PDI-1: Using Mathematical Representations to Talk About, Model, and Explain Scientific Phenomena

Date: Wednesday, March 9, 2011
Location: Marriott San Francisco Marquis Hotel, Room Yerba Buena 1
Intended Audience: Grades 4–8

Recommended Pathway Sessions

Presented by:

TERC

Presenters:

Sally Crissman, TERC
Sue Doubler, TERC

Framing Questions:

1. How can working with data deepen all students' scientific understanding and habits of mind?
2. How can working with data help students learn to reason critically and flexibly?
3. What are strategies for teaching students to use data to debate, argue, and model?
4. How can students' learning about data in math and science complement each other?

Description:

If reasoning from and about data is such an important part of science learning, why is so little time devoted to it in the classroom? We know that teachers lack their own strategies for making sense of a many numbers in a data table, knowing when discrepant data are significant and when they are not, knowing if there's sufficient evidence to support an argument.

This Professional development Institute (PDI) will provide strategies for making the most of opportunities to work with data in the service of deeper understanding of science concepts and scientists' ways of knowing.

The day will be orchestrated so participants move from small- to full-group work and discussion. We'll begin with a short investigation of transformations of matter; collect data, decide how to represent it, collate and analyze group data, and debate claims. This common experience will anchor discussion of the challenges of helping students become proficient working with data and sharing successful techniques and approaches. Participants will work with a variety of data representations such as measure lines, line graphs, change over time graphs. There will be time for participants’ own questions. These were some questions 2010 PDI participants raised and discussed:

• How can I add a data literacy strand to an already crowded science curriculum?
• What data representations are most important for students' science learning?
• What does it look like when students use data to develop arguments, models, and theories?
• How do we help students leverage their work with data in mathematics in their science investigations?

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