%0 Journal Article
%T Petri Net Model for Serious Games Based on Motivation Behavior Classification
%A Moh. Aries Syufagi
%A Mochamad Hariadi
%A Mauridhi Hery Purnomo
%J International Journal of Computer Games Technology
%D 2013
%I Hindawi Publishing Corporation
%R 10.1155/2013/851287
%X Petri nets are graphical and mathematical tool for modeling, analyzing, and designing discrete event applicable to many systems. They can be applied to game design too, especially to design serous game. This paper describes an alternative approach to the modeling of serious game systems and classification of motivation behavior with Petri nets. To assess the motivation level of player ability, this research aims at Motivation Behavior Game (MBG). MBG improves this motivation concept to monitor how players interact with the game. This modeling employs Learning Vector Quantization (LVQ) for optimizing the motivation behavior input classification of the player. MBG may provide information when a player needs help or when he wants a formidable challenge. The game will provide the appropriate tasks according to players¡¯ ability. MBG will help balance the emotions of players, so players do not get bored and frustrated. Players have a high interest to finish the game if the players are emotionally stable. Interest of the players strongly supports the procedural learning in a serious game. 1. Introduction Nowadays, serious games and game technology are poised to transform the way of educating and training students at all levels. From the previous research about serious game, it is known that serious game supports the education process. Marsh et al. [1] and Clark [2] state that serious game is learning through games which contain pedagogical aspects and is a part of e-learning tools/media [3¨C5]. Clark [2], Arnseth [6], and Smith [7] further state that learning method using game is better than the conventional one, since animations of learning material in game activate students¡¯ long-term memories. On the other hand, game learning has an inverse relationship with learning test in many instances. Clark [8] gives details; pedagogy in games is often based on unguided discovery such as; minimal guidance for high skill works, overwhelming discovery evidence without any assistance for beginners/novice learners [9, 10], discovery technique design, and some game cause memory overwork and decrease the learning process [11]. Overload will not occur if the level of players¡¯ motivation behavior is controlled. Inal and Cagiltay [12] explain the research of Csikszentmihalyi and emphasized the balance between an individual¡¯s skills and difficulties of tasks. He theorizes that the occurrence of flow experiences depends on this balance and that if the balance does not exist between the individual¡¯s skills and the task, flow experiences cannot occur. Heavier duty resulted in
%U http://www.hindawi.com/journals/ijcgt/2013/851287/