Data Literacy for Students: Reading, Visualizing, and Questioning Data

Description

Data is everywhere, from classroom assessments and reading logs to social media, advertisements, and news stories. Looking for ways to help students move beyond simply reading numbers and begin thinking critically about the information they encounter every day? In this course, Lauren Boucher explores practical strategies for building student data literacy through observation, analysis, visualization, and inquiry. Participants will learn how to facilitate age-appropriate data discussions, help students identify patterns and trends, evaluate claims and sources, recognize bias and missing information, and communicate evidence-based conclusions across content areas.By the end of this course, educators will walk away with classroom-ready routines and strategies that empower students to become thoughtful consumers and communicators of data. This course is ideal for classroom teachers, instructional coaches, and school leaders seeking to strengthen critical thinking, inquiry, and data analysis skills across grade levels while helping students evaluate evidence, recognize bias, and communicate informed conclusions.This is course one of four in the series titled "Data in Their Hands: Building Student Data Literacy, Agency, and Action."

Objectives

1. Identify key components of data literacy, including data sources, visualizations, and evidence.Explain how data can be interpreted, represented, and communicated across disciplines.Use instructional routines that help students analyze, visualize, and discuss data.Examine data sources and visual representations to identify patterns, trends, bias, and missing information.Assess claims and conclusions using available evidence and context.Design learning experiences that help students collect, organize, and communicate data effectively.

Key Take Aways

• Facilitate age-appropriate data discussions that help students notice patterns, interpret evidence, and generate questions.Select strategies for teaching students to organize, represent, and communicate data across content areas.Guide students in evaluating data sources, visualizations, claims, bias, and missing information before drawing conclusions.Build student confidence in analyzing and communicating evidence-based findings.