Modeling serious games based on Cognitive Skill classification using Learning Vector Quantization with Petri net

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 of serous game. Mastery learning is the core of the learning process in serious game. Mastery learning can be achieved by always maintaining a high interest. Indirectly, CSG always observe fluctuations in interest of the players. To asses the cognitive level of player ability, we propose a Cognitive Skill Game (CSG). CSG improves this cognitive concept to monitor how players interact with the game. This game employs Learning Vector Quantization (LVQ) for optimizing the cognitive skill input classification of the player. CSG may provide information when a player needs help or when wanting a formidable challenge. The game will provide the appropriate tasks according to players´ ability. CSG 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 player is emotionally stable. Interests in the players strongly support the procedural learning in a serious game.