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.

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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.

Content dissemination in peer-to-peer mobile ad-hoc networks is subject to disruptions due to erratic link performance and intermittent connectivity. Distributed protocols such as BitTorrent are now ubiquitously used for content dissemination in wired Internet-scale networks, but are not infrastructure-less, which makes them unsuitable for MANETs. Our approach (called SISTO) is a fully distributed and torrent-based solution, with four key features: (i) freedom from any reliance on infrastructure; (ii) network and topology aware selection of information sources; (iii) robust multiple-path routing of content via a proactive peer selection technique; (iv) an integrated distributed content discovery capability, not found in other torrent systems. We have implemented SISTO in software, and evaluated its performance using emulation and realistic mobile network models derived from field measurements. We have measured significant improvements in download latency, resiliency and packet delivery compared to traditional data delivery models and conventional BitTorrent. We have implemented SISTO on both Linux and Android platforms, and integrated it with several android applications for content sharing.

This paper defines a learning algorithm for plan grammars used for plan recognition. The algorithm learns Combinatory Categorial Grammars (CCGs) that capture the structure of plans from a set of successful plan execution traces paired with the goal of the actions. This work is motivated by past work on CCG learning algorithms for natural language processing, and is evaluated on five well know planning domains.