Tuva for Science Education

Engaging Students with Data That Hits Close to Home

In 2023, Minnesota saw an unprecedented 22 air quality alerts in just 52 days. And for one day in mid-May 2024, St. Paul held the unenviable position of worst air quality in the United States.

6th-grade Earth Science Teacher Emily Harer saw potential for authentic science learning in the unfortunate air quality downturn. Air quality issues are a suitably complex issue. Since the publication of the Next Generation Science Standards in 2013, major emphasis has been placed on anchoring science learning in complex phenomena. Even better, it was a phenomenon her students could immediately relate to.

“National curriculum is often focused on things that aren’t local,” she explained. “Having local phenomena is extremely important for students to latch onto.”

Harer, who teaches at Global Arts Plus Upper School in St. Paul, said she wants students to know that science is all around them and that they can contribute to the body of science knowledge through research and data collection. That’s much easier to do if the phenomenon they’re studying is local and relevant.

Putting Local Data into Students’ Hands

During the 2023-24 school year, Harer engaged her students in a month-long air quality unit. Throughout the unit, Harer had her students investigate the myriad factors contributing to air quality. Using historical weather and air pollution data from the National Weather Service and the Environmental Protection Agency, Harer created datasets using all local data. Then she uploaded them into Tuva and embedded them into the lessons on her class website.

Harer’s students can use Tuva tools to manipulate the data right on her class website because she has embedded the datasets on it.

“It was exciting to see students think about experimental setup, drag and drop the attributes, to then find answers to their questions,” said Harer.

Hosting the data in Tuva allowed her students to more easily interact with it and to look for relationships between particulate matter and other variables such as wildfires, rainfall, seasons, and land cover.

Students were able to manipulate the data to determine when wildfire smoke was in the air in Ramsey County in 2023. They saw the daily changes in particulate matter through time and could point directly to when the wildfire happened.

A student uses Tuva to explore the variables that impact St. Paul’s air quality.

The complexity of the phenomenon prompted students to generate new questions as they encountered unexpected findings. For example, when they compared ozone and temperature data in Ramsey County to Voyageurs National Park to the north, they realized that their prediction was actually opposite to what the data showed. Voyageurs National Park had substantially more ozone than Ramsey County in the spring. This cognitive dissonance spurred further inquiry and research.

Outcomes: Engagement and Deep Understanding

The combination of real-world, local data and Tuva tools is one Harer plans to repeat for two reasons: engagement and depth of understanding.

“I don’t usually see people getting that jacked about graphs,” admitted Harer.

Memorable student reactions when playing with the data on Tuva included: “Oh wow! Oh my gosh, I just did that!”, “Whoa! The rain washed that particulate matter out!!” and, “Dang! This is really life… in St. Paul.”

Engagement drove learning. By the end of the unit, students really understood particles in the air and were asking deep questions about weather, topography, vegetation, and air quality – startling high-quality questions. Jason Johnson, chief engineer at TSI Inc., a Minnesota-based company that designs and engineers air monitors for scientific research, visited the class near the end of the unit. During his visit, he projected a graph from his graduate program and was surprised at the students’ insightful observations and questions.

“They are 6th-graders, and they understand this so deeply!” he told Harer.

The graph Jason Johnson shared with Harer’s 6th-graders during his classroom visit, from “Engines and nanoparticles: a review” David B. Kittelson Journal of Aerosol Science. Volume 29, Issues 5–6, 1 June 1998, Pages 575-588

Taking it Even Further

This year, Harer plans to expand the project to include data collected by instruments on the roof of the school buildings. The campus has a weather station. Last year, Harer was able to use grant funding from the National STEM Scholar Program to purchase and install a BlueSky air quality monitor as well. By the time her Air Quality Unit rolls around, she will have a full year of data from these instruments. She anticipates that her hyper-local weather and air quality data will be even more engaging for her students and will help them understand how science fits into their lives.

Emily Harer poses beside the school’s new BlueSky air monitor with Dr. Lucy Rose from the University of Minnesota Department of Forestry Resources. Rose assisted with the project.

“I see science everywhere. When kids do too, that is so exciting” she said. “I want kids to see how cool Minnesota is and that we have a lot to offer here.”

Incorporate Local Data into Your Lessons

Uploading data into Tuva and sharing it with your students is simple. Here are the steps and, in case you need help, links to our associated support pages.

Find data and, if it is not already, put it into a spreadsheet.
Upload the data on Tuva.
Share the dataset by assigning it to your class or embedding it onto your website.

Tuva for NGSS or: How to Bring Today’s Climate Science News Into the Classroom.

A few days ago, the World Meteorological Organization (WMO) issued a press release announcing that globally averaged Carbon Dioxide (CO2) levels in the atmosphere have reached the symbolic and significant milestone of 400 parts per million (ppm) for the entire year. The UN agency made a bold prediction, painted a bleak picture if the trend continues, and declared that this marks the start of a new era of climate reality.

From the WMO press release:

“CO₂ levels had previously reached the 400 ppm barrier for certain months of the year and in certain locations but never before on a global average basis for the entire year. The longest-established greenhouse gas monitoring station at Mauna Loa, Hawaii, predicts that CO₂ concentrations will stay above 400 ppm for the whole of 2016 and not dip below that level for many generations.”

Next Generation Science Standards & Enabling Students to Explore CO2 Data

Did you know that the historical Atmospheric Carbon Dioxide data from the Mauna Loa Observatory is in the Tuva Datasets Library?

Tuva’s Atmospheric Carbon Dioxide (1958-2015) (Tuva Premium subscribers only) dataset enables your students to wear the hat of a meteorologist. Students can easily explore, visualize, analyze, and model the atmospheric Carbon Dioxide data from the Mauna Loa observatory, just like the meteorologists at WMO.

They can explore critical questions such as:

a. How has the globally averaged CO2 levels increased over the last 50 years?
b. When did the average CO2 level first past the 400ppm threshold?
c. How does the average CO2 levels vary by seasons, and why?
d. If this increasing trend continues, what will be the global average CO2 levels in 2050?

Cultivating students’ scientific habits of mind, building their capability to engage in scientific inquiry, and engaging them in practices that reflect those of scientists, researchers, and engineers is one of the primary goals of the Next Generation Science Standards.

At this watershed moment in climate science and human history, Tuva enables you to realize this vision with your students.

Graph Choice Chart

This blog post is written by Molly Schauffler. Dr. Schauffler is an Assistant Research Professor at the University of Maine School of Earth and Climate Sciences.

So, your students have some data. Now what?

One teaching challenge is how to guide students to apply math skills when they “analyze and interpret” data in the context of learning content in other subjects – science, or history, or social science.

Many educators see students stumbling to apply basic data literacy skills such as organizing data, graphing, fitting lines and thinking statistically in general, when working outside of math class with real (often “messy”) data about the real world.

In our work in the Maine Data Literacy Project over the last six years, we observed, over and over again, three glaring weak spots in students’ data literacy:

Lack of a clearly-stated driving question or claim to investigate
Overwhelming tendency to plot data in bar graphs as a default graph choice.
Absence of discussion about variability in data.

We wondered: how could we help students frame clear, statistical questions to drive their inquiry? How could we help them make reasoned decisions about how to visualize data as evidence? How could we help them begin to see, describe, and make sense of variability in data?

And so, the Graph Choice Chart was born. The Graph Choice Chart (GCC) proposes five types of questions that students are likely to investigate:

Questions about variability within a group
Questions about comparing groups
Questions about Correlations
Questions about change through time (a special kind of correlation)
Questions about how a group is proportioned into sub-groups.

Screenshot of the Graph Choice Chart

Students are prompted to clearly state (write out!) a complete question in one of these forms, and then to follow a decision tree for choosing – based on the nature of their question – what kind graph would make sense to use for developing their evidence.

In addition to helping students clarify the purpose of their analysis, the GCC maps a framework for building statistical thinking and language, informally at first.

Students make deliberate decisions within the framework about what kind of question to ask, what kind of data are needed (categorical or quantitative), and what kind of graph makes sense for visualizing evidence.

It prompts them to adopt language for visualizing and describing variability in data, an underpinning of reasoning about data. Once they master these skills, they are ready to move beyond the GCC to more complex kinds of questions and more quantitative analyses.

Tuva is a perfect environment for putting the Graph Choice Chart to work, whether students are collecting their own data, or working with Tuva Datasets.

In future blogs, we’ll talk more about how early informal focus on variability supports the Common Core Math Standards and is a key underpinning to data literacy.

Download the Graph Choice Chart by logging into your Tuva Dashboard or the Tuva Resources section, and share it with your students and colleagues today.