Hits : 5
A Cognitive Skill Classification Based On Multi Objective Optimization Using Learning Vector Quantization for Serious Games
Moh. Aries Syufagi 1,2, Mochamad Hariadi 3 & Mauridhi Hery P.3
1 Multimedia Studies Program, Public Vocational High School 1 Bangil Pasuruan
2Department of Information, STMIK Yadika Bangil,
3Electrical Department, Faculty of Industrial Technology ITS Surabaya
Email: moh.aries.syufagi@gmail.com, mochar@ee.its.ac.id, hery@ee.its.ac.id
Abstract. Nowadays, serious games and game technology are poised to transform the way of educating and training students at all levels. However, pedagogical value in games do not help novice students learn, too many memorizing and reduce learning process due to no information of player’s ability. 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 is using teacher’s data to obtain the neuron vector of cognitive skill pattern supervise. Three clusters multi objective target will be classified as; trial and error, carefully and, expert cognitive skill. In the game play experiments using 33 respondent players demonstrates that 61% of players have high trial and error cognitive skill, 21% have high carefully cognitive skill, and 18% have high expert cognitive skill. CSG 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. 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.
Keywords: cognitive skill classification; multi objective; learning vector quantization; serious game.
 Download Article
|
