Email to connect: email@example.com
Title: Integrating Professional Mentorship with a 3D Printing Curriculum to Help Rural Youth Forge STEM Career Connections
- Economically disadvantaged youth residing in rural mountain tourist communities represent an important and underserved rural population. These communities typically include a large percentage of children that are English language learners. Our NSF ITEST project investigates strategies that help middle-school youth in these communities to envision a broader range of workforce opportunities, especially in Science, Technology, Engineering, and Mathematics (STEM). This article highlights the initial findings of an innovative model that involves working with local schools and community partners to support the integration of engineering design challenges, 3D printing technologies, career connections, and mentorship into formal educational experiences to motivate and prepare rural youth for future STEM careers. This article describes the implementation of a new 3D printing curriculum and an integrated mentoring experience at two middle schools in the US rural mountain west. Our project established a partnership with a local STEM business -- a medical research institute that utilizes 3D printing and scanning for testing human surgical devices and procedures -- to provide mentorship to students. Volunteers from this institute served as ongoing mentors for two cohorts of 8th-grade students. The STEM mentors guided students through the process of designing, testing, and optimizing their 3D models and 3D printed prosthetics, providing insights into how students’ learning directly applies to the medical industry. Different forms of student data such as clinical interviews and pre/post STEM interest and spatial thinking surveys were collected and analyzed to understand the impacts of the mentorship program on student interests.
- ICS program: Cognitive Science Combined PhD
- Advisor: Tamara Sumner
- Home degree department: Computer Science
- Name of research lab: Sumner Lab