Understanding Users' Interaction Behavior with an Intelligent Educational Game: Prime Climb

This paper presents work on applying clustering and association rule mining techniques to mine users’ behavior in interacting with an intelligent educational game, Prime Climb. Through such behavior discovery, frequent patterns of interaction which characterize different groups of students with similar interaction styles are identified. The relation between the extracted patterns and the average domain knowledge of students in each group is investigated. The results show that the students with significantly higher prior knowledge about the domain behave differently from those with lower prior knowledge as they play the game and that pattern could be identified early during the interactions.