Cohort Themes

Tree, viewed from the ground up, with ten human hands gripping bark from either side. Foliage and sky visible but blurred in background.
Unsplash image courtesy of Shane Rounce.

The STEM Pedagogy Institute will have a significant research component, to be elaborated over time by our team, our partners (including the Center for Advanced Study in Education), and in dialogue with the STEM Pedagogy Fellows.

We have identified three core areas of pedagogical and curricular focus: Community ScienceComputational Methods, and Early Research Immersion. Building interdisciplinary cohorts around these foci will allow SPFs to explore, develop, refine, implement, and share evidence-based instructional practices that have particular utility at CUNY for helping undergraduates establish foundations in STEM, and persist in their engagement.

Community Science

Community science is defined as community-engaged scientific inquiry to address community-driven questions. Its participatory approach is characterized by contextual knowledge responsive to local concerns, collaborative learning, civic engagement, and democratization of scientific knowledge through increasing access. It allows for developing equitable communities for everyone and motivating underserved learners to participate in science. Integration of community science into teaching helps learners make local connections, develop professional identity, and practice science communication skills.

Computational Methods in the Sciences

Computational methods utilize problem-solving, critical thinking, and mathematical models to understand complex patterns of behaviors. Incorporating computational methods such as data mining, performance modeling, and data visualization into classrooms across the curriculum improves the conceptual understanding of disciplinary ideas and core practices. As recent federal reports emphasize the importance of developing a workforce with an understanding of basic computational methods, engaging students from disciplines beyond computer science and mathematics in computational learning can help them learn how to curate, manage, and model large datasets and communicate their work effectively.

Early Research Immersion

Undergraduate course-based research experiences increase student interest and retention in STEM through immersion in authentic scientific research and careful mentorship. This method can improve critical thinking, sense of belonging, recognition in the community, and technical skills that have implications beyond the classroom. In addition, responsive research training environments promote career readiness, project management and leadership skills, peer collaboration, and increased pursuit of STEM-related careers.