Do Kids Need to Learn to Graph by Hand? 

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.

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.

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.

“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. ”

Engaging Students with Data That Hits Close to Home

View from the Classroom

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 Teacher Emily Harer

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.

  1. Find data and, if it is not already, put it into a spreadsheet.
  2. Upload the data on Tuva.
  3. 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:

  1. Lack of a clearly-stated driving question or claim to investigate
  2. Overwhelming tendency to plot data in bar graphs as a default graph choice.
  3. 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:

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

                                       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.