Alternate reality games (ARGs) are powerful learning environments due to the way that they inspire collaboration and bring a participant’s day-to-day life into play. An important aspect of educational ARGs is that learning is social, with players sharing information and resources across a network of other players. In the following poster we provide analysis of gameplay data from a large-scale ARG, DUST, centered on science learning. We examine the metric of eigenvector centrality (EC) as a way of predicting meaningful learning networks in ARG play, expand upon that finding with a qualitative case study of a highly involved player, and offer the possibility of EC monitoring during gameplay as a way of improving player outcomes in future ARGs.
Publisher
iSchools
Series/Report Name or Number
IConference 2016 Proceedings
Type of Resource
text
Language
eng
Permalink
http://hdl.handle.net/2142/89395
DOI
https://doi.org/10.9776/16535
Copyright and License Information
Copyright 2016 is held by the authors. Copyright permissions, when appropriate, must be obtained directly from the authors.
Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)
IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.
