No abstract

Playing style recognition is crucially important for style-based adaptation of digital games. Unlike traditional ways for measuring of styles by means of self-reports, automatic style estimation incorporated into a video game appears to be a more efficient and ecologically valid method. The article presents a model for in-game recognition of four playing styles (Competitor, Dreamer, Logician, and Strategist) based on the Kolb's experiential learning theory. The model applies multiple linear regression over task performance metrics as explanatory variables and coefficients found first by a heuristic approach relaying on experience and observation knowledge of domain experts and, next, estimated by the least squares method. Experiments with the model implemented within an affectively adaptive video game demonstrated the benefits of emotion-based dynamic difficulty adjustment over playing outcomes and proved its validity as an accurate instrument for automatic estimation of both the four playing styles and the learning styles of Honey and Mumford.

No abstract

Purpose 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. Design/methodology/approach 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. Findings 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. Research limitations/implications 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. Originality/value The paper presents an original research on the effect of affect-based adaptation on a novel approach for implicit recognition of playing styles.

No abstract