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Data Visualizations Make the Invisible Visible for Hands-on Learners

A strand of human DNA is a mere 2.5 nanometers wide. To put that in perspective, a sheet of printer paper is 100,000 nanometers thick. It’s no wonder high school microscopes are not powerful enough to enable students to view DNA! That poses a challenge for high school biology teachers, though. Anything at such an infinitesimal scale is abstract. It’s quite the trick to teach about how structure relates to function for something none of the students have ever seen.

High school science teacher Catia Wolff recognizes the need to make genetics more concrete for her students. This is especially true because of the population of students in her classes.

“Our school attracts students that prefer working on hands-on projects rather than a traditional setting,” explained Wolff.

Wolff teaches at the Rockland BOCES Hudson Valley Pathways in Technology Early College High School, more commonly referred to as Hudson Valley P-TECH. The program is part of the larger New York State P-TECH Program initiated a decade ago with dual goals of preparing students for high-skill, high-wage STEM jobs and ensuring employers have access to a talented and skilled workforce. Students who complete the program at Hudson Valley P-TECH graduate with both a high school diploma and an associates degree at Rockland Community College. The school tends to draw students with a talent for and affinity toward working with their hands.

Hudson Valley P-TECH attracts students who are hands-on learners.

Wolff carefully selects and sequences lessons to make genetics more tangible. Her process starts with three-dimensional modeling. She gives her students a DNA sequence. Students create a complementary DNA strand. Then they model the processes of transcription and translation, simulating how a cell carries out protein synthesis.

After completing this process, Wolff found her students were still struggling to understand how all the cells in our body can have such drastically different characteristics while housing identical DNA. They didn’t understand what gene expression means. They needed something more. What Wolff did next did not surprise P-TECH Guidance Counselor Allison Paul.

“Tuva caught my eye right away. Students can see it, they can manipulate it. They can do it. They’ll say, ‘Look what I made!’”

“She is always learning. When most teachers just want to take the summer off, she is taking a course. She is constantly learning and constantly trying to improve,” said Paul.

Instead of adding in a lecture, reading or video, Wolff began searching for resources that would cater to her students’ learning style. What she found was Tuva’s activity Genes: To Express or not to Express? That is the Question.

The activity uses a dataset curated from The Human Protein Atlas, a Swedish-based program with the aim to map all the human proteins in cells, tissues, and organs. Tuva’s dataset includes 35 different genes, the function of the proteins they code for, and whether or not those genes are expressed in the tissues that make up the eye, skeletal muscle, stomach and tongue. During the activity, students look for patterns of gene expression and hypothesize an explanation of the results.

“They had to figure out the puzzle. It made them really think. It really sparked conversation,” said Wolff

This dot plot shows the genes expressed in two different tissues: skeletal muscle and stomach. It has tissue type on the x-axis and gene name on the y-axis. The legend shows the function of the proteins created by the genes. Students can see that four genes are expressed in skeletal muscle that are not expressed in stomach tissues.

Throughout the course of the activity, students use Tuva’s drag and drop graphing tools to create visuals that help them compare the tissues and puzzle out why some tissues express certain genes while others do not. Wolf observed that having a model, a graph that students could actually see and manipulate, helped them comprehend how DNA connects to cell function.

“Tuva caught my eye right away,” Wolff explained. “Students can see it, they can manipulate it. They can do it. They’ll say, ‘Look what I made!’”

Reforging Human Connection Post-Pandemic… With Data

Schools across the nation report student behavior, engagement and academic performance have all dipped below pre-pandemic levels. In fact, one Stanford University study found that chronic absenteeism has doubled, jumping from 15% in the 2018-19 school year to 30% in the 2021-2022 school year. The reasons for this decline are complex and much-debated. Regardless of the cause, as educators, what do we do to motivate students?

“What I find works the best is you getting to know who they are and them getting to know who you are. Then they start wanting to do better,” said Doug Page, a veteran algebra one and AP statistics teacher at the Galileo Academy of Science and Technology in San Francisco. “It takes a while,” he admitted.

Page’s approach is not a new one. In 1985 psychologists Richard Ryan and Edward Deci introduced the Self-Determination Theory of motivation. Their research found that relatedness, autonomy and competence are three major drivers of human motivation. Their findings challenged Skinnerian behaviorism and changed the way educators approached student engagement. Less carrot/stick, more connection.

A Clever Way to Get Acquainted

Page has found a creative way to get to know students while simultaneously introducing them to some basic statistics- collecting data. During the first few days of his AP Statistics class Page disseminates a Google survey. Students answer a variety of questions about themselves. (Try it out here.)

Some of the questions are demographic in nature. How far do you travel to get to school each day? How many siblings do you have? Other questions focus on personal habits and preferences. Do you use social media? How many texts did you send yesterday? What sports do you play?

Page opens the data in a Google spreadsheet, removes student names so the data is anonymous and uploads the data to Tuva. He shares the data with his students via Tuva. Then, students can quickly ask and investigate questions about their class. In this way, Page gets to know his students, acquaints them with Tuva’s graphing tools, and introduces some foundational statistics concepts.

Unfiltered graph showing correlation between texts sent and texts received.

Filtered graph showing a correlation between texts sent and texts received.

Page used texting data from the student survey to teach about linear regression. Tuva’s simple filter tool enabled him to easily demonstrate how outliers can skew the data.

Page continues to use Tuva for a variety of assignments throughout the year.

“Sometimes I want to spend the time analyzing the data, not doing all the mechanics. The mechanics are a time sink. What I love about Tuva is that it is so drag and drop. You can do so many things so quickly.”

The Bigger Picture

Not only has Tuva enabled Page to connect with his students, it has also helped him harness data about school wide indicators of student engagement, such as tardiness and absenteeism, and use it to problem solve. For example, when Page noticed high levels of tardiness during first period classes, he began to collect data. He uploaded the data into Tuva, shared it with colleagues, and launched a productive conversation about how to improve first period attendance.

Teaching is a hard profession, but it’s never a dull one.

“Students always say, ‘Isn’t it boring teaching the same thing for 25 years?’” Page said. “But while the material stays the same, the people change. I tell them, it’s not you sitting in the same seat everyday.”

Page said it is these ever-changing personalities and the opportunity to connect to each kid and find out what motivates them that keeps him going.

Want to Try It?

After you make a getting to know you survey using Google Forms, it’s easy to import your own data into Tuva from the Google Spreadsheet it generates. Visit Tuva’s support section or click this link to learn simple steps for uploading your data in Tuva.

HS Chemistry Students Uncover Well Water Contamination

Student Data Shows Unsafe Arsenic Levels in ME Wells

An alarming 10% of Maine’s private wells are contaminated with arsenic, and many of the people who drink from those wells are unaware¹. It’s likely that around 38,000 Mainers unknowingly ingest private well water with arsenic above the Environmental Protection Agency’s maximum contaminant levels. That’s because only 56% of wells in Maine have been tested for arsenic. ²

Students in Jon Ramgren’s high school chemistry class are helping change that. Since 2018, Ramgren’s classes have tested more than 350 wells in the vicinity of Waterville Senior High School in south-central Maine. 16% of the wells his students have tested contained unsafe levels of arsenic, a known poison and carcinogen.

The ME Department of Environmental Protection deems arsenic levels greater than 10ug/L unsafe.

Ramgren was one of the first teachers to join All About Arsenic+, a school-based citizen science initiative begun in 2015 by Mount Desert Island Biological Laboratory and Dartmouth College’s Toxic Metals Superfund Research Program. Students have tested thousands of wells in Maine and New Hampshire as part of this project, which is funded by a National Institutes of Health Science Education Partnership Award.

“We are getting real-world data that no one has; we are adding to data that is limited, “ said Ramgren.

All sites on the map represent wells that hadn’t been tested for arsenic prior to the project. Data points in bright yellow show the locations where water samples were collected by Waterville students.

One important aspect of this project is helping students build data literacy. The program identified Tuva as the right partner to help their students explore and analyze the data they have collected and to make the data publicly available. Through the All About Arsenic + project data portal on Tuva, students can access all of the project data or can filter it to show only their school’s results.

“Most of the time in high school science,” Ramgren explained, “you are doing labs that are kind of meaningless in the sense of larger scientific data. No one is interested in the data some kid got about the density of copper.”

In contrast, interest in well water data has been high. Ramgren said test results have spurred some homeowners in his area to install arsenic-removing filters. The Maine Center for Disease Control and Maine lawmakers have been paying attention too.

“You can gather real information as a citizen scientist and actually contribute even if you are ‘only’ a high school student.”

In 2022, data collected by participants in the All About Arsenic program convinced the Committee on Labor and Housing to double their request for funding allocated to well water remediation³.

“It’s real data that people are using to inform public policy,” said Ramgren. “You can gather real information as a citizen scientist and actually contribute even if you are ‘only’ a high school student.”

Engaging in citizen science is more time-consuming than using canned labs and textbooks, but Ramgren said the extra time is worth it. Amongst the benefits lauded by Ramgren are stickier learning, authentic problem-solving as students wrestle with how to act upon the data, and a stronger understanding of the nature of science. Collecting and analyzing real-world data, students get a more accurate picture of how professional scientists experience data – with lots of variability, background noise, and messiness. Because creating graphs by hand is so time-consuming, students are often only asked to make graphs when there is a correlation between variables. As a result, students may erroneously expect there will always be a correlation.

But it’s important for students to realize graphs showing a lack of correlation are equally important. The All About Arsenic dataset that’s housed by Tuva includes 28 attributes, or variables. Students can use the Tuva tools to quickly make and explore multiple graphs- both those that show correlation, and those that don’t.

Ramgren is also hopeful that real-world data collection will help students avoid another misconception that plagues our society- the notion that science ideas are absolute and unchanging.

“Science is not static. We are constantly getting new information,” Ramgren said.

He thinks altering the way we teach science can help kids recognize science knowledge is subject to revision and improvement in the light of new evidence. When we have students redo experiments for which the answer is already known, we reinforce the impression that we know everything in science already. However, if students are actively adding to scientific knowledge, they will understand its fluid nature.

MDI Biological Laboratory. “All About Arsenic+.” All About Arsenic, 2023, https://www.allaboutarsenic.org/sepa/. Accessed 31 October 2023. ↩︎
Maine Centers for Disease Control and Prevention & Maine Department of Health and Human Services. “Private Well Water | Maine Tracking Network.” Maine Tracking Network, 2021, https://data.mainepublichealth.gov/tracking/private-wells. Accessed 31 October 2023. ↩︎
Viles, Chance. “Westbrook students’ science project makes impact in Augusta.” The Portland Press Herald, 1 March 2022, https://www.pressherald.com/2022/03/01/westbrook-students-science-project-makes-impact-in-augusta/. Accessed 31 October 2023. ↩︎

Fostering Systems Thinking in the Middle Grades

Foley Teaches Complexity Using Multivariate Graphs

Downpours in Uruguay, but drought in Peru. Enormous blizzards in the northeast, but steeply climbing worldwide temperatures. Climate science can be confusing… especially if you’re only 14 years old. Without an understanding of complex systems, it can seem downright contradictory.

That’s why Maura Foley, an earth and climate science teacher at the Hopkins School in Connecticut, devotes significant energy to helping her students investigate systems interactions.

“Today’s problems are complex problems, so we need thinking that is going to reach outside of these discrete zones of understanding,” said Foley.

Foley admits that understanding complexity is part of higher order, abstract thinking – an easier task for her high school students than her middle school students. However, she believes it’s essential to begin building the idea of complexity in the lower grade bands. In her middle school courses, the process of building that understanding starts with noticing how one thing affects another, which affects another, and so on.

“Today’s problems are complex problems, so we need thinking that is going to reach outside of these discrete zones of understanding.”

“What’s great about Tuva is being able to blow through looking at a whole bunch of variables quickly,” explained Foley. “They don’t need to be a spreadsheet expert to quickly go ahead and make 10 graphs.”

Foley uses Tuva activities and datasets consistently throughout her Surface to Space class in 8th-grade. Each dataset includes multiple, interconnected variables that students can drag and drop onto the axes to explore correlations.

October Weather in US Cities Datset Image — New England Ice-Out Dates, Global Change and October Weather in US Cities are a few Tuva datasets Foley’s students use to explore complex earth and climate systems.

When students first use Tuva, she said there tends to be some oohs and aahs as they watch the dots rearrange themselves. Then, Foley starts to hear students verbalizing the patterns they observe.

“Those observations are able to keep going and going because it’s not like I’ve handed them a simple graph,” said Foley.

Graph Showing Positive Relationship Between Humidity and Particulate Matter — This graph, created by an 8th-grader in Foley’s class, investigated the relationship between particulate matter and humidity.

Foley has observed that once students notice one relationship, they begin to wonder if other variables are related. Tuva allows them to quickly and easily satisfy their curiosity. It starts a deeper conversation in which students begin to comprehend the complexity of Earth’s systems.

In addition to middle school science, Foley teaches a high school elective: Engineering Nature. In this higher level class, Foley expects her students to apply their understanding of interconnectedness to tackle pressing issues, such as climate change. Last year her students made biogeochemical terrariums and tracked CO₂ concentrations over time. Then they geoengineered the terrariums to reduce the CO₂ concentrations.

Foley’s emphasis on interconnected systems when these students were in the middle grades prepared them to take on this rigorous geoengineering challenge. In the long term, Foley‘s students will be prepared to face the complex problems of a complex world.

Stats Teacher Drives Engagement with Authentic Data

Relevance Prompts Annie Pettit’s Students to Dive Deeper

Annie Pettit has taught a variety of different high school math courses during her 18-year career, but her self-professed passion is statistics. She gravitates toward stats because it provides multiple opportunities to use real-world data.

“As much as possible, I try to get data that relates to the kids, data they are excited about or that they can relate to,” Pettit said.

histogram comparing points scored/season by Kobe Bryant, LeBron James and Michael Jordan — Engagement is high when the data is relevant to kids, such as the NBA statistics this student analyzed to determine the GOAT (greatest of all time).

Pettit designs her statistics course at Des Moines Christian School so that assignments earlier in the year are more supported. First, she acquaints students with the Tuva graphing tools using a premade Tuva activity with step-by-step directions. Then, she assigns a simple comparison project in which students can use these tools to create dot plots and box plots about data that’s personally meaningful. Students select a topic independently. DC Comics vs Marvel Comics profits, passing yards from last season’s football season, viewership for The Office vs. Friends, the US women’s national team vs the men’s national team – the topics are as varied as Pettit’s students.

Pettit continues to ratchet up the rigor throughout the year, so that by quarter four students are ready to “do stats like a statistician does” using large datasets and lots of variables.

“If you put data in front of them that interests them… they actually want to find out the answer instead of doing it to get it done.”

Last year she gave her statistics students five options of datasets to choose from for their final project: electric cars, basketball statistics, baseball statistics, state crime rates or movie production budgets. Pettit noted that real-world data motivates students at all different levels – those to whom math comes easily, and those who have to work a bit harder to master statistical concepts.

“If you put data in front of them that interests them or if they get to pick their own data, they actually want to find out the answer instead of doing it to get it done.”

A few graphs from one student’s final project about electric car performance.

The depth and insight shown in her students’ work is a testament to their level of engagement. For example, a student investigating electric car performance reflected that statistical analysis often challenges our assumptions, and variables that seem like they would be correlated don’t correlate at all.

Pettit prefers to have students analyze these authentic datasets in Tuva where they can focus on justifying their conclusions instead of on the logistics of making the graphs.

“Tuva makes statistics come alive!” Pettit said. “Tuva allows my students to be statisticians. They are able to analyze big datasets and draw conclusions from data they find relevant to their lives.”