The Problem With Saying High School Teachers Can’t Teach Data Science

Our monthlong blog series continues this week as we deconstruct another common claim about high school data science courses. 

Last week we broke down the problem with saying that data science education isn’t rigorous. In that piece, Executive Director Pamela Burdman explained that high school data science teaches high-order skills and competencies with real-world tools such as the programming languages Python and R. And creating standards for teaching data science courses will only help ensure rigor and student success across the board. 

That brings us to the third claim we are debunking:

CLAIM: High school teachers don’t know how to teach data science. 

FACTS: It is true that few high school teachers are well-versed in teaching data science. That is why investing in professional development is so important. If a lack of teaching experience were dispositive, curriculum would never change. While it may be an important consideration when talking about implementing data science courses, this cannot be a serious argument against high school data science. 

The idea that teachers cannot teach data science because data science has not yet been a regularly offered course is a red herring that distracts from the matter at hand: our high school students deserve multiple options for developing essential quantitative literacy skills. Ignoring opportunities to invest in teacher development does a disservice to students.

High school mathematics teachers are professionals capable of expanding their existing knowledge to meet the demand of equipping our youth in becoming quantitatively literate. As a mathematics educator and district instructional coach, I understand the impact that professional learning communities have on access to high-quality curricula and instructional approaches that develop student academic achievement. Partnerships with colleges, universities, and other education organizations lead to equipping high school educators with the knowledge, skills, and resources to teach a variety of rigorous courses effectively.

In fact, many school districts have already successfully implemented introductory data science courses. Look at the strategic collaborative partnership between practitioners at the San Mateo Union School District and faculty from Cal State Los Angeles and the University of California Los Angeles. They piloted a data science course called CourseKata, which supports teachers through ongoing study groups that introduce them to “materials, content, and pedagogy.” High school students who took the course ended it feeling confident in their coding abilities and surpassing college counterparts who were taking the same course in coding accuracy. Teachers left the experience saying they felt well supported to pilot the course.

To be sure, for many high school mathematics educators, the technology embedded in data science is new and requires learning to program in languages such as Python and R. This only emphasizes the critical need for school districts to invest in our high school mathematics educators through professional development. While the current lack of widespread expertise in data science instruction is a barrier to implementing data science courses, that fact should not prevent us from supporting the professional development needed to do so.

Many college and nonprofit leaders agree and are investing in high-quality teacher training and instructional materials to prepare educators to offer high school data science courses. The Data Science for Everyone coalition works with numerous experts around the country, including at the University of Chicago, to build community in this space and expand access to resources for data science instruction. And Stanford University’s Graduate School of Education is offering a new course on teaching high school data science. 

As the course professor, Victor R. Lee, notes in his syllabus, “We are going to see much more integration of data and data science in the K–12 curriculum. We are also beginning to see, and should continue to expect, the rise of dedicated courses, units, and modules labeled as ‘data science’ for secondary students.”