Graph & Analyze Data

Visualize Data         

Graphs and maps are tools to visualize patterns in data that are not easy to see in a table. We need to learn how to read the data values, whether as points, shapes, or symbols, across a range of visualization types. Creating graphs and maps (by hand and digitally) is also important and involves deciding what kind of graph or map to make in a given situation, drawing the axis/axes, scaling the axis/axes, and plotting values accurately.

Here are some resources to help your students gain these skills: 

• Confusion to Clarity: 3 Steps to Master Graphing mini course ($): With this efficiently designed asynchronous course, learn the what, why, when and how of making different data visualizations.
• Graph Type Matrix activities: Discover the Graph Type Matrix Resource and the accompanying "Creating Graph Type Matrix" activity, designed to revolutionize your approach to teaching graph choice.
• Graphs: Components of a Rubric: Get examples of what to include in your rubrics to grade students' graphs and provide more actionable feedback so that they can make better graphs the next time.
• Making effective data visualizations: The intention of this resource is to provide samples of what different graph types should look like and what to look for when students are creating them across grade levels.
• Dataspire: Data Visualization Types, Grade Levels, & Technology Options: With so many options out there to use to make data visualizations with students, what works for what, for who, and how? This 3-tab spreadsheet might help!
• Making Effective Data Visualizations: ADVizE mini-lecture: We explore how making data visualizations effective is more than just the mechanics of making the graphs, and introduction to visual and perception science, as well as some brief tips from data visualization designers.
• Teaching Graphing & Graphs: ADVizE mini-lecture: We explore differences between novices and experts looking at graphs, the question of graphing by hand or with technology, and how to have our students set up their graphs.
• Data Bite: Creating Data Visualizations in Google Sheets: Here we explore how to make 11 common graph types in Google Sheets in terms of how to organize the data (and what data we need), and what aspects of the Chart Editor to pay particular attention to for each.
• Data Literacy 101: How do we set up graphs in science? (Sep 2018): Rules of thumb to set up composition, distribution, and comparison graphs.
• Data Literacy 101: Which is the best graph to use? (Jan 2019): About the ideas behind creation of the Dataspire Graph Type Matrix and activity.
• Data Literacy 101: How can we use and interact with graphs better? (Jul/Aug 2021): About how we can interact with graphs differently for different purposes, also known as "mucking about in the data".
• Data Literacy 101: Interdisciplinary Ideas: What, Graphing Can Be Fun and Engaging This Year?! (Jul/Aug 2023): Includes low-floor, high-ceiling strategies to create opportunities to integrate both approaches into our science curriculum without taking a lot of time away from our content.
• Data Literacy 101: How can we help students explore data in their sensemaking? (Jan/Feb 2025): About steps to scaffold ways to help students first explore data before explaining what they see in the data.

Process Data to Reveal Patterns                       

Statistics are used to summarize properties of data. For example, 'average' represents what is typical for a group, but it does not show how much the group varies. It is important to understand what statistics show and what they do not show about the data. Sometimes it is strategic to transform attributes or derive a new attribute to reveal patterns related to the question at hand.

Here are some resources to help your students gain these skills: 

• Dataspire: Data Visualization Types, Grade Levels, & Technology Options: With so many options out there to use to make data visualizations with students, what works for what, for who, and how? This 3-tab spreadsheet might help!
• Dataspire: Data Moves Tabletoppers for Students ($): A digital resource to support students as they practice and learn common data moves, presented in 3 ways!.
• Making Effective Data Visualizations:  ADVizE mini-lecture: We explore how making data visualizations effective is more than just the mechanics of making the graphs, and introduction to visual and perception science, as well as some brief tips from data visualization designers.

Describe & Analyze Patterns          

Populations and phenomena vary naturally. Thus, data that are collected about them are also expected to vary. How (and how much) a group varies reveals important information about the nature of the group or phenomenon. Patterns may be strong or weak, there may be smaller patterns within a larger pattern, and quantitatively modeling patterns communicates the scale. Visual components can emphasize visually aspects of the pattern.

Here are some resources to help your students gain these skills:  

• Data Trailblazers: Advancing Pattern Recognition (K-12 and grade banded) ($): An overview of common ways we talk about patterns in different contexts.
  Grade-appropriate talk, tasks, and skills tailored to help students recognize patterns in data with curiosity and confidence.
• Think Statistically with Data: A Dataspire resource page with links to further support statistical thinking and analyses.
• Charty Party: The Game of Absurdly Funny Charts: One of our favorite PL warm-ups and a fantastic and fun way to get students thinking about patterns and their possible meanings. From Very Special Games.
• Data Literacy 101: How Can We Help Students See Patterns in Data? (Jul/Aug 2022): About how we can leverage humans' pattern-seeking tendency to our advantage to help students learn what to look for in data before they are asked to make meaning of the data - 5 common data patterns.
• Data Literacy 101: Why Should We All Embrace Statistical Thinking? (Jan/Feb 2023): About ways to get students thinking about data overall from a nuanced perspective to help them develop their statistical thinking.
• Data Literacy 101: Discover Student Thinking While Analyzing Data... And Having Fun! (Jan/Feb 2024): About how we can elicit students’ ideas about the data skills separate from the content using two simple strategies.
• Data Bite: Describe Visual Patterns: We explore five common data patterns to help students learn the vocabulary of and physical make up of patterns as well as the many things that are going on as we try to make meaning of visual information in our brains.

 General Resources

There are also a range of resources related to Graphing & Analyzing Data in our list of more General Resources:

• Building Blocks for Data Literacy - Reference and discussion-starter for all educators as we all explore how to engage K-12 students with data. 
• Data Literacy 101 Articles - Interdisciplinary Ideas column article for NSTA's Science Scope focused on various data strategies.
• Data Bites Series on YouTube - Weekly short videos of classroom-ready resources or strategies to try.
• Book Suggestions - Recommendations of various data, data visualization, and education books that we like and wanted to share.
• Data Across Disciplines - Data is NOT just for math or computer science class. We also need to use it in science and social studies...and can use it elsewhere.
• Others' Resources - There are so many great teams working on building lesson plans, interactive data tools, etc.

Also remember to check out our Blog for more helpful connections to the many ways you can support your students building their data literacy skills.