Elementary school teacher Lisa Parisi is trying to teach her students a new kind of literacy.

By the time fifth-graders enter her class at Denton Avenue School in New Hyde Park, NY., they are about 10 years old and have developed basic reading, writing, and math skills. They are less comfortable, Parisi found, handling data.

Parisi is part of a growing movement of educators creating lesson plans to teach students to collect and analyze data — even in subjects outside numbers-
intensive math and science. She hopes to prepare them to eventually fill the shortage of qualified science, technology, engineering and math professionals, but also to derive opinions from measurable, real-world data.

The United States faces a shortage of between 140,000 and 190,000 professionals with analytical expertise, and 1.5 million managers and analysts who can make decisions based on big data analysis, according to research by McKinsey, the management consultancy. Although the number of data-related graduate and undergraduate programs in the United States has grown rapidly in the past couple of years, there has been less interest in data programs in schools, said Michael Chui, a McKinsey Global Institute partner.

Last year, 361,000 high school seniors in the United States took the Advanced Placement calculus exam; less than half that number took the AP statistics exam, Chui and McKinsey Global Institute Director James Manyika noted in a report.

“It makes sense for us to be thinking about education, starting in early childhood, about concepts such as the difference between correlation and causation, what it means to have a bias as you think about data, conditional probability. These are things we as humans don’t naturally do . . . these are learned [concepts],” Chui said in an interview. He added that curricula should teach students about the realistic limitations of data sets — extraneous information, or sampling error, for instance.

Parisi’s class is studying governments, so she asks students to analyze data sets reflecting state and national policy, she said. For the past few weeks they have been examining salaries, categorized by gender.

Parisi uses data sets from Tuva Labs, a New York-based tech start-up that collects real data that educators can use to illustrate statistical concepts. Tuva Labs also develops software that lets students make graphs, track trends or upload their own data sets. In its first two years, Tuva Labs, backed by early-stage venture funding, offers the service free to teachers. About 2,800 schools in 55 countries use Tuva Labs, including the District’s Maury Elementary School, Thurgood Marshall Academy and Capital City Public Charter School, among others.

In the early stages of her new lesson plan, some students struggled to incorporate data into social studies, Parisi said. When she asked them to explain the discrepancy between men’s and women’s salaries, “they’re coming up with the most non-connected ways to explain it,” she said. One student thought an oversupply of women in the workforce might drive down wages.

“I’m like, ‘Where did you get this information?’ . . . It wasn’t in the data. It’s not in any data you can find,” she told the student.

Vanessa Ford, a science coordinator at Maury Elementary School, teaches data analysis skills to her students, who range in age from pre-school to fifth grade. Ford started incorporating more data-collection exercises into her lesson plans after noticing more emphasis on statistics in various national assessments.

In the past year, her classes have been collecting their own data. Every day at 1:40 p.m., her third-grade students measure the temperature outside, tracking changes over the year. Her fifth-grade students are measuring hours of daylight and how it relates to the Earth’s rotation. Her kindergarten class is recording predictions for whether it will be sunny outside the next day, or which foods will decompose fastest, along with the results.

“My hope is that they see data is accessible, and that it’s doable and that it’s meaningful, and if that’s the case, then they’ll want to keep using it,” Ford said. “I have students who think that they hate math but cheer when they take out their weather graph.”

Eventually, Ford plans to upload some of these data sets into Tuva Labs so other classes can analyze them. “It’s neat for them to see their data is meaningful.”

Ford said she and the school are still working out how to evaluate whether this approach to data analysis is effective. For now, she checks papers to see if students can graph data correctly and understand the process of estimation or predictions. But what is more telling, she said, is “ultimately if the kid’s able to have a conversation about it and ask questions about it.”