103522110 莫展霖 Guided Game-Based Learning Using Fuzzy Cognitive Maps

Xiangfeng Luo, Xiao Wei, and Jun Zhang

Abstract
Fuzzy Cognitive Maps (FCMs) can be used to design game-based learning systems for their excellent ability of concept
representation and reasoning. However, they cannot 1) acquire new knowledge from data and 2) correct false prior knowledge, thus
reducing the game-based learning ability. This paper utilizes Hebbian Learning Rule to solve the first problem and uses Unbalance
Degree to solve the second problem. As a result, an improved FCM gains the ability of self-learning from both data and prior
knowledge. The improved FCM, therefore, is intelligent enough to work as a teacher to guide the study process. Based on the
improved FCM, a novel game-based learning model is proposed, including a teacher submodel, a learner submodel, and a set of
game-based learning mechanisms. The teacher submodel has enough knowledge and intelligence to deduce the answers by the
improved FCM. The learner submodel records students’ study processes. The game-based learning mechanism realizes the guided
game-based learning process with the support of the teacher submodel. A driving training prototype system is presented as a case
study to present a way to realize a real system based on the proposed models. Extensive experimental results justify the model in
terms of the controlling and guiding the study process of the student.

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CONCLUSIONS AND FUTURE WORK
This paper utilizes the Hebbian Learning Rule to improve the
FCM to acquire new knowledge from data itself, and uses the Unbalance Degree to improve the FCM to correct the false prior knowledge automatically...

Via: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5557839