Automatic extraction of smart game based learning design expertise: An approach based on learning ontology

Abstract

Smart Game Based Learning System (SGBLS) design is a complex area. It requires the intervention of multiple actors with specific skills and expertise. Unfortunately, novice game designers who do not have necessary competency inspired from both educational and video games systems cannot successfully create SGBLS. For that, they have to acquire specific skills and expertise in an efficient and active pedagogical manner. To solve this problem, there are several existing techniques, among them learning ontology based on semantic annotation. This technique seems promising as it encourage individual and companies to focus on maintaining and enhancing their knowledge asset by facilitating the knowledge extraction, elicitation and representation processes. The main goals of this article are to (a) extract and represent knowledge related to SGBLSs design and (b) render possible accessibility and transfer of that knowledge to novice actors and further to meet aforementioned challenges.