Teacher Helps Students Rethink Who Can Be a Scientist

As a teenager, Christine Adamson thought science was only for the few who could survive. “It felt like a weed-out type of class,” she recalls, “like it was supposed to be so hard that only a few people would be good at it.”

Now a science teacher at Day Middle School in Newton, Mass., Adamson is determined to change that narrative. “I want my students to walk away with this idea that a scientist can be anyone,” she says.

One way Adamson does that is through the Northeast U.S. Shelf Long-term Ecological Research (NES-LTER) Data Jam, a competition where students work with real ecological data and professional scientists.

Telling a Story About Data

This Data Jam challenges students to bring real data to life through creative storytelling. The word “data” might make some people’s eyes glaze over, but this project is anything but boring. Think crustacean costumes, video games, and illustrated storybooks.

All of the data comes from research conducted as part of the Northeast U.S. Shelf Long-Term Ecological Research (NES-LTER) Program. NES-LTER scientists collaborate to gather oceanographic data about the continental shelf over an extended period. To be eligible for National Science Foundation funding, LTER sites must include a broader impacts component.

Because K-12 students can’t easily join marine research trips, NES-LTER’s Education Coordinator Annette Brickley launched the Data Jam seven years ago. She selects teen-friendly datasets from recent studies and challenges students to tell a story using any expressive medium.

Tuva Helps Students Find The Story, Boosts Confidence

The first step of the competition is finding the story within the data. Adamson, like 90% of Data Jam participants, relies on Tuva for this phase.

Adamson recalled the first time Brickley showed her Tuva: “She’s showing us how she uploads the data. All of a sudden this graph populates, and you could just pull over the variables. This is amazing, right? And even the kids were like, ‘Wow!’”

The drag-and-drop interface helped students quickly make sense of the variables and the associations between them.

Adamson noted that Tuva also accomplished something deeper—it boosted students’ confidence. They were proud of the professional-looking visualizations they were able to create. For Adamson, that confidence is essential.

“I like having them feel successful in class,” she said. “So that if science is something they’re interested in, they feel like they can do science.”

In Adamson’s classroom, success breeds success and opens the door to students seeing themselves as scientists.

Real Data + Creative Outlet = Student Investment

Once students have used Tuva to identify interesting patterns or trends in the data, they unleash their creativity to tell the story. In past years, Adamson’s students have composed symphonies, developed elaborate board games, and recorded songs.

“The kids who are, during the year, maybe not as engaged— they just light up with this project,” said Adamson.

She quickly rattled off three examples: a group so engrossed in writing their rap about fish that they didn’t want to stop for lunch, a quiet student who found her voice narrating a group video, and a chronically absent student who started showing up every day during the project—and kept coming even after it ended.

But Adamson believes it’s not just the creativity that draws students in—it’s also the sense that they’re contributing to something larger.

“It’s not just a standalone lesson,” she said. “It’s connected to something real. They feel a lot more ownership over it than just doing regular classwork.”

NES-LTER scientists serve as judges, offering feedback to every group. For many students, this is a powerful moment: real scientists, from diverse backgrounds, are not only evaluating their work but engaging with it, showing that science is a community they can be part of.

That insight deepened after Adamson’s own experience aboard a research vessel, where she spoke with working  NES-LTER scientists about what spurred them to pursue a career in science and how to interest more students.

 The answer that stuck with her: show students how the data they’re working with might lead to real, positive impact. When students understand that their work matters—that it’s part of something bigger—they’re more likely to see themselves in the role of scientist.

This Year’s Contest

This year’s submissions are due in early June. While most participants are in Massachusetts and Rhode Island, the NES-LTER Data Jam is open to U.S. classrooms nationwide.

The official registration deadline has passed, but educators can still join by emailing Annette Brickley directly at abrickley.edu@gmail.com.

Make Your Students into Data Storytellers!

No matter where you are or what ecological concept you are studying, you can take a page from the Data Jam playbook. Find some real-world data, use Tuva to identify the story, then let your kids get creative in its telling. 

Here’s how to find authentic data:

  1. Find a classroom-ready dataset in Tuva’s Dataset Library. All of our datasets are generated from real-world data. (Some, like the Giant Kelp Growth dataset and Long Term Temperature and Precipitation, come directly from LTER sites.) 
  2. Upload data from an LTER study into Tuva. (Sign up for office hours if you need help.) 
  3. Upload data from governmental sources, such as NOAA, NASA, or Data.gov, into Tuva.

Get Kids Help–Right When They Need It

Tuva recently launched a new feature that will significantly impact how students and teachers interact with Tuva Jr.: Live View.

Two Ways Live View Will Impact Instruction

1. Quickly Note Students Who May Need Your Attention

Some students constantly seek attention, while others do their best to fade into the background. Those who need your help most are not always the loudest.  

Use Live View to identify students who:

  • are stuck or distracted
  • may be speeding through too quickly
2. Create a Dynamic, Responsive Learning Environment

Students thrive when teachers build opportunities for discussion, collaboration, and personalization. When you know where students are in a lesson in real time, you can:

  • pause them for discussion after they complete a specific question
  • partner them with others who have reached the same step
  • provide extensions for early finishers
Subscribe to Tuva or Tuva Jr. for Live View Access

With a Tuva subscription, you can monitor student progress on Tuva activities in real time with the new Live View feature. You’ll also get full access to our extensive library of lessons and datasets and unlimited capacity to upload data and create your own activities. View subscription information.

Plotting a Path to Future Employment

One Teacher’s Mission to Build Data Literacy

“It doesn’t really matter what you do. You can’t get away from data.

Chaffin Middle School Science Teacher Laura Davis recognizes data skills are in high demand in the modern workplace, and she wants her students to graduate prepared. Davis rapidly ticked off three local examples of data-driven careers.

Fort Smith, Arkansas, where Chaffin Middle School is located, is home to several big manufacturing companies including ABB Motors and Mechanical, Mars, Gerdau, Nestlé, Trane Technologies, The Coca Cola Company, and L’Oreal, amongst others. Davis noted that they don’t just need labor anymore; they need people who know their way around data because many of the diagnostic programs for the machinery run on data.

Additionally, many people in Fort Smith work remotely. The city has been recruiting remote workers to relocate to Fort Smith, luring them with monetary incentives. Many of the remote workers are in the information technology sector. Nationally, 67% of IT professionals work remotely, according to Statista.

Davis pointed out how even small businesses rely on data. Small business owners use data to optimize operations, manage inventory, improve customer experience, analyze customer behavior, forecast sales, and enhance marketing efforts. Whatever profession they go into, Davis wants Fort Smith students to leave school ready.

That’s why Davis makes data exploration a regular part of her teaching. Using tools like Tuva, she helps students see data not as an abstract concept but as a way to make sense of the world around them.

Helping Students See Data is Everywhere

To help students see data as a way of understanding the world, Davis emphasizes that patterns in nature are often mirrored by human experience. For example, students notice a cyclical pattern when studying population dynamics. The prey population grows, leading to an increase in predators, followed by a decline in prey due to predation, which then causes predator numbers to drop as well. Then the cycle starts anew. Afterwards, Davis discusses patterns in our everyday lives that follow a remarkably similar pattern, such as supply and demand or the fashion cycle.

“Let’s not do the eighties again,” Davis jested. “But we know it’s gonna come back. It always does.”

Davis helps students see parallels between natural patterns, like those in this Lynx and Snowshoe Hare dataset, and patterns in human behavior–such as supply and demand or fashion cycles.

Davis thinks teachers in other disciplines could use data to help students understand their content and, simultaneously, improve their data literacy. For example, an English teacher reading “The Giver” with students could have them graph Jonas’ opinion of his father throughout the story. The visual representation would help students notice where the story turns. A social studies teacher could use census data to enrich their lessons too. Students could compare 1940 and 1950 census data to examine how World War II reshaped the U.S. workforce.

Everything, Davis tells her students, is data–whether it’s a predator-prey relationship, a plot twist, or a fashion trend. Everything follows a pattern. Everything has a next. Davis uses Tuva regularly in her instruction because it reveals these cause-effect relationships to students.

“Tuva shows them how one change makes a huge difference,” she explained, adding that students can apply that to weigh decisions in their own lives.  “Practice on Tuva helps them reason through problems better. And I just think that it is imperative.”

Fitting in Regular Practice

Davis incorporates data into her daily routine, assigning a “graph of the week” as bell work. The graph is typically tied to the core idea of the current unit.

“It’s just like practicing your instrument. If you only play it a couple of times, you’re not gonna be great.”

Davis’ bell work follows a similar trajectory each week. On Monday, students identify the scale, title, axes labels, independent variable, and dependent variable.  Difficulty identifying basic graph components is common among secondary students, but Davis observes that this challenge decreases with frequent practice.

“It’s just like practicing your instrument. If you only play it a couple of times, you’re not gonna be great,” explained Davis. “It’s a repeated practice that students need to be data literate.”

On Tuesday and Wednesday, Davis’ students analyze the graph, noting patterns and trends. By Thursday, they begin using the graph to make predictions.

By assigning the weekly graph as bell work, Davis minimizes instructional time spent while keeping students’ data skills sharp.

Davis’ favorite graph to use with students shows the elements with Group on the x-axis and Period on the y-axis. “It’s funny,” Davis said. “They just think it’s a list.  And then when you graph it, they’re like, ‘Oh, there’s a reason!’ They don’t believe me till they see the data.”

Preparing for a Data-Driven Future

For Davis, data literacy isn’t just for scientists or mathematicians—it’s a universal skill that’s essential in today’s hyper competitive job market.

 “We are now on a global stage. We are competing against people we will never meet,” she explained. “Students will need to be able to gather information, analyze it, synthesize it, and make decisions about it quickly.” 

That’s why Davis weaves data into her daily instruction. An early adopter of Tuva, she has used the platform since 2017 to help her students build data skills—completing more than 3,500 assignments in the process.

She hopes more teachers will embrace data literacy and sees Tuva as a tool that makes integrating data into instruction doable for busy teachers.

“Tuva is a gem. It should be shining on everyone’s desktop.”

“Tuva is a gem,” Davis said. “It should be shining on everyone’s desktop.”

Synergy: Our Powerful Tools + Your Best Ideas

Real-world pedagogy and data literacy are at the heart of Tuva’s mission.  Today, we are advancing that work further by enabling you to create custom activities in Tuva using your own data.

The Tuva Activity Builder allows you to place your best lesson ideas beside our powerful, accessible data visualization tools. That means the ability to quickly explore data and create, revise, and analyze graphs will be right at your students’ fingertips.

Five Reasons to Take Advantage of Tuva’s New Activity Builder

1. Strengthen Connection, Deepen Comprehension

Collect data with your students one day, then incorporate their dataset into a lesson on Tuva for the following day. Students will have a deeper connection to and comprehension of the data when they’ve collected it personally.

This deep involvement in the entire science process–from collection to sophisticated visualization and analysis–helps students see themselves as scientists, not just people learning about science.

    2. You Pick the Phenomena, We Provide the Tools

      Your creativity far exceeds the bounds of our Science Content Library; we simply cannot encompass every possible anchoring phenomenon. The ability to craft lessons in Tuva from your own datasets allows you to stick closely to your anchor phenomenon without sacrificing the benefits afforded by the Tuva tools.

      3. Create Lessons That Speak to Your Students

      Relevance drives student engagement. Use local data to center your math or science lessons around places familiar to your students. Alternatively, capitalize on your students’ unique personalities by pulling in data about topics you know interest them.

      4. Make Graphing Accessible to All Learners

        Our tools are designed to be accessible for learners with diverse abilities. So, when you create your lesson in Tuva you can rest assured all students will be able to engage meaningfully with data. (Learn more about Tuva’s Commitment to Accessibility.)

        5. Strut Your Stuff and Help Make the Future #DataLit

          You’re brilliant. Your work should be shared. You can now enable school colleagues and far-flung teacher friends to use more data in their instructional practice by sharing a direct link to your original lessons in Tuva. (And while you’re at it, remind them to hit you back. It’s a team effort.)

          How to Get Started Building Lessons

          • Select My Datasets on the Dataset Library dropdown. 
          • Insert or upload data.
          • Select the Create Activity button in the lower right corner.
          • Create your activity.
          • Select Publish.
          • Choose one of the options: Publish Privately or Request Public Sharing. 

          Now, start sharing!

          Sign Up for a (FREE) Private Tutorial

          We’d be glad to show you how to use the Activity Builder. Sign up for a 30-minute office hours session to meet virtually with one of our educational specialists for a one-on-one tutorial.

          Youth Robotics Team Uses Data to Call for Change

          A team of ten Massachusetts 12- and 13-year olds want community members in the Narraganset Bay Watershed to change their behavior.

          Their advice? Apply phosphorous-free fertilizers, use plantings to filter water, and convert impervious surfaces to absorbent ones. All of these changes, they say, will help prevent harmful algal blooms (HABs) in the bay.

          A multi-line graph shows how mean dissolved inorganic nitrogen (DIN) and dissolved inorganic phosphorous (DIP) changed throughout the months of the year. Both began to rise in May. DIP peaked in October at around 1.3 micrograms per liter. DIP peaked in December at just under 13 micrograms per liter.

          The students reported, “Phosphorous, even in tiny amounts, can trigger HABs,”

          The youngsters, who call themselves The Techno Tridents, are all members of a robotics team from Eastern Massachusetts. They became concerned about the nearby estuary this fall when they began preparing for the 2024-25 FIRST LEGO League Challenge. The annual challenge always includes two major parts: designing a robot and conducting a research project. The research portion requires teams to identify a problem related to the year’s theme, find out as much as possible about the problem, and work out an innovative solution. This year’s challenge theme was “submerged” and centered around oceanography.

          The Techno Tridents quickly became intrigued by HABs, particularly an infamous Narragansett Bay bloom in 2003 that resulted in massive fish kills and shellfish recalls. The team wanted to know what brings about these blooms.

          A “Huge Learning Curve”

          The team’s coach, Pallavi Naravane,  pointed the students toward the Northeast U.S. Shelf Long-Term Ecological Research (NES LTER) project. The NES LTER Schoolyard Program provides a series of datasets for use by middle and high school students. 

          The first dataset The Techno Tridents used. Explore it in Tuva.

          The dataset most helpful for their inquiry was about nutrient levels in Narragansett Bay and comprised 1,229 datapoints. It was marked “advanced- HS recommended.” The columns across the top of the spreadsheet simply listed date, depth, NH4, DIP, NO3 + NO2, NO3, NO2, and DIN. The problem? Many of the Techno Tridents had not yet completed a middle school chemistry unit let alone a high school chemistry class.

          Poster created by 8th-grader Akshita Serikar,

          To fully understand the data, the team needed to learn more about science concepts above their grade level such as the nitrogen cycle, the Periodic table, valency, ionization, pH, and eutrophication.

          “There was a huge learning curve,” said Naravane. She laughed, admitting that, as an engineering teacher, she was included in that learning curve. “I just let them lead it,” she said. 

          Untangling the Data With Tuva

          The students now faced the challenge of making sense of a complex, multivariate dataset. This is where Tuva became indispensable. The students uploaded the data provided by NES LTER into Tuva for analysis.

          Naravane described Tuva as a “centerpiece” in helping her students uncover associations between variables. Using Tuva, students explored data analysis techniques. They learned how to add two variables on one axis, create dual-axis charts, and identify correlations. Tuva’s intuitive tools gave them insights that other software couldn’t match.

          “Tuva labs is very sophisticated in the way that it allows you to see data,” Naravane said.

          The story the data revealed was compelling: nutrient levels in Narragansett Bay spiked in October and November. Intrigued, the students sought to uncover the reasons behind this pattern.

          The Techno Tridents included this graph in their project summary, writing, “The nitrogen and phosphorus spike in the fall… It is repetitive and we can see that the spikes are always more than 16uM of Nitrogen and 1.5uM of Phosphorus in October-November.“

          To build a more complete picture, they explored additional attributes like temperature, rainfall, salinity, and occurrence of phytoplankton species. They discovered that October is when the water reaches its warmest temperatures and more rainfall occurs, contributing to increased runoff. This surge of nutrients, combined with peak temperatures, create ideal conditions for algal blooms.

          Try using Tuva to look for relationships between sea surface temperature and phytoplankton.

          Their analysis didn’t stop there. Using maps, the students noticed a high density of golf courses and lawns in the Narragansett Bay Watershed. Further research revealed that fall is a common time for lawn fertilization, adding another piece to the puzzle.

          “We found a lot of these rabbit holes of science we could walk off into. And that’s exactly the kind of science learning I want,” Naravane said. “ You never get a chance to learn other things, unless you are powered by curiosity.”

          Techno Tridents Secure Second Place With Data Analysis and Awareness Campaign

          Based on all of the data, the students decided to launch a campaign to educate their community about responsible fertilization techniques. Their campaign included a variety of modes of communication:  bookmarks with illustrations on one side and information on the reverse; an intricate, hand-drawn poster; and a Scratch animation.

          Their data-backed campaign, along with their robot, helped the Techno Tridents land a second place finish in the Northborough FIRST LEGO League qualifier. Their work has also attracted the attention of other local organizations- including environmental non-profits looking to collaborate and a science-based art gallery that is going to display Serikar’s art (above).

          Data’s Important Role in Innovation

          Naravane coaches six teams via her small business Planet Robotics. Her work with students mainly centers around engineering. She is convinced that data needs to play a larger role in the engineering process.

          “I really believe data is a new way to see things,” Naravane said. “If you don’t know what you are studying, how do you know it is a problem?”