HNDS-R: Advancing motivational science through undergraduate labor force capacity building and community data science infrastructure

Insights from motivation science show that undergraduate students who attend university proximate to family and other support structures are more likely to remain in their local communities post-graduation. This project strengthens competitiveness for future employment within the local communities through analyses of motivational place-based datasets, human-centered case studies, and direct networking opportunities with local data science professionals. This project improves undergraduate training opportunities with datasets suitable for analyses using artificial intelligence and machine learning algorithms. Using a cohort-based approach, 120 students (60 data science students and 60 social work students) will participate in data science projects developed with a community partner. In addition, the project develops translational models for curricula and skill development that are transferable to other local contexts. This project tests the novel hypothesis that engagement with community-based datasets and cross-disciplinary methodological approaches simultaneously improve student learning outcomes, persistence to degree completion, and initial career trajectory. The project advances knowledge to investigate specific experiences of personal and communal utility and the association with student motivation. For the broader STEM community, this project develops practical and transferable curriculum models in data science by (1) creating multi-layered, place-based datasets amenable to evaluation using artificial intelligence and machine learning algorithms and (2) increasing student competency in fundamental data science tasks such as data curation and evaluation. The project yields novel insights and actions that have transferable impact to local communities served by the community partner. Broader impacts include implications for the refinement of processes for conducting community-based data analyses. 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.