New Article by Jas and Larry

posted in: Future of Work, Research | 0

Data Science Outreach Educational Program for High School Students Focused in Agriculture
Jasmine B Sami GWU
Zachary Stein
Krystin Sinclair
Larry Medsker


Members of the Data Science Program at George Washington University (GWU) designed and implemented a tuition-free two-week summer camp at GWU for high-school students from the Washington Metro Area. The United States Department of Agriculture (USDA) Office of the Chief Information Officer and his staff were our main partners in the project. The goal was to use Open Data related to Science, Technology, Engineering, Agriculture, and Math (STEAM) as the means of giving students experience with real-world data analytics methods and tools, as well insights into careers in data science and agriculture. GWU provided a prime location in the District of Columbia, expertise from the data science Master’s program, and experience in active learning pedagogy. This two-week long (July 23-August 3, 2018) project-based summer camp introduced participants to the idea of using data for making important decisions as applied to topics in food nutrition, forestry, and urban agriculture. The camp had a special focus on effective and compelling ways to visualize data. Students explored, analyzed, and reconfigured quantitative and qualitative data, using fundamental graphical principles to present their projectrelated findings. GWU faculty and students provided guidance on the kinds of questions that can be addressed with data, on the challenges of gathering data through interviews and surveys, and on the techniques for presenting compelling arguments based on data. The camp curriculum was designed for project-based active learning with the aim of all the students gaining skills with ArcGIS, Excel, Tableau, and ESRI’s Collector application for creating GIS maps. An overall goal of the camp was to spark interest in data science using the STEAM context. The learning goals were that participants finishing the camp would be able to demonstrate basic knowledge of the methods of data science by applying them to specific problems in the STEAM domain area of the camp and presenting the results in conference-style setting. Achievement of the learning goals was assessed using a survey tool. The positive results are discussed along with conclusions, limitations, and recommendations for future camps.

Ryan Watkins