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- W2967701276 abstract "This paper focuses on player 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." @default.
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- W2967701276 date "2019-08-15" @default.
- W2967701276 modified "2023-09-27" @default.
- W2967701276 title "Tracing Player Knowledge in a Parallel Programming Educational Game." @default.
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