Education Technology

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  • Subject Area

    • Math: Algebra I: Data Analysis
    • Math: Algebra I: Statistics
    • Math: Algebra I: Equations and Inequalities
    • Math: Algebra I: Expressions
    • Math: Algebra I: Linear Functions

  • Author

    Fred Mueller

  • Level

    6-8
    9-12

  • Activity Time

    60 Minutes

  • Device
    • TI-83 Plus Family
    • TI-84 Plus
    • TI-84 Plus Silver Edition
    • TI-Navigator™
  • Other Materials
    A sufficient supply of mousetrap cars. A sufficient supply of stop watches. A pre-measured and marked 'drag strip' (a typical school hallway) on which to run their racer. From the start line the 'drag strip' should be cross-marked at several different distance intervals. A supply of colored markers with which the students can decorate their racer.
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How Fast Is Your Racer

Published on 07/29/2007

Activity Overview

Students become familiar with collecting and analyzing linear data. Students first perform a manually linear fit to their collected data, and are then introduced to the linear regression analysis capabilities of the calculator. The time taken for mousetrap racers to cover predetermined distances is measured and recorded. Students then use this data to examine linear relationships, manually fit equations to the data, and investigate linear regression analysis.

Before the Activity

This activity uses mousetrap cars which the students have already constructed either at home or during a previous class period. All students should have the same type of mousetrap car. Additional information on the operation and construction of a mousetrap car, and an inexpensive 'ready to assemble' car that can be used for this activity is the Little Moe Racer available at http://www.docfizzix.com/. Bulk supplies from which mousetrap cars can be built are also available at hobby and hardware outlets.

During the Activity

Working in teams students launch their mousetrap cars from a common starting point and then record the time taken to cross the marked distance intervals. In general, the maximum distance traveled will depend on the 'road surface'. The cars will travel farther on hard surface floors than they will on carpet. On a hard surface the 'Little Moe Racer' can easily travel in excess of 50 feet; while on a carpeted floor distances of 20-30 feet are more typical. Reasonable distance intervals should be chosen by the teacher so as to provide a good 'spread' in the collected time versus distance data. If there are 5 measurement intervals, the activity can be expedited by having the students work in teams of 6. One student starts the racer on its way while the others are positioned at the various distance intervals to measure and record the times. Instead of allowing each group to run their car only once, consider allowing 3 trials per car and then having students average the 3 collected time per distance intervals. Allowing students to make multiple runs lengthens the activity time but opens the door for valuable discussion concerning reproducibility, accuracy of experimental results, and why scientists always conduct experiments many times before coming to any conclusions. Less cluttered class-wide data can be collected by having individual groups collect time data for different distance intervals. For example, one group might collect times for 5. 10, 15 and 20 foot intervals, while another for 3, 8, 12 and 17 foot intervals. Although students work in teams each individual should make a copy of the group's data. In this activity TI-Navigator is used to aggregate student data, take screen captures to check progress as well as understanding, and to display the linear data fits and regression analyses produced by the students.

After the Activity

Have students discuss sources of error in the data collection process. Prompt students with probes such as: What factors made some of the cars go faster than others? How is 'variation' in the data represented graphically? Why did we repeat the experiment multiple times and then average the results? What is the benefit of fitting a line through the data? How did the entire class' data compare to your individual data?