Data Science: A Pathway for Integration Across K-12 Disciplines

• In Event: Data Science Education: Re-Envisioning for 21st Century Teaching, Learning, and Research

Abstract

Many disciplines can host data science education in K-12. The statistical aspects of data science often lead people to see it as part of math, but other disciplines offer complementary opportunities. In elementary grades, students can learn about table structure, variables, and visualizations as part of observational experiments (science or otherwise). In upper elementary and middle grades, datasets bring concrete evidence to otherwise abstract topics and counterbalance first-person narratives in social studies. Furthermore, social studies is a natural context which to raise critical questions of how people or events are represented (or not!) through data. Once students start to study statistics in later middle school or high school, as well as non-linear functions in algebra 2, data science can provide a vehicle for applying those topics to real-world problems. These examples highlight the benefits of seeing Data Science as a topic that can leverage multiple existing disciplines in order to meet learning objectives in authentic ways. Data Science and other disciplines could share a rich, symbiotic relationship. Below are a set of ideas I will present as guiding principles gleaned from my practitioner experiences in the field.

Theme I: Viewing data science as primarily a math-based discipline is limiting. Robust data science education combines four ingredients: domains of study, statistics & mathematics, computing, and civic responsibility. While specific curricula may emphasize some ingredients more than others, students ultimately need all four in order to apply data science responsibly. In K-12, where integration is often the easiest way to include data science, the ingredients-based view provides a framework for weaving it cohesively across multiple courses.

Theme II: Data science and social studies need one another. When Bootstrap and KIPP-NYC started to collaborate on weaving data science into middle grades social studies, many teachers noted “I’m not a math person”. Over time, we collectively discovered that data science—with its (numeric and categorical) observations summarized in charts—helped teachers bring concrete understanding to otherwise abstract social concepts. On the flip side, social studies gives data science a natural home in the middle grades and critical opportunities to learn the affordances and pitfalls of representing people and societies as data.

Theme III: Data science should enhance, not replace, upper-level mathematics. Functions are the foundation of both data science and AI. To progress beyond basic data skills, one needs to understand different kinds of functions (logarithmic, linear, exponential, etc) and how functions are “learned” (regressions, predictors, etc). This is the stuff of Algebra 2, Statistics, and Computer Science. Teaching data science within Algebra 2, for example, brings real-world, concrete applications to a class that many students fail to appreciate (or, sadly, pass). If earlier grades develop basic skills and perspective on data, upper-level mathematics is free to fly with teaching the mechanics of data science to the benefit of both disciplines.

I believe these themes help nuance how we see data science education, toward an end that supports transdisciplinary and inclusive learning.

Author

• Kathi Fisler, Brown University