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Substantial research indicates that active learning methods improve student learning more than traditional lecturing. Accordingly, current studies aim to characterize and evaluate different instructors' implementations of active learning methods. Peer Instruction is one of the most commonly used active learning methods in undergraduate physics instruction and typically involves the use of classroom response systems (e.g., clickers) where instructors pose conceptual questions that students answer individually and/or in collaboration with nearby peers. Several research studies have identified that different instructors vary in the ways they implement Peer Instruction (e.g., the time they give students to answer a question and the time they spend explaining the correct answer); however, these studies only take place at a single institution and do not relate the implementation of Peer Instruction to student learning. In this study, we analyze variation in both the implementation and impacts of Peer Instruction. We use classroom video observations and conceptual inventory data from seven introductory physics instructors across six U.S. institutions. We characterize implementation using the Framework for Interactive Learning in Lectures (FILL+), which classifies classroom activities as interactive (e.g., clicker questions), vicarious interactive (e.g., individual students asking a question), or non-interactive (e.g., instructor lecturing). Our preliminary results suggest that instructors who use both interactive and vicarious interactive strategies may exhibit larger student learning gains than instructors who predominantly use only one of the two strategies. 

Active learning is broadly shown to improve student outcomes as compared with traditional lecture, but more work must be done to distinguish outcomes between different types of active learning. We collected self-reported student social network data at early and late-semester times in a Peer Instruction classroom. The subsequent networks are modeled using exponential random graph models (ERGMs), which are a family of statistical models used with relational data, like social networks. We discuss preliminary findings using this method for a Peer Instruction class. The best-fit ERGM predicts long "chains" of student edges, such as might arise from students talking along rows in the lecture hall. ERGMs appear to be a promising method for quantifying network topology in active learning classrooms.