Workshop: Barriers and Solutions to Teaching with Large Data Sets
June 11-12, 2019
Carleton College, Northfield, Minnesota
Application information: Applications will be available here in February 2019
The emergence of large, sensor-based data sets provides an opportunity to engage students in STEM and improve quantitative reasoning through open-ended exploration and interpretation of real-world data. The wealth of large authentic data sets online provides an opportunity to engage students in scientific inquiry while simultaneously improving quantitative reasoning. Open-ended exploration through the analysis and interpretation of large data sets can have substantial benefits as students explore the stochastic nature of environmental and Earth systems. At the same time, there are structural, pedagogical and time limitations to teaching the quantitative skills undergraduate students need to successfully work with data.
This workshop will bring together faculty and instructors to build a community interested in understanding current best practices and strategies, as well as the continuing needs and barriers associated with teaching quantitative reasoning and teaching with large data sets. Time will be allocated fro individual action planning and opportunity for peer input. Participants will help create a community vision and identify the topics, skills, and people to inform the design and development of teaching modules at a second EDDIE workshop scheduled for Fall 2019.
- Catherine O'Reilly, Illinois State University
- Dax Soule, SUNY Queen's College
- Cailin Huyck Orr, SERC at Carleton College
- Ellen Iverson, SERC at Carleton College
- Andrew Haveles, SERC at Carleton College