Overview
Key Considerations
Lessons
Learning Outcomes
Students will be able to...
- Describe where temperature data comes from and how scientists use this data to identify climate trends
- Interpret data visualizations to describe and identify trends in messy, real data
- Use data to make logical predictions of future conditions
Students will understand…
- Changes in ecosystems around us are local instances of global patterns of change.
- Scientists use data to investigate a question you have about a phenomenon.
- Data can be represented and organized in different ways in order to answer different questions or reveal new information.
- Using data to understand a phenomenon involves being able to read and make sense of data representations (tables, graphs, maps, etc) and models.
- We represent data in a combination of graphs and maps to show ecosystem change in both time and space, and we use models to think into the future.
Time Estimate
1 to 3 class periods
Audience
5-8
Standards Alignment
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Next Generation Science Standards (NGSS): CCC 7
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Next Generation Science Standards (NGSS): SEP 4
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Next Generation Science Standards (NGSS): MS-ESS3-3
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Common Core: Standards for Mathematical Practice: Construct viable arguments and critique the reasoning of others.
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Common Core: Grade 6 - Statistics & Probability: Develop understanding of statistical variability
This module was developed to prepare students for their LabVenture experience though it can be used in many contexts to introduce climate change and support students' data skills and understandings.
Students will need laptops, chromebooks, or ipads and internet connection to explore the data. If devices are not available the maps and graphs can be printed ahead of time.
Lesson 1: Global Trends
Students use data to explore how global temperature is changing over time and develop strategies for identifying trends in data.
Lesson 2: Regional Trends
Students explore how climate and climate change vary from region to region. Students work with climate data from the Northeast and develop strategies and habits of mind for analyzing and interpreting large and messy data.