A method for automatic situation recognition in collaborative multiplayer Serious Games
Key: WBH+15-1
Author: Viktor Wendel, Marc-André Bär, Robert Hahn, Benedict Jahn, Max Mehltretter, Stefan Göbel, Ralf Steinmetz
Date: July 2015
Kind: @article
Keywords: Serious Games, Collaborative Learning, Game Mastering, Adaptation
Abstract: During recent years, game-based learning as one aspect of Serious Games has been established as a promising alternative and addition to traditional learning concepts. Both in learning and in gaming, learners respectively players differ in various traits. In learning this might be previous knowledge, learning style or speed. In gaming, players differ in various gaming preferences (genres, play style, etc.) but also in their skills regarding a specific game. Hence, one field of research within the field of Serious Games is the problem of adaptation of game-based learning environments towards the needs of heterogeneous players. Adaptation can thereby be performed algorithmically or by a human instructor. Both for the instructor but also for an algorithmical adaptation mechanism it is vital to have knowledge about the course of the game in order to be able recognize player intentions, potential problems or misunderstandings, both of the game(play) and the lerning content. The main contribution of this paper is a mechanism to recognize high-level situations in a multiplayer Serious Game. The approach presented contains a concept for defining criteria and situations based on the gamestate, player actions and events. It then calculates how likely it is that players are in a certain situation. The gathered information can then be used to feed an adaptation algorithm or be presented to the instructor to improve instructor decision making. The concept is implemented on top of an existing collaborative multiplayer Serious Game. In a first evaluation, the situation recognition was able to correctly recognize more than 80% of the situations in a set of game sessions.
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