A pilot study on the feasibility of dynamic difficulty adjustment in game-based learning using heart-rate

Abstract

Personalization and adaption have become crucial in game-based learning to optimize user experience and performance. Recent advances in sensor technology allow for acquiring physiological data of players which, in turn, allows for acquiring more fine-grained information on internal changes of the player than conventional user interaction data. Therefore, the current pilot study assessed the feasibility of dynamic difficulty adjustment (DDA) in a digital game-based emergency personnel training using heart rate data. In particular, the game became harder/easier when learners heart rate fell below/exceeded predefined thresholds based on leaners individual baseline heart rate. For the adaptive version of the game, we observed that heart rate changes indeed triggered the game to become easier/more difficult. This also altered completion rates as compared to a non-adaptive version of the same game. Moreover, players reported the adaptive version to be more challenging, fascinating, and harder than the non-adaptive one. At the same time players felt that the difficulty in the adaptive version was just right, while the non-adaptive one was rated to be harder than just right. In sum, DDA using heart rate seems feasible. However, future studies need to determine effects on performance and user experience of such adjustments in more detail and different contexts.