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Although data science is not a new topic, recently there has been a growing interest in helping students – in K-12 and higher education – acquire data science skills.
One reason is a changing job market, said Zarek Drozda, director of Data Science 4 Everyone, a national initiative based at the University of Chicago. “The main skills in demand today are data analysis and interpretation, as well as the ability to communicate about data,” Drozda said. “It’s hard to find a career or sector of the economy where data skills aren’t important. »
With the rise of artificial intelligence tools like ChatGPT that leverage data sets, students also need to understand how to use AI responsibly, he added.
The adoption of data science education did not happen without controversial. In 2020, some public universities in California allowed applicants to skip Algebra II and substitute data science. Universities backed off their efforts this year after experts said students were taking less demanding courses, limiting their postsecondary education opportunities.
No state is currently getting rid of algebra courses in favor of data science, Drozda said. Some instead offer this subject as an additional option for students. Over the past three years, 17 states have added some sort of data science education course to their K-12 offerings, Drozda said.
“There are opportunities to reduce barriers to entry, but the benefits are high, so that students can see existing school subjects in a context that is relevant to their daily lives. »
Zarek Drozda, director of Data Science 4 Everyone, a national initiative based at the University of Chicago
In higher education, data science is often housed within a particular school or limited to a field of study, such as mathematics or computer engineering. But North Carolina State University is taking a different approach to teaching the subject, said Rachel Levy, executive director of the school’s new Data Science Academy. NC State launched the academy two years ago to introduce the use of the subject across disciplines, from biology and art to English and history.
To help its 10 colleges introduce courses integrating data science, accessible to students of different levels, the university adopted the data science model across all campuses through an accessible teaching and learning model based on projects, or ADAPT. Examples of interdisciplinary courses available to students at any college include “Introduction to Data Visualization,” “Introduction to R/Python for Data Science,” and “R for Biological Research.” Courses are project-based, and history or English majors can choose to focus their class project on applying the use of data science to a topic in their major. Students are also encouraged to apply the skills they learned in these courses to other courses not related to data science.
The university’s College of Education also uses the ADAPT model to prepare future K-12 teachers. With federal grants, NC State researchers are studying the model and its impact on teaching and learning. Meanwhile, the Data Science Academy is collaborating with the state Department of Public Instruction, hoping to roll out data science education to schools across the state, according to Levy.
Taryn Shelton, K-12 data science coordinator at the academy, said the goal is not to add yet another thing to teachers’ jobs, but to help them use data to enrich their lesson plans and expose students to data science skills early on. His team works with school districts outside of the tech- and research-heavy Raleigh-Durham-Chapel Hill Triangle region, as well as more rural and underserved districts, to help educators integrate concepts of data science in their program. “Shelton’s team also organizes events like mini hackathons where high school students can work with data.
“There are many ways, across disciplines, that teachers can import data,” said Levy, the academy director. Social studies teachers can help students explore data about people, places, events and cultures, she explained, while English teachers can ask their students to identify and to count words or expressions that help create a particular atmosphere in a piece of writing.
If teachers introduce data science in ways that are authentic and tailored to students’ interests, Levy said, their comfort with the subject will increase. “People of all ages can harness data in useful, meaningful and empowering ways,” she said.
The current challenge, said Drozda of Data Science 4 Everyone, is that most students don’t learn about data science until they take AP Statistics or Intro to Data Science toward the end of high school, if at all. But it doesn’t have to be that way. Drozda and Levy envision data science being integrated into the primary and secondary school curriculum, with teachers using data sets in biology units about ecosystems, or to analyze economic booms and busts in social studies.
“It will be very important for students to become familiar with the data science way of thinking, as well as computing and technology tools,” Drozda said. “There are opportunities to reduce barriers to entry, but the benefits are high, so that students can see existing school subjects in a context that is relevant to their daily lives. »
This data science story was produced by The Hechinger report, an independent, nonprofit news organization focused on inequality and innovation in education. Register at Hechinger Newsletter.
