Analyze Data

How the item cluster is used as a learning task:

1. Initial Data Analysis - Participants work individually on selected-response items to identify patterns in the Lightning-Caused Wildfires data.
2. Group Data Analysis - Participants share initial patterns, then...
   • ...generate additional patterns in the Lightning and Human-Caused Wildfires data.
   • ...ask authentic questions about lightning-caused and human-caused wildfires.
3. Launching Investigations - Participants study SEP #4 - Analyze & Interpret Data and SEP #1 - Asking Questions & Defining Problems, then...
   • Use SEP #4 & #1 to help generate additional questions.
     1. Cluster related questions to identify themes for potential investigation.
     2. Determine the nature of the questions (e.g. answerable with the data... investigative vs. factual... correlation vs. causation).
   • Choose an interesting / important question, and develop a general plan for answering the question by using SEP #8 - Obtaining, Evaluating & Communicating Information.

Why this kind of activity can be helpful.

Students lack frequent, authentic opportunities to work with data (see SEP #4 - Analyze & Interpret Data):

• that are large, related sets of data
• that require students to determine which data are relevant/not relevant
• to identify patterns that lead to questions that may/may not be answered with the data
• that require students to figure out ways to organize and process the data to answer questions

General notes

• This activity is intended for professional development, but there may be spin-offs for classroom practice.
• The focus of activity is on the student/teacher practice of Analyzing & Interpreting Data (SEP #4) in relation to other Science & Engineering Practices, especially SEP #1 - Asking Questions & Defining Problems and SEP #8 - Obtaining, Evaluating & Communicating Information.

Slides (for professional development)

• Launching a Wildfire Unit with Data

The Item Cluster(s) & Other Tools

Item cluster and/or Modified Tasks

The MS Wildfire Data item cluster can engage participants in various aspects of Practice #4 (Analyzing & Interpreting Data) in conjunction with ways of reasoning in the Crosscutting Concepts.

The complete data set and modified tasks provide a more open (all constructed-response) data analysis experience for students:

• Complete data set: Wildfire Data (2001-2019)
• Modified task: (#1-2) Patterns in Wildfire Data
• Modified task: (#4) Using Wildfire Data to Support Conclusions