Tracing Player Knowledge in a Parallel Programming Educational Game
  This paper focuses on "tracing player knowledge" in educational games.
Specifically, given a set of concepts or skills required to master a game, the
goal is to estimate the likelihood with which the current player has mastery of
each of those concepts or skills. The main contribution of the paper is an
approach that integrates machine learning and domain knowledge rules to find
when the player applied a certain skill and either succeeded or failed. This is
then given as input to a standard knowledge tracing module (such as those from
Intelligent Tutoring Systems) to perform knowledge tracing. We evaluate our
approach in the context of an educational game called "Parallel" to teach
parallel and concurrent programming with data collected from real users,
showing our approach can predict students skills with a low mean-squared error.
