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When asked to name scientists, students mention the likes of Charles Darwin, Albert Einstein, and Isaac Newton. And when asked to draw a scientist, students almost always draw a white man holding a test tube and wearing a lab coat. Professor Robin Costello from the University at Buffalo tells us more about a new study that parsed the effects of including visual depictions and humanizing information about scientists featured in undergraduate biology course materials.

This post was originally released by The Royal Society, here.

How students think of scientists reflects the false narrative that only certain types of people can be scientists – specifically white men with brilliant minds.

One powerful tool to combat this false narrative is to feature relatable, contemporary scientists whose identities do not match the dominant stereotype of a scientist featured in course materials. To highlight counter-stereotypical scientists, instructors can implement course materials that include photographs of scientists in their lecture slide decks. Or instructors can highlight humanizing information about scientists in their course materials. Sharing information such as the barriers scientists have faced or how they overcame obstacles in STEM may help students relate to scientists and envision their own STEM careers.

In our latest study, we parsed the effects of including visual depictions and humanizing information about scientists featured in undergraduate biology course materials with a large-scale research study. Over several academic terms and 36 undergraduate institutions in the United States, we distributed three versions of short quantitative activities (Data Nuggets) that varied in their level of information about the featured scientists (from including only their names and pronouns to full Project Biodiversify scientist profiles).

Data from over 3,700 students revealed that including humanizing information about scientists improves student engagement with quantitative biology activities. Photos of the scientists alone were not enough to improve student engagement. Instead, when provided information about the scientists’ life experiences, students found the activities more interesting, more relevant to their future careers, and put more effort into the activities. Our data suggests this pattern was driven by increased relatability of the featured scientists. 

While these results applied to all students, the strongest impacts were evident among students who shared excluded identities with the featured scientists.Our findings underscore the importance of providing students with examples of relatable scientists in STEM courses, rather than simply adding photos to increase representation. By highlighting humanizing information about scientists, instructors can both increase student engagement in their courses and improve equity in STEM.

We recommend several evidence-based resources to use in biology courses, including the Data Nuggets and Project Biodiversify materials studied here (together, DataVersify), as well as Scientist Spotlights, BioGrapI, and the Story Collider Podcast.

Article Highlights

• Michigan State University’s Data Nuggets program is starting its third round of funding from the National Science Foundation to improve data literacy in K-16 students.
• The program, operated by the Kellogg Biological Station, also introduces real STEM professionals through storytelling, helping students better relate to their projects and engage more deeply with the program’s content.
• In collaboration with Auburn University, the newest NSF grant will help Data Nuggets further that engagement and introduce students to a greater diversity of scientists.

A data literacy program that’s also changing students’ relationships with science and scientists is entering its third round of funding with a new $1.5 million grant from the National Science Foundation.

In collaboration with Auburn University, the Data Nuggets program at the W.K. Kellogg Biological Station, or KBS, will work to identify factors that improve equity and success in undergraduate STEM education.

Launched by Michigan State University in 2011, Data Nuggets is a curriculum development project designed to help students better understand and use data. The program shows how professionals in science, technology, engineering and math really work with data by sharing their stories, which also enables students to relate on a much more personal level.  

Data Nuggets challenges students from kindergarten through undergraduate levels to answer scientific questions using data to support their claims. The questions and data originate from real research provided by scientists whose studies range from physics to ecology to animal behavior. 

To add the personal element, Data Nuggets is collaborating with Project Biodiversify — another education program started at MSU — to add the scientists’ bios, which include information like hobbies and their lives outside of science. This helps students relate to the researchers and see them less as strangers in lab coats and more as scientific role models. 

“We’ve found that it’s the scientists that are engaging students in the activities,” said Elizabeth Schultheis, co-leader of the Data Nuggets program. “If they connect to the role model, then you can get students to do the data literacy activities because they know, ‘Oh, this is a real person. I relate to this person. And I’m working with authentic, real data. I’m not just doing busy work.’” 

Schultheis, who earned her doctorate in plant biology from MSU, is also the education and outreach coordinator for the Long-Term Ecological Research, or LTER, program at KBS, which supports Data Nuggets. Schultheis and co-leader, Melissa Kjelvik, developed and run the program, forming partnerships to research and fund the program.

“With our current research, we’re trying to figure out what is the special thing that’s really resonating with students in terms of the role models,” Kjelvik said.

“Our research will investigate how and why role models are critically important for students,” said Cissy Ballen. Ballen is an associate professor in the Department of Biological Sciences at Auburn, the lead institution on the NSF grant, which builds on the past success of Data Nuggets and will help ensure its future impact.

“The theory behind this is that students must be able to see a scientist’s success as attainable to relate to that scientist,” Ballen said. “My prediction is that students will find success most relatable when they see some scientists, like them, have struggled with science, but then were able to overcome that struggle.” 

Making data talk

Many students’ eyes gloss over when they hear terms like “data” or “science.” 

Even Schultheis admits she didn’t appreciate the significance of data until she was a grad student collecting her own. The problem, she said, is that kids are often taught how to make a graph, for example, but not why.

“I never really learned to care until I understood the reason I make a graph is because I want to answer a question,” Schultheis explained. “I need to see the data, what it looks like. And that’s why I make a graph.” 

Data Nuggets doesn’t change the skills that are taught in conventional curricula. Students still learn how to make and label axes, for example, and then how to plot data to create graphs. But they also get a more immersive introduction into why real people use these skills.

“Our purpose with these Data Nuggets modules is that everything is always given real context and always in service of a scientific question,” Schultheis said. “It’s always: Here’s a scientist. Here’s the question that they really care about and the reason they collected this data is because they want to answer this question. And you make the graph to visualize it so that you can see what the data is telling you.”

Data Nugget activities come in four levels, so instructors can use the ones best suited for their specific classes. Level 4 activities are designed for high schoolers and undergraduates, while level 1 activities are appropriate for elementary schools and higher grades looking for a refresher after a summer break, for example.

Teachers also have flexibility with how to present an activity based on their goals. For example, instructors can choose activities with completed graphs so students can focus on interpreting what they see to answer questions.

Or students can be given blank grids to give them experience in creating useful representations of data from scratch.

Connie High, a science teacher at Delton Kellogg High School about five miles from KBS, calls Data Nuggets “a game changer.”  

She said that her students, when they’re new to Data Nuggets, can usually make claims and find supporting evidence. The challenge is learning how to articulate the connection between the two.

“They really struggle with how to link claim, evidence and reasoning. They tend to just restate the evidence again,” High said. 

“With Data Nuggets, we definitely see an improvement from the beginning of the year to the end.” 

Humanizing data 

The Data Nuggets program started 13 years ago as a grassroots collaboration between KBS researchers — including Schultheis and Kjelvik, who were then grad students at KBS — and K-12 teachers, including High. 

More than 120 scientists have contributed more than 120 data literacy activities since then. Tens of thousands of people regularly use the Data Nuggets website. Links to various Data Nuggets stories can even be found in science textbooks. 

“Long-term relationship building is why we got such good insights from teachers about what their students needed, because they already had trust with us, and we went into their classrooms and learned from them,” Schultheis said. “And building relationships with scientists who trust us to tell their stories correctly, who are giving their own stories for students to read and learn about, continues to be critical to our success.”

But exactly how to best package and present the data stories falls to Schultheis and her colleagues. Previous research has supported the idea that focusing on the scientist and why they collected the data is essential. After all, data is just numbers. It’s human interaction that puts numbers in perspective, gives the scientific question context and engages students in the activity.

“Humanizing the data is at the crux of this work,” Ballen said. “Data Nuggets is such a successful resource because of the way they humanize the data component and contextualize it within the science itself and show that it’s being done by relatable scientists. They do that really well.”

With its third round of NSF funding, Data Nuggets is attempting to fine-tune how to best present the scientist role models and the stories to improve student engagement with science even more.

The goal is not only to increase the portrayal of under-represented groups among scientist contributors, but also for students to see that they share some things in common with the scientists they see. 

“We used to ask students to draw what a scientist looks like, and they all would draw someone who looks like Albert Einstein,” High said. “It’s incredibly important that they see there are scientists who look like them.”

“You can imagine if you were a student sitting in a classroom you might get an activity that features a scientist from a prestigious university with awards and that sort of thing, and that might not be very relatable,” Ballen said. “Success might not be perceived as attainable.”

Data Nuggets is working to combat that perception.

For example, there’s a Data Nugget called “Trees and the City”, featuring a photo of a smiling University of Minnesota ecologist named Adrienne Keller wearing a bike helmet and sunglasses. A video shows Keller riding her bike through neighborhoods in the Twin Cities as she describes her interest in tree patterns. She poses her dataset’s main question: “Are there differences in the total canopy cover or the number of tree species planted in a neighborhood based on residents’ income level or percentage of BIPOC — Black, Indigenous, and People of Color — residents?”

Another Data Nugget was written by a community scientist from Bayfield, Wisconsin, located on the south shore of Lake Superior. He’s pictured wearing shorts and gym shoes as he stands on ice. 

For his Nugget, he used historical data to answer his question if the winters were getting shorter and changing the dynamics of how people could travel in the area. 

He also happened to be a high school student.

“That’s the dream outcome,” Schultheis said, “that students realize how powerful data are, and they can be advocates for themselves and their communities because they can actually go to the source of information and ask and answer questions.” 

This story was written by Lynn Waldsmith, and was originally posted on the Michigan State University, College of Natural Science website here.

Scientific role models increase student success in their science courses as well as inspire students to pursue science careers. The Ballen Lab at Auburn University has completed significant research demonstrating that role models with diverse identities are lacking in undergraduate biology classrooms. Students with identities that are not represented in their undergraduate science courses do not have many opportunities to see themselves in science careers and as scientific leaders.

“I am excited to collaborate with researchers at Michigan State University to identify factors that improve equity and success in undergraduate STEM education. Our research will investigate how and why role models are critically important for students,” said Cissy Ballen, associate professor in the Department of Biological Sciences.

The collaborative team, led by Ballen at Auburn and Elizabeth Schultheis at MSU, was awarded $1.5 million from the National Science Foundation’s Division of Undergraduate Education.

Robin Costello, a postdoctoral scientist in the Ballen Lab working to understand the relationship between role models and successful student outcomes, explained, “Featuring relatable scientist role models in classroom materials is a low-cost and accessible way to increase the recruitment and persistence of students with identities historically and currently excluded from STEM.”

The research team’s recent research showed a direct correlation between relating to scientific role models and student engagement. “These results led to more questions about the critical features of scientist role models that make them effective and served as the foundation for the recently awarded project,” Ballen explained. “Theory makes several predictions about why and how role models are critical to student success. With this support from NSF, we will conduct critical research that tests theory on what makes an effective role model.”

Costello added, “Our research will specifically explore how to tell scientists role model stories in ways that improve student outcomes.” The project is entitled “Collaborative Research: Sharing Scientist Role Model Stories to Improve Equity and Success in Undergraduate STEM Education.”

“Several popular resources have been created to combat the pervasiveness of the stereotypical scientist in biology and STEM curricular materials,” Ballen added. An important long-term result of the project are free, open-source materials for educators to use in their classrooms to nurture more inclusive environments where students can learn from a wide array of STEM leaders to whom they can relate.

These resources will develop biology data literacy curricular materials that teach quantitative skills while simultaneously highlighting the diversity of scientists in STEM. These resources will be based on two well-known educational resources: Data Nuggets, resources that are developed in a partnership between scientists and teachers, and Project Biodiversify, a site that offers education tools for diversity and inclusion in biology classrooms.

Our team will be recruiting instructors to implement the activities in classrooms. If you are interested in participating in this project, please contact mjb0100@auburn.edu. For the original story, written by Maria Gebhardt, visit the Auburn page here.