teachers

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:

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.)
Upload data from an LTER study into Tuva. (Sign up for office hours if you need help.)
Upload data from governmental sources, such as NOAA, NASA, or Data.gov, into Tuva.

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.

Do Kids Need to Learn to Graph by Hand?

Middle School Science Team Upends Expectations

Knowing how to create graphs by hand is an essential skill.

That may have been unexpected, coming as it does from the blog of a digital graphing tool. However, it’s important to clarify that using a graphing tool, like Tuva, and manually graphing are not at odds with each other; nor does one necessarily serve as a prerequisite. In fact, as the middle school science team at Northwest Middle School discovered, the two approaches are synergistic.

Northwest MS teachers Judith Neugebauer, Victoria Mauro, Anne Salisbury, and John Pikus.

“[Our students’] ability to see the numerical data in a graphical form is really transitioning from one realm to the other,” explained teacher Judith Neugebauer.

The team, which also includes teachers Victoria Mauro, Anne Salisbury, and John Pikus, is based out of a school in Salt Lake City. They began augmenting their manual graphing practices with Tuva’s digital graphing tools about a year ago.

The Most Persistent Problems for Novice Graph Creators

Adding digital graphing tools to the mix for novice graph makers is a step some are reticent to take. (Psst… I used to be amongst them.) Because digital graphing tools automatically do some of the steps for students, teachers wonder if students should know how to graph manually before they begin utilizing digital tools.

Before we address that concern, let’s take a step back to consider the most common skills with which novice graph makers struggle:

Placing variables on the correct axes
Setting up equal intervals and scaling them appropriately
Choosing an appropriate graph type
Accurately plotting data points
Including descriptive titles

The question is, then, will using digital graphing tools before students have mastered manual graphing prevent students from developing these important skills?

Better Together

Data literacy education expert, Molly Schauffler said that 15 years ago she would have said yes, but the development of drag-and-drop graphing tools, like Tuva, shifted her perspective.

“My sense now is that playing with the data and visualizing it on a digital platform gives students a visual sense of purpose, direction, possibility, and choice without getting mired in drawing and mechanics,”Schauffler said. “That said, I am still an advocate of hand drawing graphs alongside digital exploration.”

Schauffler is a founding member of a group called Partners in Data Literacy, a paleoecologist, an assistant research professor emerita for the University of Maine, and a former Tuva consultant.

“We still have them graph in their notebooks too, but this year it’s going way faster.”

The benefits of using both methods simultaneously are evident at Northwest, where teachers say making graphs on Tuva has hastened the development of manual graphing skills for their 7th- and 8th-grade students.

“We still have them graph in their notebooks too,” Neugebauer explained, “but this year it’s going way faster.”

The teachers there credit the uptick in pace to repeated exposure to and practice with variable placement and graph types in Tuva.

Our Suggestions for Achieving Synergy

Digital graphing tools exist on a spectrum from fully automated to completely open and incremental. When combining manual and digital graphing practices, consider how the features of the digital tool may impact skill development.

For Tuva users helping graphing novices master the five skills listed above, we suggest pairing manual graphing and Tuva in the following way:

Practice Manually	Practice With Tuva
Setting up equal intervals and scaling them appropriately	Placing variables on the correct axes
Accurately plotting points	Choosing an appropriate graph type
	Adding descriptive titles

In most instances, Tuva sets up intervals and plots data automatically once students have chosen which attributes (variables) to drag to which axes. Doing these things manually helps students understand a block on the graph represents a specific numerical increase.

That said, it is not necessary for students to practice establishing equal intervals and plotting data points every time they graph. In fact, sometimes it gets in the way of the other skills students are trying to master. Making graphs manually is so time-consuming that it is hard to achieve rapid repetition of a skill. Repetition is incredibly helpful for achieving mastery when it comes to placing variables, choosing graph types, and writing titles. Tuva enables targeted practice in these aspects of graphing. Students can also quickly and easily fix errors instead of needing to start all over, avoiding frustration and shutdown.

Swapped axes are a common woe of novice graphers.

Repeated practice can help.

Are We Graphing for Graphing’s Sake?

Perhaps, however, this entire discussion misses the point. Why is it important for our students to be able to create graphs? Is it simply so that they can create graphs? Of course not. The ultimate goal is to empower students to uncover insights about data through their graphs. Scientific literacy and data literacy are, after all, inextricable.

It’s easy to lose sight of that goal, however, in the day-to-day slog of asking kids over and over and over, “If you count up by ones, will you have enough space on your graph paper?” or, “Where does the independent variable go again?” And if we’ve lost sight of our goal, chances are our students never even knew a larger purpose existed in the first place. Of course, student motivation suffers when there is a lack of purpose.

Northwest teacher Anne Salisbury said Tuva has helped boost student engagement in the graphing process.

“I think using Tuva has convinced them that graphing has a purpose,” she said. “The point is not just to make a graph; it’s to use a graph. In the past we spent so much time getting them to physically graph on paper, that we did not have time to analyze data that is already graphed and figure out what it means.”

Former Scientist Centers Data in Her HS Biology Instruction

Maria Lee, High School Bio Teacher

Before Maria Lee became a science teacher, she was an environmental scientist. Her experiences working on salmon restoration in Washington State influence her approach to instruction.

“Primarily, I understand the importance of data,” Lee explained. “Data is not perfunctory; it is really an essential part of understanding science.”

As such, Lee dedicates a substantial portion of her high school biology course at East High School in Salt Lake City to the mathematical aspects of scientific understanding. In the past when working with data, Lee has had students use premade graphs or make their own using spreadsheets. This year, she opted to use Tuva. Now Lee calls out Tuva as one of her three favorite curriculum supplements, alongside leading EdTech platforms Newsela and Nearpod.

Lee was introduced to Tuva last year, but was hesitant to use it, fearing the learning curve would be steep. When she finally dove in this year, she discovered her worries were unfounded. Lee noted she did not spend time learning the Tuva tools in advance, but learned them in tandem with her students.

“ After just one day of playing around with Tuva with my students, I felt so comfortable- nearly expert level.”

“Suddenly, They Could See It”

The reasons for Lee’s enthusiasm are multiple. First and foremost, incorporating Tuva has accelerated her students’ learning.

Her first unit, a riff off of OpenSciEd’s Ecosystem Interactions and Dynamics, leaned into analyzing and interpreting line graphs and scatter plots. Lee described a class in which students were identifying whether or not there was a relationship between rainfall and wildebeest behavior in the Serengeti.

An example of student work from Lee’s class. Lee pulled data from OpenSciEd into Tuva.

Up to this point in their OpenSciEd unit, they’d primarily used line graphs to observe change over time. Now, they were struggling with the transition to scatter plots, getting confused about time versus relationship.

Lee uploaded the data onto Tuva and projected a scatter plot of rainfall vs. wildebeest occupancy on her interactive whiteboard. Then she had students come up and trace the dots from left to right and try to describe what their hand was doing- going up, going down, staying steady, or moving erratically. Some students were getting it, but many were not. Lee had them activate the least squares line.

Then, she said, “suddenly they could see it!” The line helped them ignore the background noise and identify the trend.

Another student work sample, this time with a scatter plot.

Student Ownership

Lee also said she appreciates the level of ownership Tuva gives students in the process of data exploration.

“Tuva puts students first in their interaction with data, so that they are driving their interaction and learning with the data and not getting it secondhand through a teacher filtering it for them.”

This level of independence is possible, she said, because Tuva leaves room for making mistakes and fixing them. With other tools, she’s needed to be more prescriptive because mistakes are harder to recover from.

Academic Communication: Scaffolding Up

Finally, Lee credits Tuva with creating more opportunities for extended learning. For example, when working with a class of multilingual learners, she found that Tuva’s interactive graphing tools accelerated the learning process enough that some students had time to deepen their interpretation.

Her class was working on the Tuva activity Dynamic Wildlife. (You can interact with the Wolf and Elk in Yellowston dataset the activity uses below!) She asked all students in the section to use the Identify and Interpret ( I²) method to discuss the wolf and elk population data. For example, a student might identify, “I see the line goes up at the start of the graph,” and interpret, “This means the number of wolves was increasing.”

Try dragging and dropping Elk Count (observed) onto the Y2 axis.

Many students completed this task quicker than they would have with print resources or spreadsheets. Lee capitalized on the newly-freed time to teach them to add quantitative measures, such as year and population count, to their evidence.

Lee noted that her class’s work with Tuva fits perfectly into the larger district goals, such as strengthening academic discourse and writing. Referring to Tuva as a “writing-science interface”, she said, “It’s the only tool I know that is really actively improving reading and writing skills for science. ”