Teaching the Power of Data to All Audiences

Students walk into our classrooms with a myriad of interests, experiences, and motivations. Some might be there because it’s a required box to check for their major. Others are genuinely curious and signed up for an interesting elective. And a few… well, they are trying to figure out if oceanography—or whatever your course is about—is their thing.

Our challenge as educators is to take this mix of motivations and turn it into something that clicks for everyone. Specifically, how do we meet everyone where they are, while also elevating their thinking?

To learn more, we asked two of our very own Data Lab community members how they work with mixed audiences, and they provided us with two unique perspectives. One is an associate professor from an R1 public land-grant university, and the other is an assistant professor from a private liberal arts college.  

Ready to find out how to turn data into a universal language that speaks to every student? Let’s dive in! 

Elizabeth Harvey, University of New Hampshire: With Great Power Comes Great Responsibility 

If there is one thing Professor Liz Harvey wants her students to walk away with after taking one of her courses, it’s that they “have a responsibility to ethically show data that is truthful and not manipulated.”

Harvey holds up her decorated cupcake showing her artistic interpretation of data literacy, created at the 2024 Princeton workshop.

As an associate professor focused on biological oceanography at the University of New Hampshire, she caters to both undergraduate students and graduate students in her teaching, sometimes in the same upper-level course. To instill this sense of responsibility with both audiences at two different skill levels means that she has to be sure about her different expectations for both. 

For her undergraduate students just coming into the field, she expects them to learn how to analyze a graph and work with datasets. She supplies them with various robust time-series datasets that her students can use to create a hypothesis to test. Checkpoints and work sessions are built into each class.  

“For undergrads, we sprinkle Data Labs into the course to get them to learn how to interpret data,” she explained. As her students work with raw data to create their own data visualizations, they come to understand how many choices they have to make to show their results in the most truthful and complete light. “Students go into the class thinking this dataset will give me an answer, but most of the time, there’s no linear relationships and there’s missing data.” 

Harvey has gotten a lot of feedback from her students that her approach to scaffolding concepts helped them tackle this complex material. “I try to make this course a safe environment for my undergrads to have this struggle, whether it’s learning how to open a csv file or graph a data set, because better here than on the first day of their job,” she reasoned. “By the end, they appreciate that.” 

For her graduate students, Harvey uses Data Labs resources in a different way. “Data Labs gives them exposure to upper-level pedagogy,” she recounted. Her graduate students, by this point, have already learned how to look at data critically, so they want to focus on Data Labs as an educational exemplar. 

“I expect my graduate students to be data literate. I expect them to manipulate and interpret data,” she explained. She focuses on helping her graduate students effectively communicate their data through their own visuals and presentations to fellow peers, to committee members, and to the greater scientific community. In this way, the focus is more on narrative building. 

“Of course, I can only give them the tools,” she concluded. “At some point, people take personal ownership over their journey.” 

Natasha Gownaris, Gettysburg College: Data Is Power  

Gettysburg College is an undergraduate institution betting on a new focus on data literacy. The college recently created a first-year data and society (FYD&S) requirement along with a new data sciences minor. Classes that meet this requirement range from Biology to Economics to Women and Gender Studies. And new proposals are coming in from a multitude of departments, such as German Studies and Chemistry. With these changes, the college hopes to fortify its students with the tools they need to work in a data-driven world.  

For Assistant Professor Tasha Gownaris, this shift in focus did not come out of the blue.  

Gownaris’s research is concentrated on marine ecology and involves both field and quantitative work; it requires many long-term and open environmental datasets. As such, data analysis is a key component of the classes she teaches.  

Gownaris with other Data Labs members brainstorming ways to scaffold data literacy skills at the Princeton 2024 workshop. (from left to right: Claire Condie, Rebecca Freeman, Tom Connolly, Tracy Quan, Natasha Gownaris)

“The class that I will be using Data Labs in is a non-majors class,” she explained. “It’s a 100-level class, and historically it was taken by a lot of junior and senior non-science majors”. Those students were taking her class to fulfill a science requirement.  

“I had seniors in my class who struggled with reading graphs, and it wasn’t like I was judging them at all.” She recounted. “But it was like ‘Oh, this is something that I feel like you should be able to do.’” So, she shifted her course planning.  

Her methodology was similar to Harvey’s. She went back to the basics: from identifying min, max, and range to learning how to visualize data from imaginary datasets. 

“I wasn’t sure if students were going to be like ‘This is busy work’,” she said. But Gownaris used a peer-reviewed tool to assess student engagement after each data-driven activity, and her students’ reactions were positive. They recognized that their need was being met.  

Now her audience is shifting from juniors and seniors who come in with varying levels of data and graph literacy to first-years, all of whom have to take the FYD&S program. One of the program’s requirements is that all first-year students must learn basic data statistics and how to interpret graphs, which they will do in Gownaris’s 100-level course. “Oceanography is a great topic for teaching data literacy and how to read graphs, etc. because the data can be visualized in so many ways (time series, vertical profiles, maps, etc.).” 

Those students can then advance to her upper-level courses (if they wish) prepared for what lies ahead. 

Another requirement is that students must learn how to critically interpret data shared in the media and make two-way connections between data and society. Since the program is cross-disciplinary, Gownaris sees how Data Labs and other oceanographic data literacy tools fit into this new direction. “I’m going to have students work on spotlights for an open textbook on these data-society links,” she explained.  

“For example, where oceanographic data or biodiversity data is collected in the ocean is really biased. That is how society impacts data. But then the availability of those data determines what areas get protected. Data is power. It influences how decisions get made.”