Tracing Player Knowledge in a Parallel Programming Educational Game
This addresses the challenge of knowledge tracing in educational games for teaching parallel programming, but it is incremental as it builds on existing knowledge tracing methods.
The paper tackles the problem of estimating player knowledge in educational games by integrating machine learning with domain rules to detect skill application success/failure, then feeding this to a standard knowledge tracing module; evaluation on a parallel programming game with real user data shows it can predict student skills with low mean-squared error.
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.