Mastering Graph Creation: A Guide for Educators

Introduction

In today’s data-driven world, teaching students how to effectively create and interpret graphs is more important than it was when we were going through school. Graphs (as well as charts, plots, maps, etc.) are not just tools for presenting data—they’re essential ways that we support critical thinking, problem-solving, and communicating complex ideas in a visual format. However, making good graphs can be challenging, even for experienced educators.

This guide is designed to help educators master three essential aspects of teaching graph creation: selection, design, and assessment. Whether you're teaching students how to visualize data from their science lab investigations or helping them analyze trends in social studies, this resource will provide you with practical strategies, tools, and insights to enhance your instruction. By the end of this guide, you’ll have a deeper understanding of how to best support your students to choose the right graph for their data, avoid common design pitfalls, and become graph-literate learners.

#1: Choosing the Right Graph Type for Your Data

Selecting the correct graph type is a critical step in data visualization. The right graph not only makes your data easier to understand but also ensures that your audience interprets it accurately. But graphs come in many forms, each suited to specific types of data and questions. With so many graph types available, how do you choose the right one?

Before diving into graph selection strategies, it’s important to understand the most common types of graphs and their purposes. If you are looking for more details about different graph types, enroll in our Master Graphing Course to dive into the 14 most commonly used graph types across K-12!

Factors to Consider When Selecting a Graph

There are four main areas that are important to consider when you are selecting what graph type to make with your data…

The Question You Are Asking / Story You Want to Tell - Each graph type serves a specific purpose so depending on what we want to do with them largely influences what graph type we will want to use. For example, are you wanting to compare:

Individual values, show trends, or explore relationships?
Sub-groups or look at the whole group?
Across time and/or space?
Within a variable or among variables?

The Type of Data You Have - There are many ways we can determine “what” type of data we have (e.g., continuous, discrete, nominal, ordinal) but the most important one we feel for all K-12 students to master is:

Categorical Data: When we collect information across individual categories (e.g., color, control/treatment). We can use graph types like bar or pie charts to compare amounts or proportions of categories.

Numerical Data: When we collect information about numbers (e.g., air temperature, heights). We can use line graphs, boxplots or scatterplots to explore amounts, trends, and relationships of numbers.

The Context & Extent of Your Data - Sometimes we have WAY more data than we need to actually explore the question we are asking. So it important to think about:

How much detail is necessary to include to clearly explore your question vs what is in the dataset. For example, if we are interested in the number of slices of pizza kids typically eat at our school we likely don’t need to include the kinds of toppings they put on those slices in the graph, even if that is in our dataset.
What the data represent about the broader phenomenon/system we are exploring. For example, if we are wanting to explore the predator and prey relationships by looking at deer and wolf populations then we likely don’t need to include the weight of the deer in the visualizations.

The Audience - Especially as students start making our graphs/data visualizations for audiences beyond our class instruction (e.g., Science Fair, School Board) who will be looking at the data visualization is really important. As it is valuable to:

Consider your audience’s familiarity with data visualization. For younger students, simpler graphs like bar graphs or pie charts may work best, while older students can handle more complex visuals like scatter plots or box plots.
Consider the audience’s interest in complexity. For example, if you have 5 minutes to talk about a School Board meeting about plastic pollution in the school cafeteria, then a simpler graph would work better, while submitting a report of your project around plastic pollution for them to read then they can handle more complex visuals.

Need help teaching your students how to choose the right graph? Download our free Graph Type Matrix for step-by-step guidance on mastering the first of these four areas…as it’s a critical first step! Built in are opportunities to talk about the other three.

Common Mistakes in Graph Selection

Even with the best intentions, it’s easy to make mistakes when selecting a graph type. Here are some common pitfalls around graph selection:

Using a bar chart to show changes over continuous time data… instead use a line chart/graph.
Using a pie chart to compare counts across categories…instead use a bar chart.
Using a bar chart to compare the range of values observed in a numerical variable…instead use a histogram.
Using multiple pie charts to compare parts of the whole across categories and two plus groups…instead use a stacked bar chart.
Using a bar chart of average values (without looking at the distribution of the data values before) to make comparisons…instead use box plots.
Using a line chart/graph to investigate the relationship between two numerical variables…instead use a scatterplot, and add a line of fit if needed.

Looking for more assistance in how to overcome these or other common missteps with graph choice? Refer to tools like our Graph Type Matrix to match your data with the appropriate graph type.

Practical Examples of Choosing the Right Graph

Let’s look at some real-world scenarios from our teaching perspective where choosing the right graph makes all the difference:

Scenario 1: Comparing Test Scores Across Subjects

Best Graph: Box Plot
Why? A box plot or bar graph allows you to compare scores across multiple subjects (i.e., categories) easily.

Scenario 2: Tracking Test Scores Across the Year

Best Graph: Line Chart/Graph
Why? A line graph shows trends over time clearly and effectively as the scores of students earlier in the year are related to/influenced by the subsequent scores (and November always has to come after October).

Scenario 3: Analyzing Correlation Between Hours of Sleep and Grades

Best Graph: Scatterplot
Why? A scatter plot reveals relationships between two numerical variables and makes it easier to identify any patterns across the full set of data for the group.

Tools for Selecting the Right Graph

There are many tools available that can help educators and students choose which graph type to use effectively:

Dataspire’s Graph Type Matrix
This free resource provides a step-by-step guide for selecting the perfect graph based on your data type and goals. Download it here. Tuva offers the Graph Choice Chart as another resource that is similar and aligns with their graphing program.
Interactive Online Visualization Tools
Platforms like DataClassroom have built in a Graph Wizard feature that scaffolds students' selection of graph type based on the data they have and what they want to ask from / do with the data. Other programs like Google Sheets and Tableau that guess based off of the data for users, and offer a menu of options with some suggestions (but without guidance).

Watch our YouTube video on We've got some data...now what graph?.

#2: Creating Effective Graphs

Once you’ve selected the right type of graph for your data, the next step is ensuring that the graph is clear, accurate, and visually appealing. A well-designed graph not only communicates information effectively for the story you want to tell, and also engages your audience and helps them draw meaningful conclusions.

Key Principles of Effective Graph Design

There are four things to keep in mind when developing effective data visualizations to communicate stories you want to tell from the data:

1. Accuracy

Represent your data truthfully without distorting proportions or scales.

Use consistent intervals on axes to avoid misleading your audience.

2. Relevance

Focus on including only the data that supports your main message.

Customize your graph to suit your audience’s needs and level of understanding.

3. Clarity

Ensure that your graph is easy to read and interpret.

Use clear titles, labeled axes, and a legend (if necessary) to provide context.

Avoid clutter by limiting the number of data points or categories displayed to only those relevant to the question you are pursuing.

4. Simplicity

Keep your design clean and straightforward.

Avoid using excessive colors or 3D effects that can distract from the data.

As a note, the first two are what are critically important when you are first exploring data to make sense of what you have. Once you know what you want to say with your data visualization and from the data is when the final two components are also important.

Want more tips? Enroll in our Master Graphing Course to learn advanced techniques for creating impactful graphs!

Step-by-Step Guide to Creating a Graph to Tell Your Story

Step 1: Define Your Purpose

Before creating a final graph to communicate the story you want to tell, ask yourself:

What question am I trying to answer/investigate?
What message do I want my graph to convey?
Who is my audience?

Clearly defining your purpose will help you choose the right graph type and design elements to help tell your story.

Step 2: Organize Your Data

Ensure that your data is clean, accurate, and well-organized. Use tools like spreadsheets (e.g., Excel or Google Sheets) to sort and format your data before creating your visualizations of it.

Step 3: Choose the Right Tool

There are many tools available for creating graphs, ranging from basic options like paper to interactive graphing software like CODAP, DataClassroom, Tuva, etc. for exploring and visualizing data.

Step 4: Design Your Graph

Select appropriate colors that enhance readability while avoiding color combinations that can be tricky to read (e.g., red-green).
Use fonts that are easy to read and large, especially for titles and labels.
Add annotations or callouts to highlight specific features in the data that you want your audience to pay attention to as they are reading/hearing your story from the data.

Step 5: Review and Revise

Before presenting or handing in your graph, review it critically:

Does it clearly communicate your intended message?
Are there any errors in the design?
Could it be simplified further to more clearly help as a tool in telling your story?

Common Pitfalls in Graph Design (and How to Avoid Them)

Even experienced data visualizers can fall into common traps when designing graphs, so it makes sense our students can fall into these traps as well. Here are some mistakes to watch out for:

1. Overcomplicating the Design

Problem: Using too many colors or unnecessary decorative elements (3D effects, shadows, etc.). This also shows up as including too many categories in a bar graph, making it hard to read.

Solution: Stick to a clean, minimalist design that prioritizes clarity over glitzy aesthetics. This also applies to how much data you are including in the visualizations; simplify your data by grouping similar categories or focusing on the most important ones.

2. Misleading Scales

Problem: Using inconsistent intervals on axes or truncating axes just to the data range (though sometimes this is ok), can distort trends visually in your graph.

Solution: Always use equal intervals and clearly label your axes.

3. Ignoring Accessibility

Problem: Choosing color schemes that are difficult for colorblind individuals to interpret (the two most common are: red-green and yellow-blue).

Solution: Use high-contrast colors and patterns to ensure accessibility for all viewers. Our go-to resource for choosing colors is ColorBrewer 2.0.

4. Neglecting Context

Problem: Failing to include titles, subtitles, labels, or legends.

Solution: Always provide sufficient context so viewers can understand what they’re looking at without additional explanation. For ideas about what should be included check out our “Sample graph & components highlighted” resource.

Tools for Teaching Students How to Create Graphs

Teaching students how to create effective graphs is just as important as teaching them how to interpret them. Here are some tools and strategies you can use in the classroom:

Interactive Platforms: Tools like CODAP, DataClassroom, Tuva, etc. allow students to experiment with different graph types in real time as they are exploring and making sense of the data.

Templates and Graphic Organizers: Provide students with pre-made templates or graphic organizers that guide them through the process of creating a graph step by step... though be careful with this one to not overuse these instructions. While it is important when students are first learning how to create graphs to provide these extensive scaffolds, if we do not pull back on those scaffolds then students will learn to rely on them as a crutch to passively execute the steps rather than integrate that knowledge into their working memory to apply independently.

Collaborative Activities: Use leverage learning experiences in which students work together to input data and create graphs collaboratively. So often we have each student make their own visualization, but learning is a social activity so changing things up and having students collaboratively make their graphs every once in a while can increase all students’ skills as they learn from one another.

Download our free Graph Type Matrix for classroom-ready resources that simplify graph creation!

#3: Assessing Students’ Graphing

Once your students have made their graph selection and created the graph, we then need to think about how to assess their progress in building these skills. So we wanted to also provide some practical strategies for that as well as ways to overcome common challenges folks face when creating visualizations.

Assessing students’ ability to interpret and create graphs is an important part of teaching students how to create graphs. Here are some strategies for evaluating student progress:

Rubrics: Use a detailed rubric that evaluates key components of making effective graphs, such as: graph choice, graph completeness, construction/set-up, aesthetics/neatness.
Peer Review: Have students review each other’s graphs and provide constructive feedback based on a set of criteria. This could be just peer-assessment or you could collect their feedback to use to assess your students. And a super powerful next step for students’ graphing skills development is to have students use that feedback to revise their graphs.

Need help assessing your students’ graphing skills? Check out our Component of a Rubric.

Overcoming Common Challenges

While teaching students how to graph is rewarding, it can also be challenging and frustrating. Here are some common obstacles we faced and hear that other educators face—and how to overcome them:

Difficulty Choosing the Right Graph Type…aka “another bar chart?!”

Solution: Provide students with resources like our Graph Type Matrix to help them match their data with the appropriate graph type.

Limited Access to Technology

Solution: Use low-tech alternatives like paper-and-pencil activities or printable templates for creating graphs. Looking for some ideas? Check out our blog posts, “Another perspective on Halloween candy charts,” and “Interactive-Explanatory data experiences for MS science” with low-tech options.

Engaging Students Who Find Math Intimidating

Solution: Incorporate real-world examples that align with students’ interests (e.g., sports statistics, social media trends) to make graphing more relatable.

That was a lot! But there are a few other aspects that can influence how our students make graphs, charts, plots, maps, etc. So we have one final section to share.

Additional Topics in Data Visualization

As students become more comfortable with basic graphing concepts, it’s important to consider other aspects that impact or influence the creation of data visualizations. These concepts not only deepen understanding but also prepare students for real-world applications where data visualization plays a critical role in decision-making.

1. Data Storytelling

Data storytelling is the art of using data visualizations as tools in your toolkit to communicate a narrative or story with the data. Instead of simply presenting raw numbers, we can craft compelling stories that highlight key insights from the data.

Key Elements of Data Storytelling:

Context: Provide background information to help the audience understand the significance of the data.
Focus: Highlight the most important trends or findings.
Visualization: Use graphs and charts that support the narrative.

Classroom Activity Idea:
Assign students a dataset (e.g., climate change trends, population growth) and ask them to create a presentation that tells a story using graphs. Encourage them to use multiple graph types to support their narrative…and even push them to be creative with their storytelling.

Looking for more ideas of what to do with storytelling with data?

Check out Dear Data and their other incredible book Observe, Collect, Draw for ideas (Here is a review before you dive in).
Explore the supports and possibilities of the DataJam approach as creative ways for students to tell their stories with their data. (example: https://sites.google.com/view/nes-lter-schoolyard-datajam/home)

2. Using Real-World Datasets

Having students work with real-world datasets helps them see the practical applications of graphing skills. These datasets often contain messy or incomplete data, teaching students how to clean and organize information before visualizing it. But wow it can be hard to find them or get them ready for students to work with. Looking for ideas? Check out our list of various dataset sites: Authentic & Relevant Data Repositories

Teaching Tip:
Provide students with a dataset and support them through the process of cleaning the data, selecting the appropriate graph type, and presenting their findings. This activity mimics real-world scenarios where data are rarely perfect.

3. Ethical Considerations in Data Visualization

As students advance in their graphing skills, it’s essential to think about ethical considerations in data visualization. Misleading graphs can distort facts and misinform audiences, so understanding how to present data responsibly is crucial.

Common Ethical Issues:

Misleading Scales: Manipulating axes to exaggerate or downplay trends.

Selective Data Presentation: Omitting key data points that contradict the intended message.

Overcomplicating Visuals: Using overly complex designs that confuse rather than clarify.

How to Teach Ethics in Graphing:

Show examples of misleading graphs and discuss why they are problematic.

Challenge students to identify ethical issues in sample visualizations.

Emphasize the importance of transparency and accuracy when presenting data.

There are many ways that data visualizations can be constructed in unethical ways. Looking for more ideas of what to help your students look out for? Check out our review of Alberto Cairo’s excellent How Charts Lie book at 5 Tips to Be Aware of "How Charts Lie" [Book Review].

Conclusion: Empowering Educators and Students with Graph Mastery

Graphs are more than just visual representations of data—they are powerful tools for communication, analysis, and decision-making. By mastering graph creation, we can equip our students with critical skills that will serve them in academics, careers, and beyond.

This guide has explored:

Strategies for choosing the right graph based on data type, audience, and context.
Best practices for designing effective graphs that are clear, accurate, and engaging.
Methods for assessing students’ graph skills and common challenges in graphing of all ages.
A few additional topics like interactive graphs, data storytelling, real-world datasets, and ethical considerations that relate to making graphs.

By implementing these strategies in your classroom, you’ll not only enhance your students’ understanding of data visualization but also foster a deeper appreciation for the power of data in solving real-world problems.

Ready to take your graphing skills to the next level? Explore our Graph Type Matrix Resources or enroll in our Master Graphing Course today!

Next Steps for Educators

To make the most of this guide as you are incorporating hands-on activities and real-world datasets into your lessons to engage students in meaningful ways with graphing going forward:

Download our free Graph Type Matrix to help your students choose the right graph each time they are working with and creating visualizations. And if you are looking for more strategies and support around a range of aspects of graphing, enroll in our asynchronous Master Graphing Course (opening again March 17th).
Use interactive tools like CODAP, DataClassroom, and Tuva to introduce a wider range of graphing types and concepts as students explore the data. Looking for assistance in deciding which program to use? Check out our benefits & limitation blog post.
Emphasize ethical considerations to ensure students understand the importance of accurate and responsible data visualization.

By taking these steps, you’ll create a classroom environment where students feel confident creating and presenting data visualizations—and where they can see the real-world impact of their growing graphing skills.