An affordance of alternate reality games (ARGs) is that players play as if they were in the game world themselves. Human game-runners (proxy players) interact with participants as characters within the game’s fiction, guiding and modeling gameplay. In this paper we employ a method of analyzing gameplay called epistemic network analysis (ENA), which creates relational network graphs between actions within a game space. We found that key players exhibited behavior like proxy players, but also diverged from them in meaningful ways. We present case studies of 1 active player and 1 proxy player that demonstrate the power of ENA to model ARG play. We describe ways in which ENA reinforced the design insights that guided our original creation of proxy players while also allowing us to analyze the implications of those design choices in practice. We conclude by enumerating some research and design benefits of employing ENA in other learning contexts.
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