This paper aims to clarify how affect-based adaptation can improve implicit recognition of playing style of individuals during game sessions. This study presents the “Rush for Gold” game using dynamic difficulty adjustment of tasks based on both player performance and affectation inferred through electrodermal activity and facial expressions of the player. The game applies linear regression for calculating playing styles to be applied for achieving a style-based adaptation in other educational video games.
The experimental procedure included subject selection, demonstration, informed consent procedure, two game sessions in random order – one without and another with affective adaptation control – and post-game self-report. The experiment was conducted with participation of 30 master students and university lecturers in informatics.
This study presents experimental results concerning the impact of affective adaptation over playing style recognition, game session time, task’s effectiveness, efficiency and difficulty and, as well, player’s assessment of affectively adaptive gameplay obtained by an adaptation control panel embedded into the game and by post-game self-report.
The proposed adaptive game limits recognised styles to such based on the Kolb’s Learning Style Inventory model. Another limitation of the study is the relatively small number of participants constrained by the extended experimental procedure and the desktop game version.
The paper presents an original research on the effect of affect-based adaptation on a novel approach for implicit recognition of playing styles.
Bontchev, B. and Vassileva, D. (2017), "Affect-based adaptation of an applied video game for educational purposes", Interactive Technology and Smart Education, Vol. 14 No. 1, pp. 31-49. https://doi.org/10.1108/ITSE-07-2016-0023Download as .RIS
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