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A Motivation Behavior Classification based on Multi Objective Optimization using Learning Vector Quantization for Serious Games
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 57 - Number 14 |
| Year of Publication: 2012 |
| Authors: Moh. Aries Syufagi, Mochammad Hariadi, Mauridhi Hery Purnomo |
10.5120/9183-3604
|
Moh. Aries Syufagi, Mochammad Hariadi, Mauridhi Hery Purnomo . A Motivation Behavior Classification based on Multi Objective Optimization using Learning Vector Quantization for Serious Games. International Journal of Computer Applications. 57, 14 ( November 2012), 23-30. DOI=10.5120/9183-3604
Abstract
The player's motivation a significant role in the success of the learning process and of the game for educational purpose. However, not an easy to determine the level of player's motivation while playing the serious game. To assess the motivation level of player interest, this paper proposes a Motivation Behavior Game (MBG). MBG improves this motivation concept to monitor how players interact with the game. This game employs Learning Vector Quantization (LVQ) for optimizing the motivation behavior input classification of the player. MBG is using teacher's data to obtain the neuron vector of motivation behavior pattern supervise. Three clusters multi objective target will be classified as; active choice, persistence, and mental effort motivation behavior. In the game play experiments employ 33 respondent players demonstrates that 12. 12% of players have high and 6. 06% have semi mental effort, 3. 03% have high and 3. 03% semi persistence, and 66. 67% have high and 9. 09% low active choice motivation behavior. MBG may provide information to game engine when a player needs help or when wanting a formidable challenge. The game engine 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. The high interest players will finish the game if their emotions are stable. The players' interests strongly support the procedural learning in a serious game.
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