A world full of data requires new data science standards. This is our recommendation.


For more than a century, people have been calling for improving “statistical literacy.” In recent years, with the rise of big data and fast computing, this call has become louder and louder.

However, the reality is that the number of students failing in algebra courses is unacceptably high, and these courses focus students on outdated methods and manual calculations.These outdated courses hinder many students’ persistence in the STEM field and exacerbate the inequality prevailing in the United States

A different method of teaching mathematics is needed-to cultivate data literacy for all students. This approach will not only be more relevant and increase student engagement, but it has the potential to reduce the general vulnerability of misleading information shared through social media.

Studies have shown that students are not fully prepared to become key consumers of data and online resources, which leads people to worry that our democracy will be threatened, and our democracy depends on voters’ ability to distinguish between authenticity and falsehood. On the other hand, the emerging field of data science is defined as a synthesis of statistics, mathematics, and computer science, which is expected to provide students with powerful problem-solving strategies that they will use in the workplace and daily life. And you need people who can use data for reasoning in almost all jobs in all sectors of the economy.

For today’s K-12 educators, this is a challenge: how can teachers make young students interested in the new subject of data science.

But for a long time there has been a flaw: the lack of data science standards. Even if schools and regions across the United States recognize the need for data literacy, this situation continues; some state frameworks call for attention to data literacy (such as the California Mathematical Framework 2021); interdisciplinary teachers develop their own data courses.

Although data science is interdisciplinary, one possible destination of data science standards is mathematical standards, because there are important mathematical tools and methods to support data science. Another possibility is a set of independent standards independent of mathematics—increasing the possibility of developing truly interdisciplinary methods to develop students’ data acuity. In both cases, it seems mature now to plant a flag on the ground and provide ideas for the development of data literacy and data science for each grade. Such standards can prepare students for entry into middle and high schools, and are supplemented and deepened by high school data science courses. Some states and universities are now accepting as an alternative to Algebra 2.

In the high school stage, professors form the synthesis of mathematics, statistics, and computational thinking in data science, which can not only guide students to pursue important and high-paying careers, but also eliminate inequalities in the calculus path. In most parts of the United States, students with excellent grades will participate in the so-called “calculus competition”, that is, absent from middle school courses in order to reach the peak of calculus. However, research shows that most students who study calculus at school will retake or study lower-level courses at university.

The need to compress courses to achieve calculus also means that most students are filtered out in middle school, and the students selected are disproportionately white and male. Data science provides a fairer alternative to calculus, does not require secondary school tracking, and connects with students’ daily lives and communities, raises awareness of social justice issues, and attracts a wider group of students.

This will not be a lower-level approach, because data science is a rigorous subject, rich and important for many different university majors in STEM subjects and humanities. The National Academy of Education recently called for the establishment of high school courses that involve students in civic reasoning—focusing on the mathematical content and practices in currently available data science courses. One example is the introductory course in data science jointly developed by UCLA and the Los Angeles Unified School District and Youcubed: Explorations in Data Science at Stanford University.

In this related publication, we have developed a set of standards based on the PreK-12 Statistical Education Evaluation and Teaching Guide of the American Statistical Association. An important quality of these standards is that at each level, they are included in the data survey cycle. Data science should not be taught as a set of interrelated methods, but as a method of solving problems with data, highlighting mathematical content and practice. As students progress, they will actively participate in this problem-solving survey cycle and increase its complexity. Although important knowledge is listed for each grade, the knowledge is interconnected and developed as part of a coherent whole. The data cycle we envision is as follows:

Data Science Illustration

Our goal in developing these standards is not to claim that they are the only way to develop data literacy through performance, but to raise awareness and start or enrich the conversations that take place across the United States. Some of the key points of the data science standards we recommend include cultivating students’ curiosity about events that can be considered with data in their lives, learning to ask their own statistical survey questions on topics that interest them and affecting them, and face the challenges of data collection and analysis. Ethical influence.

However, establishing standards is only the first step. There is still a lot of work to be done in preparing educators to teach data science, setting expectations for parents, and ensuring the resources needed. Organizations across the country are working together to spread awareness of data science needs in schools (see, for example, The Messy Data Coalition and Youcubed’s data science resources), and are producing online courses to help teachers master important knowledge and teach them The required teaching methods (for example, see our own YouCubed program. In addition, the American Statistical Association has an extensive library of teaching resources.

But standards are an important part of this puzzle, and we hope that it can open up further work and consideration-improving the content field that is currently in its infancy, but it may be the most important content in cultivating data-literate citizens, and obtaining authorized navigation and Learn about their data-filled world.

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