Curriculum Materials
Wisconsin Fast Plants (WFP)
Variation, differences based on both genetics and environmental factors, is a fundamental attribute of life. Individual organisms vary from one another, and organisms vary over time. This investigation allows students to observe, describe, and measure variation using a model organism, Wisconsin Fast Plants (WFPs). Focusing on the guiding question, “how do plants change as they grow?”, students will plant, observe, and measure WFPs. This activity provides opportunities for students to make observations about individual differences in WFPs and how they change as they grow. They then make decisions about how to measure those observations to generate quantitative data and create data displays to make claims about how plants grow. As they make claims, students will have opportunities to explain how different sources contribute to the variation in the data. These explanations help students consider the different inferences you might make about the variation. For example, if we think all the variation is from measurement error then we might interpret it as mistakes or noise. If we think the variation is from errors in a production process, then we might interpret it as things to improve in the process. In this investigation, though, much of the variation comes from differences in environmental and genetic factors, which provides opportunities to use the variation to explore the factors that impact plant growth.
This investigation equips students to understand that quantitative data emerge from decisions they make as they measure, organize, and analyze their data, and how, ultimately, their choices provide evidence to support their claims about plant growth. This activity also provides opportunities to contrast ways of thinking about variation that students may have been exposed to in their mathematics classes.
Inventing Data Displays
Unit 1 focuses on the development of representational and meta-
representational competencies, meaning that students progress from case-
based interpretations of data representations to those involving characteristics of
the aggregate. Students learn that the shape of the data arises from the choices that
designers make to show and hide aspects of the data.