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Ready or Not for an AI Economy: How U.S. Students Stack Up

Data science is rapidly becoming a critical skill, and the United States has a lot to learn from global peers in equipping students for the coming economic shift.

That’s the conclusion of a new report released today by the University of Chicago’s Data Science 4 Everyone program, which analyzes global assessment performance and international approaches to teaching data science, which blends statistics, data analysis, and computer science.

“Artificial intelligence has triggered a global talent race, and whichever country is able to find the talent to not only build AI tools, but more importantly, effectively implement the technology economywide, will quickly shift the economic pecking order,” said Zarek Drozda, the executive director of Data Science 4 Everyone. “I think we [in the United States] are setting ourselves up to quickly build technologies our broader population does not understand, nor will be able to effectively leverage. To maintain U.S. competitiveness, we need to create broad, population-level data literacy.”

The Program for International Student Assessment, also known as PISA, began measuring students’ understanding of data and uncertainty in 2022. An analysis of its data shows U.S. 15-year-olds perform at level two out of six, or basic achievement, meaning they can begin to apply simple data concepts. Nearly a third of U.S. students did not meet basic achievement in uncertainty and data concepts.

That’s roughly at the international average for data content, but U.S. students performed on average a full level below other industrialized countries like Canada, Korea, Japan, and the United Kingdom, and two full levels below teenagers’ performance in four Chinese education systems, including Singapore and Hong Kong.

While more than 4 in 10 Singapore teenagers performed in the highest two levels in data and uncertainty concepts on PISA, slightly more than 1 in 10 U.S. students displayed the same level of mastery of data skills.

The performance echoes trends on national tests. U.S. 4th, 8th, and 12th graders have similarly shown a decade of declining performance on data and statistics concepts on the National Assessment of Educational Progress.

The report suggests U.S. educators can steal a page from international neighbors to catch up. For example, a coalition of educators, researchers, and statisticians from countries including Canada, Germany, and New Zealand have created a high school data science sequence of courses which is usable across countries and education systems.

The National Council of Teachers of Mathematics last week called for data science to become an official math credit course for high school graduation requirements.

“It is essential that students understand data so that they can comprehend the massive amounts of information that they encounter on a regular basis and is available at their fingertips,” the NCTM board says in its new position statement. “High-quality experiences working with data expose students to new and different kinds of content that can energize and motivate them and enable them to see many uses for mathematics to make sense of the world around them.”

Utah is now expanding a pilot program created to teach K-12 math teachers how to incorporate statistics and data science, to likewise train computer science teachers in the state, and create more cohesive courses, according to Lindsey Henderson, a secondary math specialist at the Utah state board of education. Henderson worked with business leaders and higher education institutions in the state to create dual-credit programs in data science and statistics in high school.

Nationwide, though, approaches to data literacy remain scattershot, said Drozda.

“In general, other countries are simply moving much faster than us in ensuring their population is data literate,” he said. “The U.S. is still in pilot phase, and other countries are hard-coding modern data education for all students through policies with more robust scale.”

China, for example, overhauled its national college entrance exam in 2018 to focus heavily on data and statistics, and has required that content on data science, machine learning, and artificial intelligence be taught to every student in grades 8-12.

Five U.S. states—Arkansas, California, New Jersey, Oregon, and Virginia—have increased statistics and data science topics in their K-12 math standards, however, no state now requires a data science course for graduation.

“So far, we are investing a fraction of what our international peers are for capacity development in educator training, for standards modernization, for updating assessments,” Drozda said.

“We’re seeing countries do these things concurrently … building up coding or computer science programs, introducing stronger emphasis on data literacy and statistics along with exposure to AI tools. It is really the synthesis of these skills that will create a workforce that is prepared for emerging technologies.”

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