Game log data have great potential to provide actionable information about the in-game behavior of players. However, these lowlevel\nbehavioral data are notoriously difficult to analyze due to the challenges associated with extracting meaning from sparse data\nstored at such a small grain size. This paper describes a three-step solution that uses cluster analysis to determine which strategies\nplayers use to solve levels in the game, sequence mining to identify changes in strategy across multiple attempts at the same level,\nand state transition diagrams to visualize the strategy sequences identified by the sequence mining. In the educational video game\nused in this case study, cluster analysis successfully identified 15 different in-game strategies.Thesequence mining found an average\nof 40 different sequences of strategy use per level, which the state transition diagrams successfully displayed in an interpretable way.
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