5 Key Indicators of Student Progress in Making Data Visualizations

As educators, assessing student graphing skills is crucial for developing data literacy overall as well as critical and analytical thinking with data. Here we explore five key indicators that demonstrate student progress in creating effective data visualizations (e.g., charts, plots, maps, graphs). By understanding these indicators, we can better evaluate and support our students' growth in this essential skill.

1. Accuracy of Data Representation

The foundation of any good data visualization is accurately representing the underlying data. Therefore, a key first step for our students in making data visualizations is how well they translate or represent raw data into visual form AND that the data they are using are relevant to their question (aka not using everything just because they have it). While this may seem trivial for our older learners, this is a necessary first thing to look for from all of our students (and when looking at someone else’s data visualization…but that is a different topic).

Students who excel in this area demonstrate a strong understanding of how to translate numerical and categorical  information into graphical form. This requires a 1:1 understanding of the representational nature of the dots, lines, bars, colors, etc. in a data visualization to the observed data values in their dataset. This is critical for students to learn when broadly making sense of data, and especially when making their own visualizations. Additionally, students who have mastered the ability to accurately represent data can avoid common pitfalls that many adults make in data visualizations, such as distorting data through improper scaling or misrepresenting relationships between variables.

Indicator #1: What to Look For in Student Work:

• Correct plotting of data points from the recorded data / data table into the graphing space.
• Appropriate use of scale(s) on the axis (or axes if using multiple) that relates to the range of data values recorded.

Ways that we can approach this from a progression standpoint…

1. Beginning: Visuals misrepresent the data, are missing labels, include all available data without filtering or purpose, or have incorrect scales.
2. Progressing: Data values are mostly accurate and there is some attention to labels and scaling, but still inconsistencies, minor inaccuracies, or selected subsets of data inconsistently align with their goal.
3. Proficient: Visuals present the data truthfully and have accurate axes, appropriate scales, and clear labels as well as intentionally choose appropriate data to best support the data question.

Want to improve your students' data accuracy skills? Check out our Graph Components of a Rubric for targeted ways to assess this part of students’ graphs!

2. Selection of Appropriate Graph Type

Choosing the right type of data visualization for a given dataset is a critical skill in data visualization, storytelling with data, analyzing and interpreting data, and data literacy more broadly. Therefore, the next step in determining students’ ability to make visualizations means we need to assess their ability to match characteristics of the data they are working with and the question they are asking of the data, with the most suitable visual representation that will help them explore those data for that purpose.

Progress in this area is evident when students consistently select the data visualization type that enhances their data comprehension (or for those they are communicating their data results to). For example, using bar graphs for categorical comparisons but instead selecting a line graph when they are making a comparison over time from a time series dataset rather than making the bar graph.

Indicator #2: What to Look For in Student Work:

• Alignment between data type and graph choice (e.g., if the data are all numerical they are not using a data visualization type that requires a categorical variable).
• Effectiveness of the chosen graph in conveying the intended message or enabling them to explore their data question (e.g., if they are wanting to ask questions about the relative differences among subgroups of a whole population then they are using a type of data visualization that enables them to explore the composition).
• Avoidance of misleading or overly complex visualizations just to use them.

 Ways that we can approach this from a progression standpoint…

1. Beginning: Students pick graph types based on habit or aesthetics (e.g., bar graph for everything).
2. Progressing: Students experiment with different data visualization types but sometimes mismatch data and graph type.
3. Proficient: Students accurately match characteristics of their data and data questions (e.g., categorical, numerical, comparisons, distributions) to effective visualization types.

Looking for ways to help your students better see the connection between the types of graphs they are using and the questions they are asking? Check out our Graph Type Matrix activities for classroom-ready resources to teach Graph Choice!

Connecting the graph types we make in Elementary School classes to the common kinds of questions we ask of data.

3. Visual Design and Clarity

While accuracy is paramount, the visual appeal and clarity of a graph significantly impacts its effectiveness, especially when we are using the visualization to tell a story with the data to an audience new to the data. So this indicator focuses on the aesthetic and communicative aspects of student-created visualizations. This aspect of working with and making data visualizations is extremely important…but only if you have the first two components aligned with your dataset and question you are asking of the data. 

Students showing progress in this area create graphs that are not only accurate but also visually engaging and easy to interpret. They understand the importance of design in enhancing data communication. There is a WIDE spectrum of what this could look like and what we would expect from our students along the novice-expert continuum. So don’t worry too much about the details in any one learning experience (unless you are teaching data visualization, graph design, or visual storytelling ;)). Instead just make sure along the way students are building an understanding that aesthetics are an important part of making data visualizations. 

Indicator #3: What to Look For in Student Work:

• Appropriate use of color that is meaningful to make sense of the data (aka not color for color sake, but color for a purpose) and contrast to help draw attention to different parts of the data.
• Logical layout and organization of visual elements.

 Ways that we can approach this from a progression standpoint…

1. Beginning: Graphs are confusing to read or require major explanation to make sense of what is being visually represented.
2. Progressing: Graphs are somewhat readable but may clutter key information or lack information about the data.
3. Proficient: Graphs are easy to make sense of by the intended audience and communicate the main message clearly (often supported by a thoughtful labeling and annotations).

 Enhance your students' visual design skills with our Digital Resources for data visualization!

4. Ability to Justify Choices

Creating a graph is only part of the process; students being able to communicate and justify why they made the choices they did is equally important. Students are in the driver seat when they are making data visualizations, not you or Google Sheets or their graphing platform. But in order for us to understand how well students are driving the car, we need to ask them to explain their reasoning and thinking (and stop telling them every step every time…but again that is a different topic 🙂). This indicator therefore is all about assessing a student's ability to explain their choices in how they are presenting the information in their data visualizations.

Progress in this area is demonstrated when students can articulate why they chose a particular graph type in relation to their data's characteristics (e.g., "I used a histogram because I needed to show the distribution of continuous data") AND connect their visualization choices to what they were trying to do with the data (e.g., "I chose a line graph because I wanted to show how temperature changed over time"). We need to get them to make their thinking visible to us, because just looking at the final product (the data visualization) is not the whole story of what went into it.

Indicator #4: What to Look For in Student Work:

• Student captions, explanations, or reflection pieces attached to graphs indicate links between the data, the purpose, and the audience — not just "what" graph they picked, but "why" and "how well it worked”.
• Ability to use accurate vocabulary (e.g., "categorical data," "numerical data," "relationship," "distribution," "variation") when justifying their choices, showing they understand both characteristics of their data and visualization strategies.
• Recognition of limitations and strengths of their own visualization choices for the data they have the the question they were asking of the data.

 Ways that we can approach this from a progression standpoint…

1. Beginning: Students cannot explain why they made the choices they did to create their data visualization.
2. Progressing: Students give basic or surface-level reasoning (e.g., “I just thought a pie chart looked good”).
3. Proficient: Students justify their visualization choices based on the type of data they have, the data question they were exploring in the data, and the story they wanted to tell to a particular audience with their data visualization (e.g., “I chose a scatterplot to show the relationship between these two variables.”).

For more on how to support students in making their thinking visible check out:

• Our curated list of Data + Instruction Strategies to leverage at various times when students are working with data,
• Making Thinking Visible: How to Promote Engagement, Understanding, and Independence for All Learners, by Ron Ritchhart, Mark Church, and Karin Morrison

5. Technological Proficiency

Finally, in today's digital age, the ability to use technology to make data visualizations is increasingly important as a workforce readiness skill and critically necessary as our students advance in the kinds of datasets they are working with (i.e., more complex and larger datasets require technology to visualize because it is not worth the class time to make visualizations from them by hand). Therefore, an indicator on students' progression in their making data visualizations needs to include technology…but it is the final component to evaluate a student's skill in utilizing digital tools to create and manipulate graphs.

Students progressing in this area demonstrate comfort with various technological tools and can leverage these tools to create more sophisticated and interactive visualizations. Wondering when students should be using technology, check out the “How are/should we make the graph?” blog post.

Indicator #5: What to Look For in Student Work:

• Familiarity with graphing software/platform/tool being used.
• Ability to input and manipulate data digitally on their own.

Ways that we can approach this from a progression standpoint…

1. Beginning: Student creates basic graphs (e.g., default graphs) but relies heavily on templates without modifying them and support (e.g., teacher needs to guide through each step)..
2. Progressing: Student is able to create functional graphs and begins to modify elements (e.g., titles, axis labels, scales) to better match their data characteristics or the needs of the data question they are exploring.
3. Proficient: Student approaches technology as a tool for exploration and refinement, seeking to leverage features like filters, color coding, etc. where appropriate and can troubleshoot basic issues.

Conclusion

By focusing on these five key indicators - accuracy of data representation, selection of appropriate graph type, visual design and clarity, ability to justify choices, and technological proficiency - we can effectively assess and support our students’ progress in making data visualizations. Remember, the goal is not just to create visually appealing graphs, but to develop students' overall data literacy skills as well as critical and analytical thinking with data.

As you implement these indicators in your classroom, you'll be better equipped to guide your students towards mastery in data visualization, preparing them for success in our increasingly data-driven world.

For more in-depth guidance on assessing student graphing skills, explore our Graph Components of a Rubric.