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?”

          Tuva Bolsters Multilingual Supports

          Multilingual learners are the fastest-growing population of K-12 students in the U.S. Throughout the past six months, Tuva has taken a number of steps to ensure multilingual learners have access to rigorous STEM instruction.

          1. Added a Keywords Feature

          Academic language differs markedly from the language used in everyday social interactions. Fluency in conversational English doesn’t equate to academic fluency. That’s because social vocabulary is usually acquired within two years, whereas academic vocabulary can take up to 10. 

          To support students as they develop academic vocabulary, Tuva has added a keywords feature. When students click on an underlined keyword, a definition will appear. These definitions are written at a 6th-grade reading level.

          Many of Tuva’s keywords are tier two vocabulary words.

          Tuva’s keyword feature defines terms not frequently heard in everyday conversation. This includes tier two vocabulary, academic language used across multiple subject areas (e.g. clarify, analyze, compare), and tier three vocabular, disciplinary-specific terminology (e.g. photosynthesis, velocity).

          Other keywords are tier three vocabulary.

          2. Authored WIDA-Aligned Lessons

          Using the WIDA framework, we’ve begun weaving multiple English language development supports into our math and science lessons. 

          Language Support Tips

          All learners, but especially multilingual learners, benefit from explicit instruction in academic language usage. Our newest lessons include language support tips such as sentence starters, instruction on parts of speech, or lists of helpful phrases for using data to inform, explain, or argue.

          This image is a screenshot of Tuva showing a language support box. It says, "You may see different word forms of correlate. Each on serves a different purpose in speech. Verb- correlate/correlates, noun- correlation, adjective- correlated."

          Prompt Discourse 
          Conversation with peers helps multilingual students build a more nuanced understanding of STEM concepts. It also gives learners more opportunities to practice academic vocabulary and language. 

          We’ve made a concerted effort to promote discourse in any new full-length lessons and activities. By adding “Discussion” prompts to the lessons, we encourage students to discuss their thinking aloud.

          Try One of Tuva’s New WIDA-Aligned Lessons

          Elementary STEM: Preparing for a Hurricane

          A map of North America. The states are green. A white, spiral-shaped cloud is above the southeastern portion of the United States.

          Secondary Math: Tackling Correlations

          Six American football players in a pile. Some wear golden helmets, others wear black helmets. They are tackling someone.

          Secondary Science: Sun Seekers of Turtle Island

          The picture shows a rock with a spiral carved into it. A beam of sunlight hits the spiral.

          Check out our other lessons intentionally designed to support multilingual learners. View our secondary STEM lessons with ELD support or our elementary STEM lessons with ELD support.

          3. Enabled Easy Language Translation

          Translanguaging boosts STEM comprehension. It also accelerates English language development. Thus, Tuva has prioritized making it easy for users to switch between languages in our lessons. Simply click the three dots in the upper right of the instruction panel to make the Google Translate widget appear.

          A screenshot of Tuva which shows the portion of the screen where the three dots can be found.

          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 ManuallyPractice With Tuva
          Setting up equal intervals and scaling them appropriatelyPlacing variables on the correct axes
          Accurately plotting pointsChoosing 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.”