Data Science

A recent 2022 National Academies convening on the “Foundation of Data Science for K-12 Students” examined existing K-12 learning frameworks, and found many partial building blocks throughout existing K-12 subjects, including computer science. However, these existing frameworks have yet to be unified or modernized, and are often given little real time in the classroom due to competing content expectations.

 

The 2020 GAISE II (Guidelines in Assessment & Instruction for Statistics Education) from the American Statistical Association provides a critical building block for K-12 data science experiences, forwarding clear grade-span expectations for statistics education and data literacy. However, these guidelines have yet to be updated for modern technology changes, nor handle concepts related to AI literacy, data wrangling, or other modern techniques connected to data-use in industry or other applied contexts. They have also yet to be meaningfully adopted in math expectations. 

 

The 2016 K-12 Computer Science Framework built a foundational thread on “Data & Analysis,” which provides a baseline for CS educators to consider when building foundational computing learning experiences. The framework includes four sub-concepts outlining Data and Analysis from a Computer Science lens: 1) Data Collection 2) Data Storage 3) Visualization and Transformation and 4) Inference and Models. The standards in the K-12 Computer Science Framework emphasize the management, treatment, and computational efficiency for analyzing data, and equip students to think about accuracy of computer models as a function of the quality and quantity of data – especially in the context of prediction and automation of data analysis (i.e. machine-learning). However, statistical inference, treatment of uncertainty, and other mathematics foundations are less emphasized in content expectations for CS. The Framework explicitly references Data Science: “Data science is one example where computer science serves many fields. Computer science and science use data to make inferences, theories, or predictions based upon the data collected from users or simulations. In early grades, students learn about the use of data to make simple predictions. As they progress, students learn how models and simulations can be used to examine theories and understand systems and how predictions and inferences are affected by more complex and larger data sets.”

 

The 2013 College, Career & Civic Life (C3) Framework for Social Studies Standards mentions “data” over 53 times throughout the standards. “Data” appears in a diverse set of contexts, including as one component of historical evidence (along with primary sources, documents, text, images, and other artifacts), as domain-relevant information for geography or economics, and in the context of geospatial technology and geographic information systems (GIS) (National Council for Social Studies, 2013). In formal standards, data is expected to be integrated into specific social studies courses, including Economics, Geography, Psychology, and Sociology. 

 

While written over a decade ago, the 2011 Next Generation Science Standards (NGSS) recommends the inclusion of data science foundations in explicit terms: students are expected to “use digital tools (e.g., computers) to analyze very large data sets for patterns and trends”) as early as Grades 6-8 in Practice 5. Content expectations for data analysis basics appear in cross-cutting Practices, including Practice 4 (“Analyzing and interpreting data”) and Practice 5 (“Using Mathematics and Computational Thinking”) (ibid), both in Appendix F – Science and Engineering Practices. However, the degree to which these expectations have been implemented or emphasized in state assessments needs further exploration.