Projects

Insights from motivation science shows 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 authentic 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 about expectancy-value theory using this novel paradigm to investigate specific experiences of personal and communal utility and the association with student motivation. For the broader STEM and social science community, this project develops practical and transferable curriculum models in data science and social science by (1) creating authentic, 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. Projects are expected to yield 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.

An AI acknowledgement statement that I use in classes and in working with nonprofit agencies and social workers and youth workers.

The Social Work Futures Design Initiative developed tools and workshops for participatory collaboration between clients, social workers, agency leaders, and funders to collectively shape the future of practice in human services. We use future scenario planning and other futures design and imagination processes to envision social work futures, then work to make them concrete in the present through design practices that redesign foundational tools, such as intake, assessment, and outcome evaluation. Design Symposiums gather experts in particular areas, such as culturally grounded mental health in the urban midwest, to redesign particular practices, such as client intake. We aim to scale, share, and distribute this pilot-tested Symposium process to other practice and geographic areas.