There is so much that students can do with data, but we really want to think about how they can use data to make decisions about their prototype in this engineering experience. Now, we are going to look at how analysis and interpretation can be used to help students determine the best solution based on the criteria and constraints that were identified in the experience.
How might HLPA apply to the airplane experience you completed at the start of this Design Cycle? When Mr. Sanchez’s class did the paper airplane activity, students noticed that the airplanes that had a wider wingspan seemed to go farther than those that had more streamlined or narrow wings. Students decided to create a test to find out if they were right. The first thing they did was measure the wingspan of each plane. Then they flew each plane 10 times, marking and measuring each time. The students determined the average flight distance for each wingspan and recorded them on this data table:
Students decided to graph the points in order to get a better picture of the data trends. They discussed what type of graph would best represent their data. They considered the bar graph but realized bar graphs are best for data that fall into distinct categories. They decided a line graph was a better fit. Their graph looked like this:
The NGSS asks students to identify linear and nonlinear trends in data. What trend might students have observed in the data above? Is the data linear or nonlinear? Is it perfectly so?
Next, students determined the HLPA for the airplane data. They used a table like the following:
Students looked at the HLPA data and discussed what other variables -- such as wing width, weight, and angle of flight -- might be affecting the length of the airplane's flight.
In what ways might students test these questions in order to consider more variables in their re-design?