Guided Game-Based Learning Using Fuzzy Cognitive Maps

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.