Discovering Players' Problem-Solving Behavioral Characteristics in a Puzzle Game through Sequence Mining

Digital games offer promising platforms for assessing student higher-order competencies such as problem-solving. However, processing and analyzing the large volume of interaction log data generated in these platforms to uncover meaningful behavioral patterns remain a complex research challenge. In this study, we employ sequence mining and clustering techniques to examine students' log data in an interactive puzzle game that requires player to change rules to win the game. Our goal is to identify behavioral characteristics associated with the problem-solving practices adopted by individual students. The findings indicate that the most effective problem solvers made fewer rule changes and took longer time to make those changes across both an introductory and a more advanced level of the game. Conversely, rapid rule change actions were linked to ineffective problem-solving. This research underscores the potential of sequence mining and cluster analysis as generalizable methods for understanding student higher-order competencies through log data in digital gaming and learning environments. It also suggests future directions on how to provide just-in-time, in-game feedback to enhance student problem-solving competences.