and data collection were a big struggle, as many platforms that track book sales require payments and subscriptions. I love the platform Flourish! It is easy to use and incredibly helpful. If I were to work with it more, I’m sure my love for its abilities would only grow.

Final thoughts

Overall, I was pleased with the assignment and what the students created. Racing bar charts may be a sugar rush, but they are fun, which I think makes them a great way to introduce students, particularly students who aren’t studying subjects that traditionally include a lot of data-specific courses, to data and data storytelling. I hope having a (primarily) positive experience with the racing bar charts will encourage students to work more with data in the future and to think about other ways data can be used to tell a story.

The post Racing Bar Charts: An Experiment in Interactive Storytelling appeared first on Nightingale.

Our investigation of what defines a successful visit to a digital science center revealed several key factors for impactful school visits involving interactive data visualization. Immersive encounters, such as full-dome movies, provide unique perspectives about vast and microscopic phenomena. Hands-on discovery allows pupils to manipulate and investigate data, leading to deeper engagement. Collaborative interaction fosters active learning through group participation. Additionally, clear didactic connections to the curriculum ensure that visits are pedagogically meaningful. 

We propose a three-stage model for designing school visits. The “Experience” stage involves immersive visual experiences to spark interest. The “Engagement” stage builds on this by providing hands-on interaction with data visualization exhibits. The “Applicate” stage offers opportunities to apply and create using data visualization, advancing active participation in science. A future goal of applying the model is to broaden STEM reach, enabling pupils to benefit from data visualization experiences even if they cannot visit physical centers. This approach will incorporate digital tools seamlessly into classroom practice, making science education more accessible and engaging for all students.

What is the purpose of school visits to science centers?

For more than half a century, science centers have become an important vehicle for science communication to the public and school pupils. A premier example in San Francisco is the Exploratorium conceived by Frank Oppenheimer in the 1960s. School visits to science centers are assumed to help increase pupils’ interest in STEM and contribute to lifelong learning. Typically, science centers feature hands-on and discovery learning approaches in attempts to spark curiosity and interest in science. The explosive growth of computer science and technology during the last decades has enticed a transformation in science communication towards the emergence of more digital and interactive exhibits, often based on data visualization. The educational and outreach landscape needs a fresh lens on how today’s data visualization technology can support and boost learning and interest during school visits to modern science centers.

What is a digital science center?

Contemporary science centers integrate interactive technologies such as large-scale dome displays, touch tables, virtual reality, and augmented reality to create immersive and engaging learning experiences. Data visualization can reveal complex and otherwise unobservable dimensions of reality, such as entities that are too massive, too small, or phenomena that are too fast or too slow to be perceived with the naked eye. Interactive data visualization can provide visitors with opportunities for hands-on exploration of the same data as scientists. For example, consider the interactive exploration of a human body by conducting a virtual autopsy—interacting with a true digital replica. 

The shift towards digital and interactive data visualization also redefines the boundaries of science centers as not only physical locations but also as virtual spaces that can extend into classrooms through internet and cloud-based exhibits. For example, we have brought computing intensive interactive data visualization such as the interactive human body into classrooms through cloud-based solutions, providing pupils with a one-to-one exploration of science center exhibits. Such remote access can foster much sought-after integration between school visits to centers and classroom practice in novel ways, and where exploration can continue post-visit in the classroom, or from anywhere in the world. 

An example of a public space containing many such elements is the Visualization Center C located in Norrköping, Sweden. The center provides experiences with digital interactive visualizations for visitors to perceive, comprehend, and uncover scientific explanations about our complex world. As part of a dynamic hub that interconnects research, innovation, and outreach, the center offers a range of school programs, with more than 100 classes or 3,000 pupils visiting the center annually. 

While pupils and teachers often expressed enjoyment when visiting this science center, we desired a more in-depth and systematic understanding of what factors define a successful visit. Over three years, and as part of this pursuit, we employed a suite of observations, surveys, and interviews to identify success factors that make data visualization impactful during school visits to a digital science center.

What are examples of success factors? 

Our research has discovered several key success factors that teachers, pupils, and science center educators believe are crucial for a successful visit. While many of these are related to broader aspects of school visits to science centers and museums, the following specifically focus on the integration of interactive data visualization: 

• Immersive encounters like viewing a full dome movie offer unique encounters with the macroscopic (e.g., deep space) and submicroscopic (e.g., molecular interactions), which emphasise relative size and presence. Pupils typically state “getting a feeling for the vastness of space” and “how small humans really are.” 
• Hands-on discovery through interactive manipulation and investigation allows for exploration and making predictions. In such cases, pupils prefer to engage in their own hands-on explorations in addition to only passively receiving information from a guide or educator. 
• Collaborative interaction provides a social dimension where pupils’ group and collaborative participation with interactive data visualization fosters active learning. Such observations are also typically reinforced by science center educators, which in turn has implications for future exhibit design. 
• Didactic connections where clear links to curricula and integration with classroom practice are essential for embedding the teaching context. Explicitly developing these connections allows the visit to transcend beyond a mere “fun”, “different”, or “out-of-class” event.

How can we model school visits of the future? 

Uncovering a range of success factors has informed a three-stage model for how school visits to digital science centers could be designed for different pedagogical and communicative purposes. An overview of the model by specifically interpreting its implications within the context of interactive data visualization in a digital science center is as follows:  

1. “Experience” stage – view a fulldome 3D movie that presents an immersive visual experience of complex phenomena related to STEM. An example could be a visual portrayal of how data visualizations are applied in visual effects. This could be followed by a guided tour of data visualization exhibits depicting how visual effects are created and used in film and computer games. The experience stage is coupled with visual storytelling techniques that stimulate interest and curiosity about STEM topics, which provide a foundation and motivation for the second and third stages of the model. 
2. “Engagement” stage – specifically builds on “experience” by integrating opportunities for hands-on interaction and exploration of data visualization through different exhibits. As an example, such engagement could require critical visual literacy skills, such as learning about different forms of representation, and being able to discern strengths and limitations of diverse visualization.
3. “Applicate” stage – denotes opportunities to apply and create through data visualization. Possibilities include tasks such as confronting a real-world problem, or creating a novel solution where pupils take ownership of what questions are pursued. Examples could involve performing data visualization experiments—such as visualizing climate change trends—by drawing on the unique interactive technologies of the science center. Importantly, this stage is exemplified by pupils “doing science” rather than merely “learning about science”. In doing so, pupils make informed decisions about data selection, collection, and visualization, which in turn also complements the development of visual literacy skills.

How can the model inform design of data visualization experiences?

We are of the opinion that the three-stage model can help design and implement pedagogically successful school visits to science centers and museums. The model could also facilitate the explicit linking of school visits to STEM interest, curiosity, and learning. We are currently implementing the model in several research projects wherein it will be further validated, developed, and refined. 

In one example, we tested the applicate stage in the context of entrepreneurial learning with data visualization technology in a joint development project between the science center, the Swedish Science Center Assocation, and the Swedish Agency for Education. The preliminary results showed an increase in pupils’ entrepreneurial skills in relation to digital competence, collaboration, and self-efficacy. Currently, we intend to incorporate all three stages of the model in an outreach project termed “TellUS—the talking planet” that renders an interactive spherical visualization of Earth in combination with AI-supported dialogue. The model will inform an outreach program aimed at increasing science capital of pupils by developing replicable educational activities aimed at 7 – 14-year-olds across Sweden.

We see the ultimate importance of our work as broadening the reach of STEM education, allowing pupils who do not have the opportunity to visit physical centers to nevertheless benefit from the potential power of data visualization experiences.

Further reading

Visualization Center C: https://visualiseringscenter.se/en/

The post What Makes School Visits to Digital Science Centers Successful?  appeared first on Nightingale.

Personalized learning paths guided by adaptive visualization could also support individual progress. Despite growing interest in interactive data visualization, there is little research on designing these tools for meaningful integration into teaching about systems thinking. The development of Tracing Carbon aims to bridge this gap and targets junior high school students. Using an iterative design-based approach, we combined systems thinking theory, cognitive learning principles, and carbon cycle knowledge, and involved teachers and students in the design process. 

Introducing Tracing Carbon to teachers and collaborating on its classroom integration revealed that digital tools must align with educational goals. Our work demonstrates how the intersection between design and science education creates research opportunities for enhancing learning experiences. The development of Tracing Carbon paves the way for future research on how students of different ages use visualization and how adaptive learning environments can enhance visual learning environments for STEM education.

Why is thinking about systems important?

Systems thinking is fundamental for understanding complex problems. Addressing twenty-first century global challenges such as digital privacy, world health, biodiversity loss, and climate change all rely on thinking about systems. Indeed, humanity faces the massive task of tackling global warming. To act, we all need to comprehend what is happening—and that requires thinking about how different components of earth systems connect and impact one another. The carbon cycle plays a crucial role in our planet’s climate system, making it a highly relevant and powerful context for helping the rising generation develop systems thinking skills.    

Despite the importance of the carbon cycle, our young students are often presented with the sheer complexities of this system with deceptively simple and traditional 2D static images (see below). Many young learners find it demanding to learn about the carbon cycle using conventional visual tools. This is perhaps unsurprising since established and standard visual representations do not necessarily communicate the intricate and dynamic features of the carbon cycle, such as traversing multiple temporal and spatial scales, constituting several subsystems-within-systems, and incorporating multiple interconnected processes. Additionally, these conventional visualizations are not always paired with the scientific data that represents the status of the global carbon cycle and its implication for environmental challenges.

Could interactive visualization foster systems thinking?

In the visualization community, the term visualization is usually acquainted with computer-based visualization systems that augment human decision-making capabilities by providing visual representations of data. Coming from a science education background, we adopt a broader perspective on visualization, by considering it as the representation of information in visual formats such as images, diagrams, or charts. 

The carbon cycle represents an abstract conceptual framework in science education that requires grasping multiple layers of information, including system components and dynamic relationships between components at microscopic and macroscopic organizational levels. We hypothesize that a learning environment that represents this information through multiple interactive visualizations can facilitate learning about the complexities of the cycle. Additionally, we believe visual representations of scientific data about the carbon cycle—such as atmospheric carbon dioxide, earth temperature anomalies, and carbon flux—can enrich this learning environment and provide guidance towards evidence-based insights on this topic. By enabling users to dynamically explore and visualize components and relationships of a complex earth system such as the carbon cycle, and motivate consideration of the influence of human impact, this learning environment can foster the development of systems thinking abilities. Furthermore, we believe that as students gradually build their understanding about how earth systems work, personalized learning paths guided by adaptive visualization could support their individual learning progress. In parallel, teachers play a vital role in providing support as students navigate their learning trajectories with such an interactive visual environment. Our work currently pursues the following questions:

• How can we create interactive data visualization tools to help pupils understand how earth systems work?
• What happens when pupils use such visual tools to explore the carbon cycle? 
• What are the potential benefits of interactive and adaptive visuals for facilitating systems thinking?

To explore these questions, we apply mixed method endeavors that incorporate data visualization, interactive and visual design, adaptive learning environments, science education, and educational psychology.

How can design intersect with science education in developing Tracing Carbon

Despite increasing inroads into the value of interactive data visualization in science education, there remains a significant gap in research on how to design these tools effectively and integrate them into teaching practice. The development of Tracing Carbon exemplifies a pedagogical effort to bridge this gap, highlighting how design intersects with science education. Targeting junior high school science classes (students aged 13 – 16), we designed an adaptive and interactive learning environment to support systems thinking skills about the carbon cycle. We used an iterative design-based approach informed by theory, teachers, students, and a research team that included educators, designers, and programmers. 

We combined systems thinking theory, cognitive learning and carbon cycle knowledge to create the components of the learning environment. We used a hierarchical systems thinking model as a scaffolding framework to develop sequences of learning support for Tracing Carbon, and integrated various learning objectives, tasks and modules to help students understand the carbon cycle. Teachers and students are key in designing learning tools, and science teachers and students were involved in the design process. The research team used their collective insights to guide and refine the design through various focus group meetings, individual interviews, and classroom interventions.

The design process led to Tracing Carbon, an adaptive interactive learning tool consisting of tasks and quizzes across progressive modules. The learning experience starts by exploring the forest carbon cycle by covering carbon pools and transformation processes. Then it focuses on the carbon cycle at the global scale and ends by exploring scientific data on anthropogenic effects on the carbon cycle. Students can engage with Tracing Carbon through various interactions (see below), that include dragging and dropping items to complete images (A) and drawing and completing arrows to “trace carbon” through various sub-cycles (B).

While progressing through the visualization environment, students are presented with quizzes and tasks representing various levels of systems thinking skills, designed to stimulate reasoning and problem-solving abilities. While engaging, the adaptive characteristics of Tracing Carbon personalize the learning experience by adjusting the task and quiz difficulty and quizzes according to students’ real-time performance. Additionally, various forms of visual feedback strive to validate students’ answers and guide them through the learning experience.

What happens when Tracing Carbon is used in the classroom?

Integrating digital tools in classrooms involves much more than merely making them available to teachers and students. It’s about aligning them with educational goals to enhance learning processes and outcomes. Given the diversity of data visualization tools and educational settings, there is no universal recipe for integrating a digital tool into every classroom. However, observing how a specific tool like Tracing Carbon can support systems thinking can illuminate key considerations for implementing similar educational tools in teaching practice. 

To date, we have begun introducing Tracing Carbon to teachers and collaborating with them while they integrate it into their teaching. Perhaps unsurprisingly, teachers seek tools to streamline their workload while enhancing students’ learning. A general recommendation is to complement digital tools with additional resources to maximize pedagogical effectiveness. In terms of student experiences, we observed that they generally enjoy using the digital resource and seem to show increased engagement. It follows that harmonizing teaching activities and digital resources is of high pedagogical importance.

Future directions for enhancing systems thinking with data visualization?

We have tested potential principles for creating interactive diagrammatic visualizations that can help learners grasp complex and interconnected science concepts. In this way, we demonstrate how the intersection between design and science education provides a research space where visual tools are crafted with the intention to enrich learning experiences. The developed Tracing Carbon tool is intended to enhance systems thinking as a key aspect that contributes to notions of environmental literacy and informed decision-making. Such integration also provides prompts for how systems thinking could be approached in STEM domains at large. Furthermore, learning and reasoning about fundamental STEM concepts through a visually communicated environment could compensate for differences in pupils’ language proficiency. Lastly, the fact that such a visualization platform can be used in any learning setting equipped with a computer and internet ensures accessibility for all pupils and teachers. 

The multidisciplinary collaboration undertaken in this project sets the stage for several future directions. These could involve comparing how students of different ages use data visualization platforms to develop systems thinking, or how such abilities develop over time. The adaptive visualization component of the work also provides insights into how emerging AI can enhance visual learning environments for STEM—in continuing to seek interventions for equipping the next generation with essential knowledge to solve urgent global issues. 

Acknowledgements

We heartily appreciate the contributions of our collaborators in the project team Måns Gezelius, Gunnar Höst, Marta Koć‑Januchta, Jonas Löwgren and Lena Tibell. This work is supported by the Swedish Research Council (Vetenskapsrådet, Grant 2020-05147).

Further reading

Conducting Educational Design Research, Susan McKenney and Thomas Reeves

Development of system thinking skills in the context of earth system education, Orit Ben-Zvi Assaraf and Nir Orion

Students’ conceptions of the carbon cycle: identifying and interrelating components of the carbon cycle and tracing carbon atoms across the levels of biological organisation, Katharina Düsing, Roman Asshoff and Marcus Hammann

The fabric of visualization, Elisabeta Marai and Torsten Möller

Visualization analysis and design, Tamara Munzner

The post Tracing Carbon: Visualization for Systems Thinking appeared first on Nightingale.

Transforming data into understandable and honest information is a matter of societal survival in this world of fake news and “alternative facts”. The Master in Data Design by Elisava, Barcelona School of Design and Engineering is a hands-on experience harnessing data for crafting information and making a change.

The following five projects were completed by individuals and groups of students who have passed through this program.

“shhh…” | Students of the Master in Data Design (2022-2023)

Noise pollution is considered by the WHO (World Health Organization) to be the second most harmful environmental factor for health in Europe. 

The exhibition “shhh…” was an effort to highlight the conflict around noise pollution in Barcelona, promoting reflection and a critical view on the subject. 

“shhh…” was built over the analysis of previously unpublished data gathered from official “requests for access to public information” that included citizens’ complaints to the police (Guardia Urbana) for excessive noise or the data gathered from the network of noise pollution monitors of the Barcelona City Council.  

It was conceived as a multi-sensory experience that included audiovisual, sound, and graphic pieces—such as sound installations or cartographic video essays—and invited visitors to reflect and publicly debate in order to demand public policies that enable a healthy acoustic environment. 

The exhibition, developed by the students within the course Data for the Common Good, was directed by the architect and exhibition curator Olga Subirós with the collaboration of the urban planning agency 300,000 km/s, was held on April – May within the program of the Model Festival 2023, Barcelona’s Festival of Architectures. 

Students: Natalia Blay, Andrea DiLeo, Enrique Peralta, Sebastian Perez, Manuel A. Ortiz, Judit A. Zanelli, Debbie Zamd

View Project

“Bodybuilder” | Carla de la Torre

We live in a “fit” society, where gyms are temples of transformation towards a globalized stereotype. The cult of the body plans and controls our daily routines and ends up shaping our identity, fueling the fitness industry, which is only growing. 

At the same time we are in the age of information, of data; it is said that having information is power, and we can apply it to this fitness paradigm as well. We design our body in gyms by entering data (15 repetitions of squats for 3 sets, 100 grams of rice…) to build the ideal body and at the same time gain individual power. Beauty ends up being something measured, weighed, equated, and compared.  

The bodies that visualize this in the most extreme way are bodybuilders. Mass body, corporal and data body, representing a bodily ideal of power. 

Carla de la Torre used modified workout machines to invite the public to experience this datification of the flesh through a series of pieces that toyed with self-image, repetition, data rituals and unreal beauty canons. 

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“The World in a Minute” | Students of the Master in Data Design (2022-2023)

“The World in a Minute” was an installation that used data and sound to represent the state of the world today. Conceived as an experimental object-based sonification, it was created by the Master in Data Design ’22 – ’23 students with the guidance of Domestic Data Streamers to be part of the Sónar+D Festival. 

The aim of the project was to create a device that represented the state of the world in any given minute—compressing time and using recognisable, everyday objects to visualize our impact in the world telling a story. From the mundane to the cataclysmic or from the emails we write to the earthquakes we suffer, this piece creates a multi-scale representation of our world – a data based soundscape – that gets data closer to the participant, overcoming the data numbness of today’s world. 

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“Detrás del miedo” | Maria Moreso

Throughout our lives, women, or people with a vagina, will visit a gynecologist multiple times. The Public Health recommendation is to do so every 1 – 3 years, depending on individual circumstances; however, more than 50% of women will not follow this recommendation. It is not news that almost no woman like to go to the gynecologist. Unfortunately, feeling anxiety, fear, vulnerability, embarrassment, and discomfort are part of the common experience of many women when visiting this specialist. But why do we experience it this way? 

Lack of sensitivity and empathy towards a patient in an extremely vulnerable position, judgment, infantilization, paternalism, lack of scientific progress, misinformation, taboo, or normalization of female pain are some of the subtle forms of patriarchal violence that we women suffer in relation to our intimate health. 

This work shed light on what’s Behind the Fear re-signifying the processes and instrumental that belong to the gynecological public health system. Prescription notes, speculums or the performative act of waiting make personal the data of a seemingly aseptic and bureaucratic power structure. 

“War Owned” | Jordi Farreras

War is something that, as humans, belongs to all of us, but we don’t connect with it. We see those numbers, but we don’t care about the people behind that war. We don’t care how much we are contributing to it. Why are we still having wars if violence is a tool we no longer need since we can converse? 

Data about war should be communicated more adequately. Morality, Money and Love are the three pillars of this project:  An Augmented Reality Exhibition developed through the city of Barcelona, and three different books that have been designed to compile research and curious data to highlight war in a way that we can better understand it. 

View Project

The post Five Projects: Highlighting the Work of Elisava’s Master in Data Design appeared first on Nightingale.

And yet, few STEM majors actually have data visualization in their curricula at all (with perhaps the exception of those pursuing degrees in computer science). Higher education typically only offers these students seminars on how to design a good research poster; students are, for the most part, left to pick up data visualization skills here and there along their academic careers.

Grad students, in particular, who generate their own data, really lack curricular support in data visualization and communication skills. Grad students also tend to perform more advanced data analysis and have complex stories to tell with their data. Often they are working with datasets that hold many dimensions, lots of nuance, uncertainty, etc. Learning about data visualization at that level is as much about design as it is about science communication: distilling the key messages of one’s research, and making difficult decisions about what content should be sacrificed at the altar of good design and a clear message. (In contrast, most researchers are typically taught to try and fit as much as possible in the space given, be it a poster or a scientific paper they’re working on.) 

Designing a syllabus to establish data visualization foundations 

Personally, data visualization and visual communication in general has become increasingly important in my work. At Penn State University, I study climate impacts on water resources and planning for the future, which often requires the exploration of large simulation modeling experiments and large datasets with many dimensions. This has pushed me to be more inventive and thoughtful with how I communicate my scientific results. I have seen direct benefits from becoming a better visual communicator in my conference posters or talks. These are skills I want my own graduate students to pick up, but also, as an educator, I felt it important that new crops of students get some formal training on this.

Enter the Data Viz class for Scientists and Engineers, which is an attempt to give undergrad and graduate students in the College of Earth and Mineral Sciences at Penn State a design and communication foundation. Though I have no formal design training, I spent more than a year conceptualizing this class, which I taught for the first time this spring semester. My vision from the beginning was to teach all I would want someone else to teach me when I was in college:

• Some of it was very fundamental to design in general, like use of color and how some color scales match different types of data better than others.
• Some of it was very practical to what STEM jobs entail—in academia or industry. For example, how to save Python figures into scalable vector images instead of rasters, or how to guide your audience through a complex graphic using animations and annotations in Powerpoint. 
• Some of it was just about getting them to be visually creative even if we don’t know how to get there yet with coding or software skills. 

In designing the syllabus, I relied heavily on books (for example,  Storytelling with Data by Cole Nussbaumer Knaflic, The Functional Art by Alberto Cairo, and Better data visualizations by Jonathan Schwabish) as well as other teaching materials I could find online. For example, Tamara Munzner, who teaches Information Visualization at the University of British Columbia, and Amelia McNamara, who teaches Data Communication and Visualization at the University of St Thomas, both share their syllabi online. I leaned on both to structure mine. I also picked up some fun activities from the Data Visualization Society’s Slack channel on teaching, including activities like the one shown below, which gives students a dataset and physical materials to depict it. 

Evaluating and grading the coursework, using a feedback loop model

The 12 students who enrolled were in the physical sciences so most had no prior background on design or aesthetics, nor did they have advanced coding skills to make interactive dashboards or fancy web interfaces. They also didn’t actually want to learn these skills in great depth (that’s why they’re physical scientists!), but wanted to know just enough to be better visual communicators. 

While my students’ backgrounds made planning the course more challenging, it kept the course focused  on just the key skills that are most directly useful to scientists and engineers: coding simple analysis and charts in Python and creating more complex visualizations and infographics in Adobe Illustrator. The goal was to stretch them a little on Python and also introduce them to some practical aspects of using software like Illustrator. 

The main assignments were to develop three “mini-projects,” such as infographics or research posters. The course challenged them to try to present a certain number of variables in their graphics. For example, in their last mini project they had to figure out creative ways to show at least eight different dimensions of a dataset. Even though data visualization mastery is not strictly about how much information one can squeeze into it, trying to balance informational complexity with good design is a very pertinent challenge for our students and graduates. 

Students were provided openly available datasets (like the Gapminder data), but they were also free to use some of their own—particularly if they were already working on research projects as part of their other coursework. 

Another dimension that strongly shaped the class was constructive criticism and feedback during the process of making the visuals. In most of STEM education, students deliver an assignment and receive back a grade, with some instructor comments on what was wrong. There’s little space for just exploring weird ideas or being creative in a way that’s not formulaic. So I wanted to emphasize a growth mindset and give the students a space to explore and try out design ideas in a low-stakes environment before they submitted their finals.

To accomplish this, students submitted a sketch or draft of the current stage of their infographic on a weekly basis. They also had to give critical and constructive feedback to their classmates to get marks for the assignment. We provided training early on on how to give constructive feedback and be a good peer mentor, utilizing the RISE model. For example, oftentimes when students are asked to give each other feedback, they’re tempted to just say “this is good!” but that’s terrible feedback! The recipient cannot use it in any way and nothing really improves. Specific and detailed feedback, even when negative, is much more effective in elevating the classroom community. 

This process turned the classroom into a learning community where every student came to understand that the creative process is messy and iterative—and it is through this iteration that we learn from our audience about what works. Even though the final products were graded on having applied design principles from the class, all other homework was assessed on the basis of showing growth instead of perfection (i.e., demonstrating how they used feedback) and on the quality of feedback they gave their peers. 

This classroom environment was a great introduction to real-life situations, where data visualization practitioners lean on a supportive community as they practice and refine their skills. In other (future) contexts, these students may find that showcasing designs, justifying design choices, and communicating with data, are all part of the visualization process. On several occasions throughout the semester we held “show-and-tell” sessions where all students presented their work to an audience of their peers as well as visitors from the department.

Takeaways from the semester

From conversations with the students, they saw the feedback element of this class as essential to their growth and success. Some said they valued the process because it exposes the stuff we often don’t see in published products: the scrapped ideas and the not-quite-right color schemes that don’t make it through the final cut. 

When reflecting on this experience, I felt that this course design approach allowed for deeper and more meaningful learning, through building a sense of community and belonging. I viewed my course as a tiny learning community, in that we are all in charge of the success of others. This shifts the traditional power dynamic of teacher and student and democratizes it through having a leader with active members. We truly established a learning community that held a shared vision and goals for 15 weeks.

The biggest success, I personally believe, was the grading system. As opposed to grading on quality of their designs, this course specifically rewarded a growth mindset and supporting one another through constructive criticisms and suggestions. This format not only helped students practice using design vocabulary, but also created an intentional environment that gave them permission to fail and to keep refining until they fully implemented all the feedback in their final copies.

In the end, it felt like we all got really close to one another. I loved how open and comfortable students were to express their thoughts (even if critical) about the designs and how they appreciated the importance of self improvement and helping others.

The post Weaving Data Viz Into Science and Engineering Education appeared first on Nightingale.

Now that I am older I am starting to realize my stereotypes of math were wrong. I always thought that, because I didn’t understand it, I would never be able to do it. I am predominantly a creative right-brainer, so it was easier to accept that and spend my time on other subjects (ones I enjoyed more). But that kind of thinking is misguided. 

We like to categorize people as either right-brained (those who are creative and artistic) or left-brained (those who are logical and analytical). It’s a common belief that if a person is dominantly one, then they can’t be the other. This is simply not true. Math is a complex, multifaceted subject that requires a wide variety of skills. In its most basic format, we need to be able to understand the problem and the way to solve it and that has to be expressed through language, whether verbally or written.

But math is more than that.

Take data analysts—they take a set of data and have to ask the right questions so that they can apply data correctly. Then they translate it, making it easy for people and businesses to understand what it means and why it matters and, chances are, they will achieve this with storytelling. Research conducted by Duke University and the University of Michigan has shown that communication between the left and right hemispheres increases when we work on math problems. While it’s true that left-brainers might have an upper hand, this is largely due to the fact that traditional teaching, such as lectures where the students are expected to take notes, are more geared towards left-brained thinking. 

Educators have started to realize that there is a problem with the way we teach math and are trying to come up with solutions. One train of thought is that kids don’t believe they will use it after school, so logically the solution would be applying it to their careers and showing them just how useful it is. However, kids aren’t interested in abstract somedays, they want to be engaged now. The key is to make math fun and engaging; to show the wonder and beauty of it. As role models to children, it’s our responsibility to do just that.

1. Showcase the wonder

There is beauty to math. It’s more than just numbers and formulas—geometry is present in nature and formulas can be found in almost everything we do. The American Mathematical Society created a list of galleries showing beautiful mathematical images. There is also beauty in the way numbers line up and in their patterns, particularly if you think of the Fibonacci sequence.  Fundamentally, math is a means of exhibiting and satisfying curiosity. It’s about asking questions and finding explanations, much like data visualization. There is a wonderment to it, but somewhere along the way we lose that magic. What if we started teaching it like an art form? Instead of looking at it in black and white—we teach it in colors.

And by colors, I mean using colors to separate different processes and numbers. Have the kids draw what they are learning so that they can better visualize and understand what it is they are doing. Bring in music by creating songs about numbers or formulas. It’s already commonplace to use physical objects such as manipulatives so that math becomes something physical for children to touch. By expanding on these creative ideas, students will likely be more engaged in their classrooms. 

2. Invoke the power of storytelling

A large part of the problem is that school often focuses on memorization. Teachers give their students their information, they memorize it for their test, and then it’s gone. Forgotten. Some facts will stick, but most will disappear. Education should be focused on building skills that children will need as adults, such as problem-solving and analyzing. But sometimes memorization is necessary. When it is needed, teachers should consider approaching it through storytelling. Stories are powerful—largely because the good ones leave an impact. They’re memorable because they allow us to connect to ideas in more meaningful ways. Try giving each number and fact a story, write it on a flashcard and see its effect for yourself. By using the emotion and power of narratives, students will better be able to form mental pictures in their minds and retain the intended lesson.

3. Incorporate math into daily life

Kids are heavily impacted by those around them. If they hear negative opinions about math, they are likely to start thinking about it in the same way. If you are a role model to a child, show yourself being confident with it whenever you encounter math. More than that you should encourage them to use math in their daily lives.

When you are at the store, you can ask them to figure out how much some items cost or how long it will take to save up for a goal. Make math relevant. Figure out what children are interested in and find a way to tie math into it. Play math-based games such as chess, checkers, dominoes, or Yahtzee.

4. Avoid reinforcing stereotypes–even inadvertently

Stereotype threat is a serious concern. It’s when we underestimate a group’s ability to do something that they actually begin to underperform. This is especially true for girls. There is a nasty stereotype that girls can’t comprehend math as well as boys. Perhaps this is why only 26 percent of all computer and mathematical positions are filled by women, according to a study published in the Science of Learning.

This stereotype has been proven to be untrue by several studies—one of which was done by Jessica Cantlon at the Carnegie Mellon University in Pittsburgh. She studied MRIs of groups of boys and girls as they watched educational videos and found there to be no biological difference. The difference comes from unconscious messages spread through society. Teachers spend more time with their male students and the girls begin to pick up these cues that math just isn’t for them. Educators, and people influencing children, need to be aware of these stereotypes and make sure that they are giving all children equal access to the same education.

5. Broaden your perception of literacy

Parents tend to put more emphasis on their children learning how to speak, read, and write, while neglecting math. It’s understandable because children have to learn to talk before anything else, with reading and writing following close behind. These skills are essential to communication and are needed before attempting any other type of education. Still, math is just as necessary. It surprised me how much math is foundational to a field like design—ranging from understanding geometry and grids to knowing portions and scales.  

Introducing math early will give young learners a better foundation, helping them down the line. But make math fun and engaging—let it keep its wonderment and magic. I can’t help but wonder if someone had approached teaching math differently when I was younger if I would feel differently towards it. I still don’t enjoy math—and I doubt I ever will—but I now have a healthy appreciation for it and I know just how important it is for young children to feel the same. They need to know that you can be good at both language arts and math—that it doesn’t have to be either-or.   

It’s easy to get wrapped up in labels: of being a creative or a scientist, left brained or right. We don’t have to limit our identity to one thing. Data analysis, a conventionally STEM career, requires creativity in the way they break down data and reorganize it into meaningful and compelling narratives. We should inspire children to do the same, allowing them to be creative without limitation.

The post Five Ways to Teach Math as an Art Form appeared first on Nightingale.

No one ever has declared general education the sexiest anything in the world. 

The differences don’t stop there. Data-related skills are generally considered part of the ever-popular STEM (science, technology, engineering, and math) field and are some of the most coveted skills in the workplace today. General education courses are often considered less valuable than technical or major-specific courses. Students and their parents sometimes question, for example, why someone majoring in business or planning on becoming a physician needs to study Shakespeare or anthropology. 

Still, data and general education could be just the pairing we need to improve data literacy in the United States.

Data literacy and general education defined

Being data literate is tremendously important for just about everyone, but what it means to be data literate is a little harder to determine.  First, there’s the terminology. I’m using data literate, but data fluency, graphicacy, and media literacy are also common terms. Then the definitions–there are many. I think data literacy executive Ben Jones’ thoughts are a nice place to start, in part because his definition focuses on data literacy as a process:

“Data literacy encompasses a wide variety of skills, but ultimately it’s the ability to read and understand data, and also create and communicate it. So there’s a receiving end and there’s also a delivering end. A transmission, if you will.”  

Ben jones

Be Data Lit suggests a different way of looking at data literacy. It provides more of a list rather than a traditional definition. According to the article, some signs of data literacy include “being curious about data” and understanding how to balance data with other things–such as critical thinking. The article ends with this critical point: “Data literacy is evolving and so, if you are committed to being data literate, know that it’s an ongoing journey and you must keep working and evolving.”

General education is a little easier to define, but sometimes it is harder to articulate its importance. General education courses are simply the required–or core–courses at colleges and universities in the United States. General education courses focus on essential, but not industry-specific, skills (often referred to as soft skills). Think problem solving, cultural awareness and understanding, critical thinking, empathy, writing, research, and oral communication. Specific courses might be in subjects like English, psychology, communications, or anthropology. While general education courses can’t be industry specific, they are valuable in that they are the courses all students seeking a bachelor’s degree in the United States take.

Data skills have become—much like what is taught in general education—things everyone needs to know. Despite this, data literacy is often seen as lacking in recent college graduates—particularly college graduates not majoring in subjects like data science or business analytics. Numerous businesses, organizations, and nonprofits are working to bridge this data gap.  Data visualization platform Tableau, for example, launched its Data Literacy One curriculum in September 2020 (followed by Data Literacy Two in 2021). They even have a Tableau for Kids curriculum designed to inspire “kids to explore the data that’s around them every day.” Their programming matches their message:  “We need all hands on deck to bridge the data literacy gap.”   

General education needs to be one of those hands.

While data literacy and general education might seem like an odd pairing, here are three reasons the partnership makes sense.

Reason One: Data literacy and general education are both essential

General education is all about teaching essential skills, and it’s hard to argue that data literacy isn’t essential.  

A simple LinkedIn search validates that the ability to work with data is essential—over 1.5 million job postings on LinkedIn ask for some type of familiarity with data, strongly suggesting that working with data is no longer a discipline-specific skill or something only data scientists or analysts need to know. Instead, it’s simply another form of communication almost all professionals need to be successful in their chosen fields.  Business literature on the importance of data skills has become commonplace. Author and Content Manager, Paul Petrone posits,  “… today, data visualization is becoming an absolute must-learn skill. As all organizations become increasingly data-driven, the ability to work with data isn’t a bonus, it’s essential.” 

A few more examples:

•  A recent Harvard Business Review article opens, “Data skills are now essential for almost every role in every organization. Companies need more people with the ability to interpret data, to draw insights, and to ask the right questions in the first place.”
• An article from MIT cites Piyanka Jain, a data science expert and author, who says, “Everybody needs data literacy, because data is everywhere,” and further claims, “Data is the new currency, it’s the language of the business. We need to be able to speak that.”
•  A post from BusinessWire notes not only the importance of data literacy but also the data gap: “The ability to make decisions from data is the number one skill employers require. But despite its importance, too many graduates are entering the workforce without sufficient data literacy.”

The articles, expert quotes, and surveys go on and on, and they all say the same thing—data literacy is essential in today’s job market.

Many of these same articles and experts go on to say American colleges and universities aren’t doing a very good job preparing students to work with data. 

Reason Two: Data literacy and general education have a lot in common

Data literacy would be relatively easy to incorporate into a general education curriculum because so many elements of data literacy are already taught in general education. 

Industry expert, Bill Shander, outlines four skills necessary to become a “dataviz unicorn.” These skills include being able to analyze data, being able to communicate effectively, having the creative skills to visualize data, and having the technical skills to “pull it off.” 

Analysis and communication are two cornerstones of general education curriculums. Creativity is often another. While software such as Tableau or PowerBI might be too industry specific for a general education course, effective visuals can be created in Canva, Excel, Adobe Illustrator, or PowerPoint—all platforms that are already frequently used in general education courses.

When listing skills needed to become a dataviz unicorn, Shander doesn’t mention empathy, but plenty of other articles do discuss the importance of empathy or humanizing data. Consider, for example, Andy Krackov’s thoughts on persuasive data narratives:  “In my mind, the purest form of data storytelling is when you relate a story about an individual and use data to describe how this issue impacts more than just this one person. A persuasive narrative is one such way to humanize the data, telling an individual’s story from problem to potential solution.”  Imagine an English composition course that assigned a persuasive data narrative instead of a personal narrative. This  easy switch would not only teach everything, or almost everything, learned from writing a personal narrative, but it would also supply the added benefit of incorporating data skills.

Data literacy can be approached in many ways–via specific assignments or more comprehensively. It doesn’t always have to be about technical skills, such as R, SQL, data extraction, or creating really cool visuals. As Laurence Bradford, Forbes contributor and founder of Learn to Code with Me, notes, “Becoming data-literate isn’t necessarily about tools, software, or programming languages. Rather, it starts with a holistic view of how to think about data and what questions to ask.”  

Data executives, Josh Bersin and Marc Zao-Sanders, agree that the missing skills aren’t necessarily the technical ones. Instead Bersin and Sanders’ research found that employees lack “the skills to

•  Ask the right questions
•  Understand which data is relevant and how to test the validity of the data they have
•  Interpret the data well, so the results are useful and meaningful
• Test hypotheses using A/B tests to see what results pan out
• Create easy-to-understand visualizations so leaders understand the results
•  Tell a story to help decision-makers see the big picture and act on the results of analysis.”

This more holistic view of data literacy fits nicely into general education. Asking questions, problem solving, finding relevant sources, and storytelling are all hallmarks of classic general education curriculums. Assignments related to interpreting data, data visualization, and data storytelling could easily be worked into composition, anthropology, psychology, and public speaking courses, and most likely other courses as well.

That said, entire courses dedicated to subjects like data storytelling and data visualization could just as easily be created for general education departments. These courses could not only provide hands-on instruction related to data literacy, visualization, and storytelling, but could also provide a forum to look at the history of data visualization, explore theoretical approaches to data, and read books such as W. E. B. Du Bois’s Data Portraits: Visualizing Black America and Data Feminism.

Reason Three: General Education could use a rebrand

I don’t want to understate the valuable contributions general education courses currently make in education today. Serious industry research, such as that done by the U.S. government, confirms most of what is taught in general education courses is valued by employers. Educators from general education departments would likely also argue that what they teach makes students better and more engaged humans. That said, advocates of general education curriculums (often in this context referred to as liberal arts or the humanities) constantly seem to be on the defensive. Books and articles written on both sides of the debate flood the marketplace and the internet, often with titles like The Washington Post’s 2020 article “Liberal Arts Education: Waste of Money or Practical Investment? Study’s Conclusions Might Surprise You,” but some of the harshest criticisms come from the students themselves.

Just Google “gen ed waste of time” to see the number of op eds published on the subject in college newspapers. Common complaints are that general education courses make up too much of the overall curriculum, thus increasing the cost of a degree as well as the amount of time it takes to complete a degree. Other student critics of general education argue courses simply repeat information covered in high school. As noted in The Atlantic,one particular student tweet went viral (almost 70,000 retweets and over 200,000 likes) after claiming, “Unpopular opinion: general education courses in college are a complete scam for your money to keep you paying for 4+ yrs. If gen ed courses weren’t a requirement, majors really only require 2 yrs of classes. All of high school was gen ed- it’s simply unnecessary.”

While some students may not appreciate general education, study after study does support its inclusion in college curriculums. However, perception is also important and including data literacy in general education courses might help change the perception some students (and, let’s face it, their parents) may have concerning the value of general education course offerings. To be clear, I would never suggest adding something to general education that I didn’t think belonged in the curriculum, but including data literacy provides multiple wins. It’s a great fit for general education, and it could be a clear sign that programs can evolve with the times.

Final thoughts

Including data literacy in general education won’t eliminate the data gap, but it will accomplish some important things. It will ensure all students have at least a foundation in data. It will signal that academia recognizes data literacy as a fundamental skill—like writing, oral communication, and critical thinking. Additionally, including data literacy in general education may make it easier for faculty in major-specific courses who need to teach more advanced or industry-specific data skills. These faculty will know that students have had an introduction to data and can then move into more sophisticated content more quickly.

Ultimately, including data literacy in general education courses can only be one step in a much larger educational process, but as we all move forward on this data journey, one where precious few people can afford to be data illiterate, it is a good step.

The post Data Literacy and General Education: A Potentially Perfect Partnership appeared first on Nightingale.

Want to get to know Erica more?  Here’s a 60 second Q&A responding to a handful of the communities’ most popular questions over on the DVS Slack channel:

What’s your favorite chart, and why? ?

The bar chart. It is simple, but endlessly flexible. Every charting library has a version ready-made, so it’s usually quick to experiment and adapt. With a few basic modifications, you can create almost any kind of comparison. Almost everyone knows how to read it, making communication easier and derivative charts more approachable. I often move beyond a basic bar chart when presenting data for an audience, but for exploratory work it’s the first chart I reach for, and often the only one I need.

What tools do you use in your work? ??️

I use Excel, Illustrator and Figma for my day job, and a mishmash of d3/miscellaneous Javascript libraries, Excel, Illustrator, and R for personal projects. Mostly, I use whatever tool does the job and gets me to my goal in the fastest and least-painful way. Often, I jump back and forth between one tool to another for each step, especially in the exploratory stage: data aggregation in R, manual cleanup and JSON structuring in Excel, paste into Notepad to remove empty spaces/formatting, load to HTML/Javascript to draw and interact. Once I know where I’m going, I go back and turn the scraps into a reproducible trail.

What are key questions you ask / skills you’re looking for when hiring for a dataviz role? ?

Can you give me an example of how you use charts to make decisions in your day-to-day life? I’m looking for people who know how to think with data, and who use charts to do that job.

Thank you and welcome Erica!  Have a question or would like to get more involved? Reach out directly via education@datavisualizationsociety.org.

The post DVS NEWS: Erica Gunn joins the DVS board as Education Director appeared first on Nightingale.

As a data scientist, I am surrounded by data on a daily basis. Irrespective of the type of data, I find the best way to communicate ideas and stories is through visual means. Outside of work, when discussing my favourite topics, such as cinema and music, I find myself exploring ways to discuss them through charts and plots.

Whether you are a data scientist or not, you are quite likely to come in contact with categorical data on a daily basis. For example, if you are an avid sports fan, you may be interested in the stats behind the players. Additionally, if you are a fan of film or music, both fields provide a rich source of categorical data that allow for deeper exploration. Visualising trends can be a powerful way to communicate with others.

One of the main challenges of categorical data is that such data may involve determining the relationships between data points. Such data may also be represented in the form of a hierarchy: it may be necessary the trace the “flow” of data from one level to another. Data sources may have complicated underlying structures, therefore the main goal of any data visualisation is to represent information in such a way that is widely consumable.

Traditional charts types (e.g. bar, line, and scatter) can be used to plot categorical data types, but they have their drawbacks. Bar graphs are useful for showing a point in time or count of data, however it may be difficult to show the relationships of data points. Line graphs also are useful for showing trending data over time, and data relationships may be inferred graphically but it can be difficult to show data flows. Scatter plots are ideal for showing relationships between two data points, although having more than two series makes scatterplots difficult to read.

Thankfully, we have a number of options to display categorical data using a combination of flow and network diagrams. In the following sections, I shall review the main flow charts that one can use. I’ll provide a history as to the origin of the chart, discuss what types of data can be illustrated by such a chart, and provide a little insight as to how to tell a story using such a chart.

The Arc Diagram

The first chart we can use to display categorical data is an arc diagram. The first appearance of an arc diagram was back in 1964. Thomas Saaty was working in the field of graph theory and wanted to illustrate the number of intersections on a plane. He developed an idea to link categories across a fixed-line, using semicircles.

Let us fast-forward to 2001 to look at another application of an arc diagram. Martin Wattenberg, then a researcher at IBM, wanted to visualise patterns of repetition found within music. He observed that an arc diagram was an effective way to match sequences of notes within a longer passage of music. Over time, his arc visualisation technique has evolved.

Figure 2 (below) illustrates how he used an arc diagram to depict the sophisticated structure of Beethoven’s Für Elise. Wattenberg’s technique employed using “equality of pitch” of a note, or where a chord is used the top pitched note is used at a categorical point.

One can determine at a glance how the initial series of notes are used at the start and end of the piece. Additionally, there are many substructures that are repeated throughout the piece of music.

Graph theory and music are not the only domains in which arch diagrams can be used to great effect. In our third and final example, we consider how to visualise the co-occurrence of characters within the novel Les Misérables.

Figure 3 below provides an example of an arc diagram created in d3. In this chart, the author uses arcs to link characters as they appear within the narrative structure of the book. From the diagram, we can see at least three significant co-occurrence links: Marius to Valjean, Cosette to Valjean, and Thenardier to Valjean. Valjean gets about quite a bit!

Arc diagrams can be used as a method to visualise categorical clusters, some care should be taken in how the clusters are ordered. Heer, Bostock, and Ogievetsky note in their 2010 paper entitled ‘A Tour Through the Visualization Zoo’, “Though an arc diagram may not convey the overall structure of the graph as effectively as a two-dimensional layout, with a good ordering of nodes it is easy to identify cliques and bridges.” The key takeaway is that ordering categories by frequency should be a primary consideration.

Irrespective of your field of research, an arc diagram can be a powerful means to visualize links between categorical entities. Arc diagrams can be plotted across multiple graphical frameworks including d3, Python, R, and Tableau.

The Chord Diagram

Chord diagrams are a useful way to depict inter-relationships within categorical data. Chord diagrams were initially used in the field of medical statistics to show the relationship between the number of chromosomes between humans and distinct animal species. One of the first examples of a Chord diagram was published in The New York Times in 2007. Figure 4 (below) provides an example of this chart.

Chord Diagrams get their name from a geometric element: a straight line from one point on a circle to another is a chord. However, in almost all chord diagrams used for visualisation, the interconnecting “line” is generally some form of an elliptical curve.

Using the example in Figure 4, the key purpose of a chord diagram is to illustrate inter-relationships between categorical entities. The outer band represents the millions of base pairs of a chromosome. The connecting chords are used to join similar chromosome types from different species. The thicker the chord, the more significant the relationship. Let us look at some more examples of how Chord Diagrams can be used to great effect.

If you watched TV during the 1990s, most likely you have tuned in to at least one episode of Friends, a sitcom revolving around the day-to-day lives of six friends in New York City.

The author of this chart, Julien Assouline, wanted to plot the number of words spoken to and from each of the six main characters in the show. We can use this type of chart to show the inter-relationships between characters and make some inferences about the dynamics between the characters.

As with all chord diagrams, each character (entity) has its own distinct colour. Additionally, the chord widths are used to provide a visual representation of the number of words spoken between characters. Furthermore, the size of the character “arcs” provides an indication as to the total number of words spoken by a character.

Two points of interest, Rachel and Ross have the highest aggregate word count (over 1,000 words), while Rachel and Chandler have the lowest aggregate word count. For regular viewers of the show, this may not be a surprise.

In our last example, let us consider the question of hair colour. For the four main hair colours (black, blonde, brown, and red), would individuals prefer to change their hair colour or are they happy with their current one? Figure 6 below shows how this type of question can be illustrated using a chord diagram.

The setup for this last example is as follows: a number of individuals were interviewed to understand whether they were happy with their hair colour, or whether they would prefer to change to one of the three other main colours.

The chord diagram provides a number of useful reporting features. First, we can infer the number of respondents for each colour by the arc scale on the outer band. Additionally, we can see that for all colour types there is a cohort of users that are happy with their current hair colour type. However, the biggest adjustment comes from individuals with brown hair expressing a preference for blond hair.

Note author Mike Bostock’s clever use of colour. The intuitive use of colour (matching the chord and hair colours) reduces the need to provide additional labelling of the outer arc bands. Implementations for chord diagrams can be found across many programming (e.g. D3.js, Python and R) and non-programming (Tableau) frameworks.

Finally, in terms of visual storytelling, there are some rules to bear in mind: (1) The group placement around the circle is important. Minimise the number of chord crossings. (2) Either omit weak connections or collapse into an “other” category. Depicting every interdependency chord may lead to chart clutter. (3) The presentation of chords and arcs may appear counterintuitive to non-domain experts. Therefore, additional visual cues using Gestalt principles may be appropriate.

The Sankey Diagram

So far we have discussed DataViz chart types that are useful to illustrate flows from one entity to another. Consider the scenario in which we are required to show flows across a series of entity types. The Sankey chart is used to depict such flows. The evolution of the Sankey (or flow diagram) chart has an interesting history.

Charles Minard was a French civil engineer and statistician that had a keen interest in the area of informational graphics. During his career, he developed at least 50 flow charts to visually illustrate the level of passenger, load, and traffic rates of railways he designed. Minard is best known for his illustration of Napolean’s loss of soldiers during his 1812 Russian Campaign.

Figure 7, above, shows the first usage of a flow chart. The core purpose of this chart is to show the size of Napolean’s army as it progressed through the 1812 campaign. The count of soldiers is proportional to the thickness of the line. As the soldiers snake through various geographically locations (helpfully annotated), the viewer can see the flow line is reduced in width as the soldiers reach Moscow. Minard’s chart was created in 1869.

Matthew Sankey was an Irish Captain of the Royal Engineers, who had a research interest in railway accidents and braking systems. In 1898, he wrote an article in the Minutes of Proceedings of the Institutions of Civil Engineers, to discuss the efficiency of steam engines. Figure 8 below shows the diagram used to show the flow of thermal energy across an actual and idealized steam plant.

Figure 8 uses the same technique of flow width to depict the loss of energy. In Minard’s case, he overlaid geographical location to show the points at which the Napolean’s army suffered losses. In Sankey’s case, he used a logical diagram of a steam plant and directional flows. Irrespective of whether illustrating flows of soldiers or thermal energy, the Sankey diagram is clearly a versatile chart.

Lets us consider one final example of a Sankey chart. Figure 9 above provides a modern take on the flow chart. The chart provides a view as to how alternative energy sources can be used to generate energy for distinct purposes. The numerical values are TerraWatts per hour.

Even with this busy infographic, we can see the qualities that make a Sankey chart so useful. Energy types are on the left with energy use on the right. Flow arrows are included to provide an intuitive means of energy from source to use. The line thickness is proportional to energy consumption along with clear annotations.

The use of colour is an importation element of a Sankey chart. If many colours are used, the chart can be hard to decipher. Therefore careful consideration is required before building a flow chart. Limit your flows to five or six primary colours. If sub or child flowers are required consider using a different shade of one of your primary colours.

Like the two previous charts discussed, Sankey charts can be created using multiple frameworks: D3.js, Python (Mathplotlib), R (networkD3) and Tableau.

The Sunburst Chart

A Sunburst chart is another chart type that can be used to show flows or hierarchical data. The chart is built using a series of concentric circles. The centre of the chart is the root node, while each subsequent concentric circle is an outer leaf node. Each segment is linked to both an outer and inner node, with the exception of the root and most outer node.

One of the first examples of a Sunburst chart was developed in 1890 by Lawrence W. Fike. He developed a hierarchical circular chart to illustrate animal family, genus, species and subspecies. Figure 10 below shows Fike’s chart.

Let’s fast-forward to a modern implementation of a Sunburst chart. Figure 11 below provides an example of a Sunburst chart developed in D3. The chart is used to illustrate the flow of pages accessed by used on a website. The main purpose of this Sunburst chart is to understand how each sub-se

quence of events is related to an overall sequence of events. In this implementation as we hover over each segment, we are provided with a display of what percents of users used a distinct sequence of web pages for navigation.

As it is difficult to compare the relative sizes of sequences, it is convenient to have a total proportion of a distinct sequence displayed. Additionally, colour is vital to the readability of a Sunburst chart. It is understood that a Sunburst chart may have many hierarchical levels, careful placement of each segment can play a key role in the readability of a Sunburst chart. We’ll take a look at a final example where an abundance of colours are used, however, due to the placement of the categories and intuitive use of colour the chart remains readable.

Consider our final example of a Sunburst chart. Figure 12 above depicts a coffee tasters flavour wheel. In terms of colour, a wealth of shades are used. The inner segment contains nine distinct colours, the middle segment contains twenty-eight colours, while the outer segment contains over seventy colours. You may recall from our Sankey chart discussion the use of many colours may introduce readability concerns.

Due to the layout of the colour scheme and category placement, the red, orange and yellow hues are placed in the upper half of the wheel, while the blue, green and grey hues are positions in the lower half of the wheel. There is some ‘colour crossing’ on the outer segment of the wheel, most notably some brown and green hues, however as the chart is meant to be read from the centre outwards, the juxtaposition of colour is less noticeable.

Sunburst charts are a very popular type of chart, that allows for a level of interaction. Implements are available across a wide range of frameworks. In addition to the frameworks previously mentioned, Sunburst charts can be created in Microsoft Office and SPSS.

Conclusion

In summary, four types of charts for displaying flows between categorical data were discussed. Each chart type has its own unique quality for visual storytelling.

An Arc Diagram is useful at mapping 1:1 entity-relationships. Applications of such a chart include plotting the relationships of notes used in Beethoven’s Für Elise, to the character interactions in Les Misérables.

A chord diagram is used to display the strength of inter-relationships between categories. The thicker the chord, the stronger the relationship. One application of a chord diagram considers the relation between the count of words spoken between characters in the TV show Friends.

Sankey charts were shown to plot the multi-flow relationships between categories and can also be used to show the process flows between multi-component systems (e.g a steam engine)

Finally, a sunburst chart is used to plot event sequences and their proportional relationships as part of a wider set of relationships. Whether understanding the page-sequence flows on a website or the sequence of coffee flavours, a sunburst chart can cover it.

For all their differences, each chart types provides a unique way to tell a visual story with categorical data and their associated counts. Let the charts flow.

The post Endless River: An Overview of Dataviz for Categorical Data appeared first on Nightingale.

Learning to go from data to story is hard. You often have to wear many hats — statistician, analyst, domain expert, graphic designer, and more. One practice used consistently by data visualization experts is “sketching.” Most folks who practice data visualization have notebooks full of ideas for symbols, encodings, and narrative flows. Previous discussion in the DVS Slack channel has addressed this. But what makes sketching such an effective method? What can people getting started with data visualization learn from this practice?

Sketching helps us integrate different kinds of knowledge

Why do professional dataviz experts sketch? This is an important question to ask if you want to help learners develop this practice. Luckily, academics have been digging into this for a while — we’ve tried to translate some of what they’ve found into understandable motivations.

For one, the creation of external representations aids the learning process in many different ways. There have been studies on the role of externalization in learning, and others on the application of drawing to reason and learn within science education. These studies find that:

• drawing an idea makes explicit the metaphors and shorthands you’re using to think about the idea;
• a shared representation leads to more productive conversation and ideation in group contexts;
• physical representations allow you to see characteristics and relationships of concepts more easily.

We sketch for lots of reasons, but these studies highlight some of the key motivations that might inform your own approach to sketching for data visualization. Think about why you want to sketch: what do you hope to learn from the process that you didn’t already know?

The idea of integrating visual, verbal, and mental modes is further explored in Joanna Kedra’s thoughts on visual literacy. Kedra argues for the importance of visual literacy skills in an increasingly “multimodal” environment, and lays out visual literacy skills as being on par with reading literacy and numeracy. We evaluate and interpret images, “translating” between visual and verbal modes of communication, so sketching helps us try out images that communicate our ideas. Makes sense! Of course data literacy and visual literacy are not synonymous — data does not necessarily take a visual form — but, when we’re learning to sketch data visualizations, they are definitely interrelated.

These academic research projects establish why sketching is so important. Our brains are constantly translating the visual and the verbal, so externalizing this process helps us communicate and process more effectively.

Sketching to overcome “visualizers’ block”

People new to visualization often fall back on using data representations they are familiar with — bar charts, pie charts, etc .— without considering alternatives that could more effectively, or more appropriately, show the data. When you’re sketching you’re exploring a broader design space of your data visualization, having a visual conversion with yourself (and others) about how to represent the data effectively for your context and goals. Grammel’s work on how novices create visualizations shows us how this default to known charts particularly happens when learners have difficulty figuring out how to answer questions they have about statistical relationships with which they are unfamiliar. Similarly, Walny’s work on how learners sketch shows us that learners default to numerical representations over more abstract or symbolic ones.

Our takeaway? Drawing can be daunting, but sketches are useful even when the output doesn’t look “professional.” We learn more when we experiment and try new things, and you can surprise yourself with what you see when you try to put your thoughts and ideas down on paper. If you’re stuck on something, even if you feel unimpressed by your drawing skills, try sketching your way out!

When trying to design a data visualization, you can get stuck in lots of ways. Bressa’s work with data visualization novices, workers at a food bank, reveals some more insights from which to learn. This group struggled to move from text to visual descriptions. They also had difficulty managing the spatial aspects of their visualizations. Sketching helps with both of these challenges! Targeted approaches to visual brainstorming can address these barriers more directly, for instance if you get stuck on how to visually represent a concept or word you can try an exercise like creating a visual “word web.”

Building and bolstering data literacy

Why do we care? Our Data Culture Project is a free, lightweight, self-service curriculum of playful activities designed to help people build “data cultures” in their organizations. One activity in this toolkit, Sketch a Story, focuses on helping all kinds of people learn how to sketch — challenging them to quickly draw visual “data stories” based on word frequency data in political speeches and song lyrics. This is an exercise in creating a narrative from data: whatever factoids or relationships stick out to them in the frequency data are transformed into a visual representation, with participants working together in small groups with big paper, markers, and crayons (or when circumstances require, using online tools like Miro).

We’ve run this activity dozens of times, leaving us with a corpus of hundreds of sketches created by diverse groups of data literacy learners — higher-ed students, staff at non-profits big and small, government employees, journalists in newsrooms, and more. Feedback from this activity suggest it has a positive impact: more than half of participants in a testing workshop said it made them feel more comfortable with analyzing text, and a majority also found it both useful and easy.

A preliminary review of these sketches has shown some interesting patterns. For example, color (as opposed to other variables like size, shape, font, or position) seems to be the most common method used to differentiate different elements and categories in a sketch. And the majority of stories being told are comparisons: showing the difference between two parts of the dataset, or identifying an outlier within an overall trend. As we move forward with our exploration, we hope to discover more patterns, and add to what we know about the process of learning to sketch and work with data to tell stories.

Remember: stick figures are OK! The point of sketching isn’t to produce a beautiful work of art, but to get your thoughts and ideas into a format that leads you to new insights and ways of thinking. Importantly, sketching allows you to flex your creative muscles without any constraint: with your paper and pencil, you can do whatever you want. While more constrained tools like software and programs for creating data visualization undoubtedly have their place in the process, they don’t allow for the kind of unfettered exploration you get from just sitting down and sketching. Hopefully, this post helps you understand a little more about what research has to say on how and why we sketch to help us visualize. We look forward to learning even more in the course of our own research, and sharing these findings about the nature of sketching to help you with your own learning process.

This work was also accepted to the 2021 IEEE Visualization Conference.

The post How and Why We Sketch When Visualizing Data appeared first on Nightingale.