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Snotty Plots: How Do You Graph a Sneeze? Activity - Tweaked to Practice Quantifying Data šŸ™‚

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Tweaked Lesson Plans 

This article is part of the “LP Tweaks” blog series showcasing how small adjustments to the questions, organization, and/or data moves within your existing curriculum can help align the learning to different data skills for your learners. This original lesson is strong, and the intention is not to communicate otherwise but rather to share how you could adjust things for a different desired outcome.

Written by: Naomi W


So imagine that you have a great data-based lesson called “Snotty Plots: How Do You Graph A Sneeze?” from Science Friday Educate on using real scientific data to investigate how far a sneeze travels for your learners to work on concepts related to MS-PS1-4. Your students have been exploring the structure and properties of matter in this unit and they like hands-on graphing activities.

However, based on your student's performance in the last unit you are looking to give them more time to “Quantify Data” in a practiced way to help them identify case values using real-world data with units in a histogram (seeBuilding Blocks for Data Literacy to explore the corresponding data skills of these areas more).

You are torn, you like the Snotty Plots lesson from Science Friday and your other colleagues are using it, but you are worried the questions aren’t directly getting at the data skills your learners need to practice at this point in the year, but the content is. What do you do?

Let’s check out some easy optional tweaks to make the lesson more of what you are looking for now.

Here is an example of how we can add a few questions to the lesson to more directly help your current students practice making sense of the numbers visualized in a histogram – the data skills we know they are weaker on and/or we are more interested in targeting with this specific data interaction at this point in the year.

The questions are designed to help students to “calculate statistical values” in grades 6-8 in appropriate ways. They do not add a large amount to the workload of the students, but instead, provide more targeted practice of reading case values from a histogram they construct in such a creative way in the original lesson. 

The new questions help students look at the histogram as its own type of graph. At first glance, students may look at it and think it may be a bar graph because that is what they are more familiar with. Bar graphs are useful for displaying data that can be divided into discrete categories whereas a histogram, on the other hand, is a graph that shows the distribution of numerical data. It shows the frequency or number of observations within different numerical ranges.

Even though the students have looked at bars before to identify patterns, they may not be as comfortable with using histograms to read off the frequency of case values. This is a critical precursor step – reading the histogram – to set students up for success in revealing or highlighting patterns in data. 

This line of questions can also be a jumping-off point to help students to consider the limitations of data. For example, you can ask students to reflect on factors that can affect how well their sample represents the whole phenomenon of sneezes! This is a great way to introduce, in age-appropriate ways, ideas of sampling bias or methods of measuring without it taking over the whole lesson.

Therefore, with the addition of 4 new questions, you can continue to use this tried-and-true lesson you found years ago while also better aligning it to your current learners' needs and/or your desired focus areas for your learners to practice data skills. 

And that is what we are going for…using a lesson that you like with a more strategic data focus to help learners build their skills :)

Will this exact tweak work for everyone or every lesson? Absolutely not!

In fact, other adjustments in this lesson could also help students that are needing to work on their ability to “Recognize and Describe Variability” skills at this time of the year. So rather than just practice quantifying the case values and frequency of distribution, you could adjust the questions to compare variability within each numerical range (or bin) and across the full range of data values for one student. You could extend it by comparing across students to pool together all of the class data and see how the range and variability shifts with more data.

So many possibilities!

The point is that with a better sense of what we are working towards with data skills (see Building Blocks for Data Literacy to explore the range of data skills K-12 our learners should be working to master) we can be empowered to make our existing curriculum work better for us and our learners…rather than needing to find or write all new curriculum.

Give it a try! What in your next lesson with data can you slightly adjust to make it better hit the skills you want your students to practice? Let us know how it goes.