For more than a century there have been calls to increase “statistical literacy.” And such cries have grown even louder in recent years, with the rise of big data and fast computing.
The reality, though, is that an unacceptably high number of students fail algebra courses, which focus students on outdated methods and calculations performed by hand. These antiquated curricula have discouraged many students from persisting in STEM fields, and exacerbated inequities prevalent in the U.S.
A different approach to teaching mathematics is needed—one that develops data literacy for all students. Not only would such an approach be more relevant and increase student engagement, it has the potential to reduce the widespread vulnerability to misleading information shared via social media.
Research has shown that students are not being well prepared to be critical consumers of data and online resources, which has led to concerns about a threat to our democracy, which relies on voters’ ability to sort truths from lies. On the other hand, the emerging field of data science, defined as a synthesis of statistics, mathematics and computer science, promises to provide students with powerful problem-solving strategies they will use in the workplace and their daily lives. And the need for people who can reason with data in almost all jobs in all sectors of the economy.
For K-12 educators today, this represents a challenge: how can teachers infuse in their young students an interest in the new discipline of data science.
But there has long been a missing piece: a lack of standards for data science. This situation continues even as schools and districts across the U.S. recognize the need for data literacy; that some state frameworks call for attention to data literacy (such as the 2021 California Mathematics Framework); and teachers across subject areas develop their own data lessons and courses.
Although data science is interdisciplinary, one possible home for data science standards is in mathematics standards, as there are important mathematical tools and methods that support data science. Another possibility is a separate set of standards that stand apart from mathematics—increasing the possibility to develop a truly interdisciplinary approach to developing students’ data acumen. In either scenario the time seems ripe for planting a flag in the ground and offering ideas for the development of data literacy and data science through the grades. Such standards can prepare students as they move through middle school and high school and be complemented and deepened by a high school data science course, that some states and colleges now accept as an alternative to algebra 2.
At the high school level, teaching the synthesis of mathematical, statistical and computational thinking that make up data science can lead students not only to important and well-paying careers, but it can also eradicate the inequities built into the calculus pathway. In most districts in the U.S., high-achieving students engage in what is known as a “race to calculus,” missing courses in middle school to get to the calculus pinnacle. Yet research shows that most students who take calculus in school repeat it or take a lower-level course in college.
The need to compress courses to reach calculus also means that most students are filtered out of the pathway in middle school, and the students chosen to go forward are disproportionately white and male. Data science provides a more equitable alternative to calculus that will not require middle school tracking, and will connect with students’ daily lives and communities, raise awareness of …….