Abstract
With the increasing reach of digital games, it is beyond doubt that the gaming experience should be pleasurable while at the same time appropriately challenging. In this context, the Dynamic Difficulty Adjustment (DDA) technique is used to adapt the difficulty level as a function of the player’s ability. In this work, electrodermal activity (EDA) data were used to infer the arousal levels and affective states of each player in order to use them as input in the comparison of two DDA algorithms: Data Subset Analysis (DSA) and Real-Time Arousal Set (RTA). A blind experiment was conducted with 60 participants, implementing these algorithms within the game Asteroids: in the 2nd and 1/2th Dimension and collecting data through game metrics, algorithm adjustments, and through questionnaires regarding the participants’ perception of the experiments, such as game difficulty and their joy while playing the game. Our findings indicated that the DSA algorithm could detect the player’s excitement level more adequately when compared to the RTA algorithm. This allows for finer adjustments to the game’s difficulty, creating a more enjoyable experience for players.
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Nery Bandeira, I. et al. (2022). Dynamic Difficulty Adjustment in Digital Games: Comparative Study Between Two Algorithms Using Electrodermal Activity Data. In: Fang, X. (eds) HCI in Games. HCII 2022. Lecture Notes in Computer Science, vol 13334. Springer, Cham. https://doi.org/10.1007/978-3-031-05637-6_5
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