Activities and Examples: Using Data in the Classroom Today

This collection contains links to on-line activities for teaching STEM concepts with data of all types and examples that describe ways in which on-line, published, and student-collected data are currently used in teaching and learning.

Show more background about the examples

This collection includes both data activities identified from across the web as well as a local example collection.

The initial example collection was created in collaboration with the Geological Society of America where the examples were given in a poster session "Using Data to Teach Earth Processes: An Illustrated Community Discussion" sponsored by the NAGT On the Cutting Edge program. We invite you to contribute additional examples in any of the STEM disciplines.

Each example includes a description of the activity, its learning goals, the context in which it is used, information on the needed data, tools, and activities and suggestions for evaluation.

Skip to search results Skip to search facet filters Skip to text search form Skip to pagination

Refine the Results↓

Inquiry Level

Special Interest

Data, models, or simulations 2 matches

Subject Show all

Mathematics

2 matches

Statistics 2 matches

Grade Level Show all

College Upper (15-16)

2 matches

Current Search Limits:

Results 1 - 2 of 2 matches

Simulating Size and Power Using a 10-Sided Die part of CAUSE Teaching Methods:Teaching with Data Simulations:Examples
This group activity illustrates the concepts of size and power of a test through simulation. Students simulate binomial data by repeatedly rolling a ten-sided die, and they use their simulated data to estimate the size of a binomial test.

Simulating a P-value for Testing a Correlation with Fathom part of CAUSE Teaching Methods:Teaching with Data Simulations:Examples
This activity has students use Fathom to test the correlation between attendance and ballpark capacity of major league baseball teams by taking a sample of actual data and scrambling one of the variables to see how the correlation behaves when the variables are not related. After displaying the distribution of correlations for many simulated samples, students find an approximate p-value based on the number of simulations that exceed the actual correlation.