Interpret & Use Data

Interpret Data to Learn Something       

What a pattern means has to do with the scale of the pattern, how the pattern relates to the question, and the relationship of one pattern to other patterns in the data. Interpreting patterns with a statistical mindset engages questions, conjectures, claims, predictions, and models. Justifying an interpretation means explaining how patterns and other features of a graph or map support the interpretation, and how the interpretation relates to the question or context of the data.

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

• Dataspire: Developing CER Capability Framework: We have developed a framework to think about key components (Content, Visuals, and Data) that users need to integrate as they build their explanatory CER.
• Dataspire: CER for Argument or Explanation: As we looked to build more support for students practicing their CER writing, we teased apart the key differences when explaining or presenting an argument based on data as evidence.
• Data Literacy 101: What Can We Actually Claim from Our Data (Feb 2020): How knowing what you can and cannot say from your data is core to students’ understanding of how to “distinguish observations from inferences, arguments from explanations, and claims from evidence” (NGSS 2013).
• Data Literacy 101: How can we help get data into students' science explanations? (Apr/May 2019): In this article, we share an example of how students need to step through content, visuals, and data to make meaning and pull everything together into a CER.
• Data Bite: Interpret Data in Context: In this short clip, we explore what it looks like for learners of different levels to identify data patterns and then interpret what that pattern might mean for a question.
• Dataspire: What to Ask (K-12 and grade-banded) ($): This versatile resource includes: targeted, grade-appropriate prompts for different types of graphs, maps, and visuals to deepen how students analyze data, all in a slidedeck format for flexible use.
• What are some strategies we can use to draw conclusions better?: Before we called them Data Bites! This 20 min video discusses six different strategies from other disciplines that we can leverage to our advantage to help students draw better conclusions from their data and/or make sense of the data better. 

Articulate Uncertainty       

A given collection of data comes from a subset of a phenomenon or a population. Therefore, some degree of uncertainty is inherent in any claims, forecasts, or inferences made from exploring the data. Degree of uncertainty is important to consider. Factors that contribute to limitations of data (and thus uncertainty) may include sampling (selection, how many, timing), methods of measurement or analysis, natural variability, or unmeasured confounding factors.

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

• Data Literacy 101: Why Is Variability Worth the Teaching Challenge? (Jan/Feb 2022): In this article, we explore what we mean by variability, why it is important for data use in science, and how we can integrate it more into what we are already doing.

Use or Build on New Knowledge     

Data are collected to gain new information for a purpose, such as to understand more about how something works, to predict future events, to improve a policy or design, to inform decisions, dialog, or a new investigation, or to satisfy curiosity. Transforming data to action might lead to sharing findings with peers or community, proposing a new investigation, redesigning and retesting a policy, model, or device, adopting a change in strategy, telling a story, or simply raising new questions.

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

• Where Sampling, Data, and Inferences Meet: ADVizE mini-lecture: In this short video, we explore what a sample is, why it is important to consider when working with data, and how our sample influences what inferences we can draw from our data.
• Data Literacy 101: What Can We Actually Claim from Our Data (Feb 2020): How knowing what you can and cannot say from your data is core to students’ understanding of how to “distinguish observations from inferences, arguments from explanations, and claims from evidence” (NGSS 2013).
• Data Literacy 101: Communicating With Data Around Phenomena (Jul/Aug 2024): In this article, we outline a simple instructional strategy for integrating and utilizing data visualization into our phenomenon-based units. Additionally, we recommend two reflection exercises to consider.

 General Resources

There are also a range of resources related to Interpreting & Using 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.