Modeling Learning Trajectories with Epistemic Network Analysis: An Investigation of a Novel Analytic Method for Learning Progressions in Epistemic Games

Choi, Y., Rupp, A., Gushta, M., & S. Sweet. (2010). Modeling learning trajectories with epistemic network analysis: An investigation of a novel analytic method for learning progressions in epistemic games. Submitted to National Council on Measurement in Education, Denver, CO.

http://epistemicgames.org/eg/wp-content/uploads/ENA-Simulation-Paper-NCME-Submission.pdf

Epistemic games are designed to help players develop domain-specific expertise that characterizes how professionals in a particular domain reason, communicate, and act (Bagley & Shaffer, 2009; Shaffer 2006b). To analyze the complex data that arise from these games, a novel analytic method grounded in social network analysis called epistemic network analysis (ENA) has been recently proposed (Rupp, Gushta, Mislevy, & Shaffer, 2010; Rupp et al., 2009; Shaffer et al., 2009). In this paper, we introduce the basic ideas of this method and report on the preliminary results of an ongoing research program that investigates whether ENA statistics are sensitive to detecting players’ differential learning trajectories throughout different game structures under different solution strategies. Preliminary results show a complex emerging picture of the conditions under which one ENA statistic can be suitable for this purpose.