Visualization of user-learning game interaction unveiling learner's learning patterns

Many existing games collect various data from their users and some of the data are in the form of event logs. Event logs of games are potential resources to obtain empirical data since they capture the real world of how games are used by players. Using the event logs, we can discover the behavioral models of how players interact with games. Specifically for learning games, their event logs can be used to discover the learning patterns of learners. From the use of our sorting algorithm learning artifact, an event log is collected and used to construct a player's activity graph during his or her interaction with the game. To identify the learner's learning patterns, the graph is visualized using Harel-Koren Fast Multiscale and Horizontal Sine layouts. Some visual spatial attributes of nodes and edges that construct the graph are used to convey several information about the course of events resulted from the interaction between the learner and the artifact. Based on the visualization, this research has successfully identified ten interaction patterns, in which most of them exhibit that the learner whose activities are logged tends to perform learning based on the experiential learning rather than the didactic learning. The learner prefers exerting “trial and error” approach to build an association rule in order to predict the next correct steps in executing the sorting algorithm. The identified interaction patterns will be studied further into formalization and the construction of algorithms to identify the learning patterns in the real-world.