1. How Tall Are You? By Marjorie Pine, Grade 8 Science

2. Lesson Focus from Massachusetts Curriculum Frameworks 2006 Design & conduct an experiment specifying variables to be changed, controlled, & measured. Draw conclusions based on data or evidence presented in tables or graphs; make inferences based on patterns or trends in the data.

3. Experiment Basics Students predict if their height will be the same or change from morning to evening. Two measurements are taken by each student: height in morning and, again in evening.

4. Experiment Basics Students enter data into iSense. Exploration of visualizations. Explanations of visualizations.

5. Entire Team Scatter Plot This should be a picture of all 66 data sets. There is a cluster of points on the left of the graph and a cluster of points on the right of the graph. Students are asked what the data tells them. Using whole group discussion, we manipulate the data, changing criteria.

6. Other Visualizations As students peel away some pieces of data, we get to looking at just a couple of students. Visualizations suggest students are shorter in the morning and grow throughout the day. Students look at the data table associated with the visualization. It’s noted that how the data is entered affects the visualization.

7. Class Discussion Students explain why they entered the data the way they did (FIFO vs AM comes before PM). Then we looked at visualizations under different groupings. Gender, for example. Students were asked the visualization represented. (Were females taller than males? (No))

8. Focus Transition The lesson morphed into a discussion about really looking at data and interpreting it correctly. For example, if we look at only a single individual’s height morning and evening, the height difference looks dramatic. Reviewing the scale of any visualization is critical.

9. Individual vs Multiple Data Sets

10. Learning Outcomes Many students had difficulty saving their data; an error message recurred. –Sometimes the error message came up even though the data had been saved. Student data was saved more than once. –Sometimes students had to re-enter the data in order for it save. –At one point, students and I were looking at the same visualization, but mine was different. –How data is saved is important (AM, PM order).

11. Learning Outcomes Students started to develop an appreciation for how to look at data. Ended with review of manipulated, responding, and controlled variables.