How do undergraduate students come to see themselves as researchers? What shapes their experiences, builds their confidence, and helps them navigate challenges? In Research/Research, we use qualitative and mixed methods research, including machine-learning supported analysis, to study student experiences.

An undergraduate student joins a research group unsure if research is right for them. They struggle at first, then start asking questions. They gain confidence, learn new skills, seek guidance from their mentor, develop relationships with their peers, and share their research with others. By the end of their experience, they describe themselves as a “researcher.” How does this happen? What shifted this student from being uncertain about research to identifying as a researcher? Who made an impact on their experience? How do so many other students, with entirely different experiences, come to see themselves the same way?

In Research/Research (“Research on Research”), we center our focus around these questions. Using data from student reflections, assignments, interviews, and focus groups, our team works to understand and contextualize the experiences, identities, skills, influences, and factors that may impact undergraduate student researchers’ experiences.

But that’s not all! Rooted in social sciences, data science, and program evaluation, R/R analyzes student experiences using multi- and mixed-methods approaches. In addition to addressing questions about undergraduate research experiences, a secondary focus of our team is understanding, testing, and proposing best practices for the use of AI and ML in mixed methods and qualitative research methods. R/R team members will develop hands-on skills in study design, qualitative research, and mixed-methods research.