Visualizing Changes in Strategy Use across Attempts via State Diagrams: A Case Study

Abstract

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