Abstract
In this paper, we study the effect of time pressure on player behaviour during a dilemma-based crisis management game. We employ in-game action tracking, physiological sensor data and self-reporting in order to create multi-modal predictive models of player stress responses during a crisis management scenario. We were able to predict the experimental condition (time pressure vs. no time pressure) with 84.5% accuracy, using a game-only feature set. However, lower accuracy was observed when physiological sensor data was used for the same task. The method presented in this paper can be employed in crisis management training, aiming at assessing players’ responses to stressful conditions and manipulating player stress levels to provide personalised training scenarios.
Original language | English |
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Title of host publication | International Conference on the Foundations of Digital Games |
Place of Publication | New York, NY, USA |
Publisher | Association for Computing Machinery |
ISBN (Print) | 9781450388078 |
DOIs | |
Publication status | Published - 2020 |
Event | Foundations of Digital Games 2020 - , Malta Duration: 15 Sep 2020 → 18 Sep 2020 http://fdg2020.org/ |
Publication series
Name | FDG '20 |
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Publisher | Association for Computing Machinery |
Conference
Conference | Foundations of Digital Games 2020 |
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Abbreviated title | FDG 2020 |
Country/Territory | Malta |
Period | 15/09/20 → 18/09/20 |
Internet address |
Keywords
- multi-modal player modeling
- serious games
- crisis management
- Game-based training