Repeated searches, even when nothing is found, are important for data collection in Citizen science projects. Searching during the same or different weather, locations, and vegetation provides the opportunity for students to not only collect valuable data, but to also gain a deeper understanding of their schoolyards and patterns in their data. This is valuable information not only for the Lost Ladybug Project, but for comparing with a another location’s data as well.

Which species have your students found so far? Are the patterns you see at your school similar or different than the ladybugs found at other locations? This month, you’ll start to look at how your school’s findings relate to other locations, which helps address the second overarching scientific question. Now that students are beginning to make sense of data from other locations in the state, revisit their predictions related to the question about geographic variance.

Looking at the file, he is concerned that the export as-is may be challenging for students to interpret. Before sharing the file with students, he decides to modify it in the following ways to make the file more student-friendly.

• Selecting a single user/location and filtering out submissions from other people so students are accessing multiple submissions by the same user.
• Hiding columns that students aren’t likely to use (e.g., submission ID, altitude): With columns hidden, students can focus on more pertinent pieces of the dataset (e.g., city, habitat, weather, time of day).

For more information on working with Google Sheets, including hiding columns, check out the Google Sheets cheat sheet.

• Sorting the entries by date submitted: With the entries sorted by the date they were submitted, students can more easily look for patterns over time.
• Setting up filters so that students can narrow the data set in different ways based on the factor they’re examining. For example, Mr. Jones wants his students to compare the habitats where ladybugs have been found in different locations, so he sets up a filter on the “habitat” column.  With the filter set, students will be able to select particular habitats and view only entries of ladybugs found in those specified habitats.

Technical support for how to sort & filter data

Analyzing and interpreting data involves organizing and graphing collected data and making sense of it (e.g., finding relationships, patterns, outliers). As students begin to analyze and interpret another location’s data this month, they may be overwhelmed by the amount of information to consider. Help students manage the information by reviewing key strategies for analyzing and interpreting data.

Sample Data Set

• Provide students opportunities to decide which data are relevant and irrelevant to their investigation question.
  • What is the question you are trying to answer?
  • What categories of information from data spreadsheets will help you answer the question?
  • What categories of information from data spreadsheets will not help you answer the question?
  • Are you missing any information that you need? How could you get it if you don’t have it?
  • How can you organize only the parts of the data spreadsheet that you need to look for patterns?
• Have students make connections between the relevant data they have selected and the questions they have developed.
  • What patterns would make sense as an answer to your question? Do you see any of these patterns in your data?
  • Are you finding any unexpected patterns? Why are you surprised?
  • If you find a pattern to describe one factor, is there another factor that could be related to it? What pattern(s) do you see there?
  • What will you do if you cannot find a pattern to answer your question?
  • How will you decide how confident you are that you have enough data to conclude that a pattern really exists?

Modeling/Guiding Questions:
While it is important to model these practices for students, it is also important to consider the sources used when modeling. Avoid using the same exact information that students will work with to preserve the authenticity of students experiencing the practice of analyzing and interpreting data. Modeling this practice with a comparable data set that is unrelated will help students see the practice in action and may need to be done multiple times before students fully grasp the concepts.

Scaffolding questions for each investigation group:
When students are divided into groups, each group will consider a particular factor. The groups will collaborate to find patterns in another location’s data related to the selected factor and evaluate their own school data. Allow groups to generate their own questions and/or share select questions from the list of sample questions, organized by factor.

Because the other location is different from yours, allow students time to consider how data collection might be different for each location. After sharing the map with students highlighting the other location, ask students:

What considerations or features of the school grounds might be important to keep in mind prior to data collection for the other location? Are these the same for your school and the other location?

Time of year, location (mountainous, plains, coastal), significant weather, climate, vegetation, urban/rural, terrain, time spent looking, number of students looking at one time

Think about the features of the other location and your school. How can you connect the data to a comparison feature and make a fair comparison between your school and the other location?

Check student responses for the following: 

• Students compare the same feature for both  locations (writing about the temperature at both locations instead of temperature at your school and vegetation at the other location). 
• Students refer to specific data from both locations as evidence for their explanation. 
• Students have considered how the feature is related to the number of ladybugs found at each location.
• Students have considered how the feature is related to the variety of ladybugs found at each location. 
• Students describe how reviewing the other location’s data has supported or challenged ideas they already had about the feature before looking at another location’s data (this is helping students recognize the value of citizen science and considering data in addition to your own when making claims).