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Elementary Data Literacy
It is impossible to escape the deluge of data that surrounds us whether at home, at school, at work, at the doctor’s office, on television and online. The revisions within the 2023 New Jersey Student Learning Standards&mdashMathematics reflect the changing ways in which data has increasingly become the lens with which students discuss and respond to data-based questions and make sense of the world. Our students need to be able to both read data and write with data; that is, they need to be able to understand and interpret data that is presented to them and use data effectively to make arguments of their own and to refute the arguments of others. It is important for several reasons that they begin their data literacy journey in elementary school.
- Data are inherently interesting to students because they are about the world they live in and can enable them to learn about their world.
- Working with student-centered data — that is, data that students collect by themselves and/or about themselves &mdash can help students make personal connections to mathematics.
- Working with data supports many other aspects of early mathematics learning, including basic computation, fractional reasoning, graphing and geometric concepts like area.
- Data appear in many school subjects. Learning to analyze data will help students across the curriculum.
In grades K–5, students work with data builds foundations for the later study of data science, probability and statistics. The K–5 data standards run along two paths. One path involves categorical data (i.e., non-numerical data) and focuses on bar graphs to represent and analyze such data. Students are introduced to categorical data through sorting objects into categories in kindergarten. The other path deals with numerical data, including measurement data — data that comes from taking measurements. Ways to generate measurement data might include measuring liquid volumes with graduated cylinders or measuring room temperatures with a thermometer. Numerical data also includes data that is gathered by counting (e.g., counting the number of spots on black and red ladybugs), questioning (e.g., surveying classmates by asking about their favorite summer activity), or observing (e.g., observing which students go to the gym for indoor recess versus to the playground for outdoor recess). In each case, the NJSLS-M call for students to represent numerical data with a line plot and/or other visualizations that display frequency.
The new data literacy standards, organized by grade and cluster are as follows.
Grade 2
Cluster A. Understand concepts of data
- Understand that people collect data to answer questions. Understand that data can vary.
- Identify what could count as data (e.g., visuals, sounds, numbers).
Grade 3
Cluster A. Understand data-based questions and data collection
- Develop data-based questions and decide what data will answer the question. (e.g. “What size shoe does a 3rd grader wear?”, “How many books does a 3rd grader read?”)
- Collect student-centered data (e.g. collect data on students’ favorite ice cream flavor) or use existing data to answer data-based questions.
Grade 4
Cluster A. Organize data and understand data visualizations
- Create data-based questions, generate ideas based on the questions, and then refine the questions.
- Develop strategies to collect various types of data and organize data digitally.
- Understand that subsets of data can be selected and analyzed for a particular purpose.
- Analyze visualizations of a single data set, share explanations and draw conclusions that the data supports.
Grade 5
Cluster A. Understand and analyze data visualizations
- Understand how different visualizations can highlight different aspects of data. Ask questions and interpret data visualizations to describe and analyze patterns.
- Develop strategies to collect, organize and represent data of various types and from various sources. Communicate results digitally through a data visual (e.g. chart, storyboard, video presentation).
- Collect and clean data to be analyzable (e.g., make sure each entry is formatted correctly, deal with missing or incomplete data).
- Using appropriate visualizations (i.e. double line plot, double bar graph), analyze data across samples.
References
Bargagliotti, A., Franklin, C., Arnold, P., Gould, R., Johnson, S., Perez, L., & Spangler, D. (2020). Pre-K–12 Guidelines for Assessment and Instruction in Statistics Education II (GAISE II): A Framework for Statistics and Data Science Education. American Statistical Association.