Modeling User Exploration and Boundary Testing in Digital Learning Games

Digital games can be potent problem solving environments which afford discovery learning through thoughtful exploration [1, 2]. As such, game microworlds facilitate self-regulated learning through sandbox elements in which students have agency in individualizing their pathways of interaction [3]. These agency-driven environments can support learning via individual discovery of problem space constraints and solutions, particularly through boundary testing and productive failure [cf. 4]. Thus, modeling of user interaction in digital learning games can provide considerable insight into emergent trajectories of discovery-based progression, in which equally engaged players may interact differently with the system. To this end, this research leverages educational data mining (EDM) [5] to investigate organic player trajectories of thoughtful exploration (around boundary testing and productive failure) in a learning gamespace. We align behavioral coding with log file data to automatically detect sequences of thoughtful exploration (TE) in play. Results include a robust predictive model of event-stream TE, with multiple trajectories of emergent student behavior-offering insight into organic learning pathways through the game-based problem space, and informing iterative design in optimization of user experience and student engagement.