Integrating data analysis into subjects such as social studies can make math and data science more accessible for students, according to experts quoted in a K-12 Dive article.
“Anytime we can cross over disciplinary lines without abandoning the core principles and practices of the disciplines, learning becomes more engaging, especially when it’s relevant,” says Tina Ellsworth, president of the National Council for the Social Studies.
Data skills and data analysis are an emerging fundamental basic that every student needs by the time they graduate from high school, says Zarek Drozda, executive director of Data Science 4 Everyone. But the vast majority of K-12 students in the U.S. are not receiving data science education.
Connecting data science to various academic subjects is especially important during middle and high school, where subjects tend to be siloed. “What data science offers for the school subject of mathematics is that connective tissue to show why quantitative analysis is important and what it can do if you’re excited about history or economics or ecosystems in biology,” says Drozda.
How to incorporate data analysis into the broad curricula?
Start by looking at state standards that already need to be covered, says Drozda. Don’t rely on a textbook chapter but find one of many readily available data sets online that cover that topic, he says.
Data sets can explore the history of World War II, the cycle of economic booms and busts, and the growth of certain species in an ecosystem, he points out.
Teaching how to grapple with data allows students to discover the subject matter themselves, which makes learning more engaging and more likely to be retained, says Drozda.
Social studies professionals should include data literacy because of all the data our students are engaging daily that they may not yet know how to make sense of, says Ellsworth.
Analyzing data can strengthen social studies curricula, and social studies can bolster data literacy by teaching students to question the data and its source to assess different biases.
To support cross-curriculum collaboration, administrators and principals should provide educators effective professional development, says Drozda.
Data Science 4 Everyone has a network of curriculum and professional development teams that can partner with schools or districts to host workshops on teaching strategies.
Teachers should also access data analysis software, such as Excel spreadsheets and other education-specific tools to help build lesson plans, Drozda recommends.
“We’ve seen again and again the power of giving the math and the science teacher a couple of hours to sit down together and really plan out a couple of units jointly or think about where they see an intersection between their content at a particular grade level,” he says.
One challenge: there is often not a perfect alignment between math standards and social studies or science standards.
“Mathematics is an obvious home base for data science, but we would lose a major opportunity if we didn’t make clear connections to other school subjects where we know it can be a beneficial teaching strategy,” Drozda says.
K-12 Dive
