Postdoctoral Fellowship: STEMEdIPRF: Defining Latent and Social Characteristics of Active Learning Environments in Biology

Colleges and universities across the country use active learning to enhance student understanding of biology content knowledge. Active learning is both a style of teaching and way of learning that encourages students to engage with their instructors and peers to solve problems, explore ideas, and apply concepts to real-world settings. Studies have shown that active learning is more effective at improving students’ understanding of core concepts across STEM fields when compared to traditional lecture-based instruction. While there has been widespread adoption of active learning, little is known about the specific mechanisms that contribute to its effectiveness. The importance of this project is that it intends to define instructor and student behaviors and interactions across biology active learning classrooms and how these aspects of the course learning environment shape students’ understanding of biology content. Using video, survey, and interview data from a national sample of biology classrooms, the significance of this research is that it could result in practical guidance for instructors on how to support active learning in their classrooms This project goals include the identification of latent behavioral and social characteristics of biology active learning environments and how they impact students’ understanding of core biology concepts. In Aim One, course video data collected from a national sample of large introductory biology courses with various learning instructional models can be used to identify latent profiles of instructor and student behaviors in active learning environments. In Aim Two, social network surveys and concept inventories collected from students enrolled in the biology courses are intended to help describe the structure of classroom social networks across the latent profiles identified in Aim One. In Aim Three, semi-structured interviews conducted with students across varying network positions can lead to understanding how students perceive their roles, the resources exchanged, and the norms shaping student engagement during active learning. Social learning theories, social network analysis, machine learning, and a dual coding approach should lead to practical insights and inform strategies to foster student engagement and improve learning outcomes where active learning is used. An additional goal of the project is to support the PI’s development in advanced STEM education research methods, teaching, and mentoring. The project is funded by the National Science Foundation’s STEM Education Individual Postdoctoral Fellowship Program, which provides funding and professional development support for early-career scholars pursuing postdoctoral training in STEM education. 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.