Reasoning and Data and Chance Summary

Monday - Friday, June 26 - June 30, 2006

The Reasoning with Data and Chance group spent the first two days of this week learning how to use Fathom Dynamic Statistics™, a data visualization and analysis program that will be used in some of the final projects. Members read though newly developed guidelines for integrating data analysis and statistics into the K-12 mathematics curriculum (A Curriculum Framework for Pre K - 12 Statistics Education, ASA, 2005). These guidelines were developed by the GAISE project (Guidelines for Assessment and Instruction in Statistics Education).

Members agreed to develop a series of lessons that will illustrate effective ways to implement the GAISE guidelines in grades 7-12.

During Thursday and Friday, members brainstormed to find a single statistical question that could be investigated from a variety of approaches and levels. By focusing on a single question we hope to clarify different levels of statistical thinking throughout grades 7-12.

We agreed to study the following question: "Can you estimate the weight of your backpack accurately?" We chose this topic for the following reasons:

  1. The topic would be interesting for students in grades 7-12.
  2. The topic allows for easy data collection.
  3. The topic is appropriate and meaningful for a wide variety of students from many backgrounds (since most students bring a book bag or backpack to school).
  4. The topic allows for a rich study of statistical topics: forming a focused question, appropriate data collection, understanding variation, analyzing associations.
  5. The measurements collected vary in an interesting and instructive way.

In order to get test data, members will take a convenience sample of SSTP teachers at PCMI next week, and ask them to estimate their backpack weights (in pounds). Each subject will also find the actual weight of their backpack. This data will then be used as a common basis for all projects that are developed.

Next week, members will begin collecting data and developing their lessons.

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This material is based upon work supported by the National Science Foundation under Grant No. 0314808 and Grant No. ESI-0554309. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.