Three-dimensional Learning Progression Understanding data representations. Lessons 1 and 2 focus on helping students make sense of representations of data about Earth systems. This is difficult and challenging for many students. We see four interconnected issues: Representation: Students see many different representations of data about Earth systems. For example, in Lesson 1 students see (a) animations of satellite data showing maps that change over time, (b) tables with numerical representations of sea ice extent, and (c) graphs showing patterns and trends. Students need to recognize that even though they look different, these are all representations of the same phenomena. They also need to recognize that the same variables (e.g., time, extent of sea ice) are represented in different ways, and that there are different choices about which data to represent from a larger data set. Generalizability: Data about Earth systems are usually selected to be representative of patterns in systems, but the relationship between the patterns in the representation and the patterns in the Earth systems can be difficult and confusing. For example, students make graphs after choosing one month (September) and recording data for September of each year. What patterns in these data extend to other months? How are patterns in these data connected to data from other places, such as the Antarctic or lakes that freeze over in North America? Students need to consider and answer questions such as these. Short-term variability: Like many data sets about Earth systems, Arctic sea ice data show random variation from one year to the next; there is no good way to predict whether the extent of ice will go up or down in the next year, or how much. Most humans are very good at finding patterns, even when they don’t really exist (this is why games of chance are so popular). So students need to recognize randomness and understand how it limits our ability to make claims about short-term patterns or predictions of how sea ice will change in the next year. Long-term trends: Arctic sea ice is also like other Earth systems in that even when data are noisy in the short term, there can also be clear trends over longer periods of time. Students need to develop strategies for identifying and representing long term trends, such as the averaging strategy that they practice in Activity 1.5. Explaining patterns in Earth systems data. Students need to recognize that random patterns in short-term variability are very difficult or impossible to explain, but that good explanations for long-term trends are often possible. Some students will probably suggest that the decreasing trend in Arctic sea ice is due to “climate change” or “global warming.” They need to recognize that this kind of explanation—recognizing relationships between variables—is useful, but doesn’t go very far. Scientists look to understand mechanisms: How is climate change affecting Arctic sea ice, and what is causing climate change. We hope that Lesson 1 will end with these unanswered questions, to be addressed in later lessons. Key Ideas and Practices for Each Activity Activity 1.1 includes the pretest for this unit. The discussion in this activity (a) helps students to anticipate and begin thinking about the questions that they will answer in this lesson and (b) helps you as a teacher see how your students reason about patterns in Earth systems, including climate change and global carbon cycling. Activity 1.2 establishes an important question: why is Arctic sea ice melting? Although students do not gather the evidence, they need to answer that question in this lesson, in this activity they are given an opportunity to share their initial ideas about why this phenomenon might be happening. Activity 1.3 gives students an opportunity to practice interpreting data in the Arctic sea ice graph, which is one of many data sets they will use throughout the Human Energy Systems Unit. Students retrieve data from a publicly available data set about Arctic sea ice. They use the data to construct a graph. When they first construct the graph, it is difficult to see a trend due to the noisy nature of the data. They also discuss how different representations of data allow for different interpretation and knowledge. In Activity 1.4, students use samples of messy data sets (similar to Arctic sea ice) to find a “signal in the noise.” The students develop different strategies for finding a trend line in noisy data. Finding a trend is a key practice in analysis of scientific data sets. Finding global trends in merged data sets, for example, is how scientists have learned that climate change is taking place as a result of human activity. This activity helps students develop a critical eye when analyzing data that ideally will help them distinguish a pattern in noisy data. In Activity 1.5, students then return to their graphs of Arctic sea ice and use the same strategies from the previous activity to find a trend in their arctic sea ice data. They discover that arctic sea ice extent is decreasing over time. However, at the end of this lesson they only have evidence to show that the ice is decreasing, but not why. These questions will be answered later in the unit when the students discuss the greenhouse effect and CO2 emissions. Content Boundaries and Extensions Lesson 1 is intended to help students develop facility is working with Earth systems data and to encourage them in their roles as Questioners—generating questions about how and why the Earth is changing. The answers to those questions will come in later lessons.
