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Abstract
Discovering and predicting a gamer's behavior and adapting the game environment to improve the learning is a challenging task in any game-based learning environment. QuaSim is a gamified intelligent tutoring system (ITS) developed to teach quantum cryptography. In QuaSim, students solve problems related to quantum cryptography through different lessons/game plans. In this paper, we provide an overview of QuaSim, and our approach to analyzing students' performance and gameplay behavior based on activity sequence modelling and clustering. We present the results of our analysis and identify different student groups having distinct gaming patterns and problem-solving behaviors. Finally, we discuss the pre- and post-game survey results.
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