Artificial Intelligence, Machine Learning, Neural Nets, Blockchain, ChatGPT.
What do all these new tools and technologies have in common? They run on the same fuel: data., And a lot.
For example, Netflix’s machine learning algorithms leverage its wealth of user data to not only recommend movies, but also decide which new movies to produce. Facial recognition software deploys neural networks to leverage pixel data from millions of images. A blockchain is essentially a large database distributed among many users. Generative AI algorithms like the one used to create ChatGPT train on large language datasets.
Acquiring the data that powers these technologies quickly raises challenges around bias, accuracy, privacy, and intellectual property rights. Since at least 2006, technology leaders and mathematicians have argued that data is the new oil. Just as oil is a critical resource for physical products from fabrics to shampoo, data is a critical resource for our digital lives, and a growing share of our off-screen lives.
In K-12 schools, students face an onslaught of new technologies — new developments are happening every day — and yet we wonder if these tools are changing our daily lives. As such, it still teaches many of the school’s major subjects.
Since 2011, National Assessment of Educational Progress (NAEP) national math test scores have declined 17 points in grade 8 and 10 points in grade 4 in data analysis, statistics, and probability.
Even more worrying is that our collective data literacy has actually declined over the past decade. , statistics and probability. The impact of the pandemic was only one factor, with dropoffs outpacing declines in other content areas.
Academic performance is also highly disproportionate by race and income, with black students lagging white students by more than 30 percentage points on the basics of data analysis. For reference, some researchers believe that just a 10-point difference is equivalent to her one school year’s worth of learning.
Related: ed How technology exacerbates racial inequality
There are many reasons for these challenges. For example, outdated state standards and test combinations are leading teachers to push data-related content to the bottom of lesson plan lists.
Predictably, this lack of prioritization is reflected in the emphasis on self-reported content from national educators. This shows that lesson plans focused on data analysis and statistics consistently score the shortest straws in mathematics and other school subjects. It’s because of the system that limits our time so much and the systematic choices we’ve made so far.
As a result, student performance is moving in the opposite direction of modern technology. We urgently need to reverse this trend.
Many schools and states across the country are experimenting with the best ways to create and integrate data science programs for K-12 students. Year-round math courses focused on data science are being piloted in Ohio, Virginia, and Utah. Arkansas and Nebraska have added data science career education and technical education. Data Science electives extend the foundations of computer science in Georgia. Data-embedded lesson plans for each school subject and grade are popping up in classrooms from coast to midtown.
Students practice these fundamental life skills over the long term through all careers, all life situations, and all forms of civic engagement.
All of these efforts seek to bring data analysis and computational skills into the core school curriculum, with an emphasis on mathematics, science and social studies. Importantly, these are complementary, but distinct from the K-12 computer science community’s approach, which has historically focused on building independent school subjects. Many of these new programs add datasets and technology as a way to increase understanding, reinforcing what teachers already know and can express about their subject.
Despite these efforts, few programs remain in data science at the K-12 level. In a recent analysis of state programs, he found that only nine states received an “A” or “B” rating for teaching data science. The majority of states received a ‘D’ or ‘F’.
Our country must do better. Our primary goal at K-12 is to create a strong foundation of data literacy for all students before they graduate from high school. Students should have the ability to interpret, manipulate, analyze, and communicate data effectively. Students can develop these fundamental life skills over the long term, in all careers, in all life situations, and through all forms of civic engagement.
The goal is not to create an army of professional data scientists fresh out of high school.. Rather, it is intended to provide students with the necessary exposure to data fundamentals and to inspire them to pursue a two-year, four-year, or graduate degree in these areas if they so choose. The coursework is challenging but should be accessible — ‘low floors, high ceilings’. Her 51% of students who do not have a college degree in the near future need to explore low-cost digital training opportunities to learn the fundamentals, learn technical skills, and get rewarding jobs.
Importantly, students actually report enjoying their data science courses. A recent summit at the National Academy of Sciences cataloged an increasingly diverse range of curricular approaches in this field, demonstrating a consistent theme of tremendous student engagement.
One math teacher, who has been teaching for over 20 years, said that until he taught data science, no student ever asked for an internship related to his course.
Students stop asking, “Why should I learn this?” Instead, ask, “What’s next?” Some teachers even report that their students are progressing through material faster than expected.
We must act quickly to make these opportunities available to all students and to support educators with the right resources to successfully teach data literacy and science. Our students are counting on us to prepare for a future that is already here.
Zarek Drosda Director of Data Science 4 Everyone, a national initiative based at the University of Chicago.
This article on teaching data science hechinger report, A non-profit independent news organization focused on inequality and innovation in education.Apply Hechinger newsletter.
