This project will contribute to the Earth science education community's understanding of how engaging students in authentic computer science experiences, including innovative methods such as machine learning, can deepen students' motivation and learning of geoscience concepts. The SeismicML project will engage middle school students in Anchorage, Alaska, in authentic investigations of their community's natural and human-caused seismic events using practices of professional geoscientists. Through a partnership among teachers, geoscientists, educational researchers, technology and curriculum developers, and science administrators, the project will create a one-week seismology curriculum centered around an innovative block programming interface called Dataflow. Within the curriculum, students will (1) explore the occurrence of earthquakes in the community by installing scientific grade seismometers in their school, (2) use machine learning to identify and classify seismic events, (3) create data visualizations of seismic events registered at their school, and (4) construct block programs that import real-time seismic data to find patterns in seismic events over different time periods and across different regions. The project will produce evidence-based teaching strategies that promote students' ability to conduct authentic computational science investigations. The goal of the SeismicML project is to engage Alaskan middle school students in contextualized inquiry investig