NSF Convergence Accelerator Track H: Developing Experiential Accessible Framework for Partnerships and Opportunities in Data Science (for the deaf community)

Purdue University, West Lafayette IN

Investigators

Abstract

This project will create university-to-industry use-inspired research partnerships, curriculum in Data Science, and projects geared specifically for deaf learners, to empower deaf data scientists in the workforce. DEAF PODS will collaborate with partners in industry and academia to create strategic initiatives to overcome barriers and biases that deaf individuals face in the workplace. Developing Experiential Accessible Framework for Partnerships and Opportunities in Data Science (for the deaf community) ["DEAF PODS"] will enable 75 deaf and hard of hearing students to work in teams on data-driven projects with mentors. These interdisciplinary partnerships will span several applied domains. These research stipends will be available for deaf undergraduate students from any college in the USA. The materials for data science training will be available to be used in the many use-inspired research projects in the Convergence Accelerator program. DEAF PODS will also provide nationwide coordination of accessible research experiences for deaf students (from any university) in the data sciences. The DEAF PODS model is designed to be reproducible at other universities. DEAF PODS will strengthen and support the experience of students whose universities cannot (or will not) invest sufficient funds for isolated deaf learners. A key goal is to align faculty, students, and companies, to work closely on use-inspired research. This project will foster a welcoming research culture throughout the year. A key expected outcome is a broader pathway for deaf college students to graduate school and/or to data science careers in industry. The project will use culturally responsive pedagogical strategies to teach data science content to deaf populations. Deaf mentors and students will organically develop the fast-developing language of data sciences in American Sign Language and will freely share this data science content online. Because ASL expression is heavily influenced by the subject's context, it is imperative to work directly with deaf business owners and deaf scientists who are domain experts. A key strategy for success is the emphasis on deaf-deaf mentoring: deaf employees and fluent signers mentoring deaf students. While deaf employees share domain expertise with student learners, the mentors will also benefit by picking up new data science skills during their mid-career. Industry culture and pathways are key aspects of the planned project. The investigators will partner with dedicated corporate partners who have committed to cultivating a welcoming environment, with a culture where deaf data scientists can thrive. DEAF PODS and the greater STEM community have a timely opportunity to create career pathways in data-intensive industries, by offering use-inspired research experiences for deaf and hard of hearing students and mentors. DEAF PODS will use flipped classrooms with active learning, in projects (not lectures) as a crucial piece of the student learning environment. One expected outcome of the project is the integration of captioning, transcripts, and bilingual videos, in both ASL and English, as well as front-loading new vocabulary, immersive examples, and vignettes. Gallaudet has also just begun a new Data Science program. The project will start the work toward building a Corporate Partners program at Gallaudet during Phase 1. Purdue will help to integrate 9-month academic year Corporate Partners experiences for deaf students at Gallaudet, providing working data science experience and professional development. RIT/NTID is also building new data science programs; the team of investigators will work closely with RIT during Phase 1, with plans to expand and build on these activities in Phase 2. DEAF PODS has a comprehensive recruiting and supportive mentoring plan for students to participate remotely in all activities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.