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Award ID contains: 1711017

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  1. ; ; (, Physical Review Physics Education Research)
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  2. ; ; (, Physical Review Physics Education Research)
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  3. ; ; ; (, Physical Review Physics Education Research)
    null (Ed.)
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  4. ; ; (, 2019 PERC Proceedings)
    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. 
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