Across the country, state middle school science tests are putting dramatic new emphasis on students’ ability to analyze data. This is sound pedagogy: data analysis is an increasingly important tool, in higher education and in the workforce, in science and countless other fields.
Data in the News
Number of Suspected or Confirmed Norovirus Outbreaks Reported by NoroSTAT-Participating States Per Week, 2012–2025
Source: CDC
Generative AI use cases will have different impacts on business functions across industries.
Source: McKinsey
When it comes to helping teachers prepare students for this new component of the tests, we have found a wide variance: some states offer an abundance of practice tests and resources, while others offer very little support at all.
That’s where we come in. In earlier Mosa Mack Science blog posts, we’ve presented strategies for turning students into data-lovers, and advice on using performance assessments to prepare students for state science tests (including “what to notice” about state test questions). In this blog post, we’ll focus exclusively on how state tests measure data analysis, and how teachers can help students develop that skill in preparation.
What Data Analysis Looks Like in State Tests
First, here are some of our key takeaways on how data analysis is featured in state tests:
- Test questions present data in a variety of formats (i.e., in graphs, within a sentence), and they combine different types of data (i.e. population, temperature).
- Test questions often come in clusters, centered around applying scientific skills and reasoning to a new phenomenon, with a variety of data featured throughout the question cluster.
- Tests require students to use data to draw conclusions, make predictions, and reason
- “Data moves” are important on tests, too: shifting data format (i.e. from graph to table); combining different data sets – particularly within question clusters.
Data on State Tests
MA Grade 8 Science
NJ Grade 5 Science
1: Introduce Data Analysis as an Important Skill
Early in the year, teachers often use standalone lessons to introduce core scientific practices like lab safety and claim-evidence-reasoning. We can spotlight data analysis early in the same way.
Start simple, opening students’ eyes to “the world of raw data” all around them, presenting data analysis as a crucial tool for making meaning in science. Our data analysis blog is full of sources for real-world data to bring into the classroom, including NASA, the National Weather Service and the Centers for Disease Control – check out the “Resources for Great Data” section at the end of the blog. You can also download practice data sets for your classroom here.
DOWNLOAD PRACTICE DATA ANALYSIS QUESTIONS HERE
Via practice and discussion, introduce techniques of data analysis incrementally, building a foundation for more complex data analysis in the year to come. The key is to model and reinforce overall skills and strategies of data analysis, so that students can apply that learning to new challenges they will face on state tests.
2. Integrate Data Analysis into the Curriculum
When exploring a real-world phenomenon on any topic with your class, present data analysis as an “at-the-ready” tool for exploring and engaging with that phenomenon.
- Introduce graphs, charts, tables, statistics and maps wherever possible.
- Look for opportunities for students to work with raw data, whether it’s collecting data from their own labs or challenges or analyzing existing data.
- Get students used to doing their own “data moves” to reason and draw conclusions.
Student presentations are a fantastic way to concretize and reinforce this process of meaning-making. We all have seen that feeling of joy and accomplishment wash over students as they present their very first pie-chart or graph to their classmates, realizing in that moment: “I am a scientist.”
In our own Mosa Mack Science curriculum, data analysis is thoroughly integrated into nearly every unit. To locate these data-rich lessons, use this comprehensive list as a guide.
DOWNLOAD DATA-DRIVEN TEST PREPS HERE
Data Analysis in Mosa Mack Science
From Biodiversity
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3. Practice Practice Practice
It’s essential to demystify the language and format of state testing early on, so find time to make practice testing part of the classroom experience.
- Start simply with standalone questions and single data sets.
- Move up to question clusters and more sophisticated data moves.
- Don’t move on until the concepts and skills are fully integrated.
Many teachers have had great success making Data Analysis test prep a weekly practice, assigning sample questions from a variety of different state tests each week (converting state practice test questions into Google Forms for students to easily access). Teachers might start by having classes chop up questions/question clusters as a group and analyze them together, then move on to individual practice – again, reinforcing the underlying skills for students to apply to new questions.
Where to find practice tests and questions? Mosa Mack has you covered – check out our extensive collection of unit-by-unit question banks and sample tests, downloadable here.
DOWNLOAD DATA-DRIVEN TEST PREPS HERE
State Science Test Practice Questions
Teachers can also make up their own data analysis practice questions, based on phenomena they are already teaching or new phenomena, too. This process can be a real challenge, so start with simple questions and work your way up. The more familiar you get with the language and format of state testing, the more comfortable it will feel. And it always helps to share and test-run potential test questions with colleagues.
4. Track Performance and Progress for Science Success
As students advance, use homework, graded tests and class presentations to assess their mastery of each particular component of data analysis.
- How well are they translating data from one form to another?
- Are they struggling with data-based reasoning skills?
- Review, reinforce and adjust the pace of instruction accordingly.
- Pay particular attention to how students are improving (or struggling) when it comes to test format and language.
The more practice test questions and data analysis lessons you have in your back pocket, the better (see resources below). When some students need extra help, deploy techniques for differentiated learning (i.e. have students work in groups; tailor lessons to individual learning styles). When the actual test-time rolls around, we want all of our students to feel confident, empowered and fully prepared.
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