PERC 2019 Abstract Detail Page
Previous Page | New Search | Browse All
| Abstract Title: | Modelling Student Collaborations Using Valued ERGMs |
|---|---|
| Abstract: | Network analytic techniques are particularly well suited to studying how students form groups, since interactions between people are not independent events. Collaboration between students during a calculus-based, introductory physics course at a liberal arts college is described using networks. Students are represented by nodes, which are connected by edges, representing interactions between pairs of students. Both the nodes and the edges are associated with various covariates representing the characteristics of the student and the intensity of their collaboration. Exponential family random graph models (ERGMs), a network analytic technique analogous to logistic regression, are used to estimate the probability of the existence of a particular edge, based on the various covariates and the overall structure of the network. An extension to ERGMs, valued ERGMs, model the strength of the ties instead of their existence. Considering the strength of ties is crucial for measuring subtle effects that affect student collaborations. |
| Abstract Type: | Contributed Poster Presentation |
| Session Time: | Poster Session II |
| Poster Number: | B71 |
| Contributed Paper Record: | Contributed Paper Information |
| Contributed Paper Download: | Download Contributed Paper |
Author/Organizer Information | |
| Primary Contact: |
James E Wells University of Connecticut |
