The real-time detection of when a player is struggling presents an opportunity for game designers to design timely and meaningful interventions, as well as to provide targeted support that improves student learning and engagement. In this paper, we present a struggle detector in the context of students playing the learning game, Wake: Tales from the Aqualab. Using the interaction log data of the game, we engineered four sets of features that captured distinct aspects of gameplay and trained prediction models to identify human-coded cases of students struggling, cross-validating at the student level. Our best-performing detectors have shown some capability in identifying student struggles with modest performance, at an AUC (Area Under the Curve) value of 0.635. We discuss current limitations of this approach, as well as next steps towards providing real-time support within the game.
Liu, X., Slater, S., Andres, J., Swanson, L., Scianna, J., Gagnon, D., & Baker, R. S. (2023). Struggling to detect struggle in students playing a science exploration game. CHI PLAY Companion '23: Companion Proceedings of the Annual Symposium on Computer-Human Interaction in Play. https://doi.org/10.1145/3573382.3616080