Technology Education

For Women In Transition

This project in the Advancing Informal STEM Learning (AISL) program’s Innovations in Development track aims to broaden participation in STEM education among underserved populations through innovative and inclusive approaches to technology education. The project is designed to enhance knowledge and comfort with technology and develop computational thinking among women who were formerly incarcerated and are now seeking to reenter the workforce or adjust to their lives outside the criminal justice system (“women in transition”) in the Midwest. While women have become the fastest growing segment of the incarcerated population, prison education and reentry programs are not well prepared to respond to this influx. Women in transition are rarely exposed to STEM education and they are generally isolated from the digital world while in prison. Consequently, they face post-incarceration challenges in accessing and using rapidly changing digital technologies. Against this backdrop, this three-year technology education project will aim to help women in transition in Kansas and Missouri develop STEM skills relevant to job applications and post-incarceration adjustments. The project may serve as a template for building evidence-based workforce preparation efforts in informal settings, and the concurrent online peer networking and app development may also facilitate adaptation for and scaling to other regions and other similarly digitally disadvantaged populations. This project is funded by the AISL program, which seeks to advance new approaches to, and evidence-based understanding of, the design and development of STEM learning in informal environments. This includes providing multiple pathways for broadening access to and engagement in STEM learning experiences, advancing innovative research on and assessment of STEM learning in informal environments, and developing understandings of deeper learning by participants.

Predicated Upon

Computational Thinking

Problem Decomposition

Reduce the problem to a set of smaller, easier to manage, problems

Pattern Recognition

Analyze and look for repeating sequences

Abstraction

Reduce complexity by removing unnecessary information

Algorithm Design

Define logical steps to solve a problem

AISL women

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