Modeling Learning Trajectories with Epistemic Network Analysis: A Simulation-based Investigation of a Novel Analytic Method for

Epistemic games are designed to help players develop domain-specific expertise that characterizes how professionals in a particular domain reason, communicate, and act [1, 11]. 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 [8, 9, 12]. In this paper, we report on preliminary results of a comprehensive simulation study that investigates whether ENA statistics are sensitive to 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. 

• Type: Conference
• Year: 2010
• Where: EDM
• Authors: Andre A. Rupp, Shauna J. Sweet, Younyoung Choi
• Keywords: Data Mining, EDM 2010, Ena Statistic, Epistemic Games, Epistemic Network Analysis