Semantic Social Sensing for Improving Simulation Environments for Learning

Abstract

The rapidly growing learning simulations market calls urgently for innovative ways to facilitate the simulation design process [1],[2]. Social spaces can provide an extensive source of reports on individuals’ experiences and their real-world contexts that may be exploited for the purpose of identifying relevant content and evaluating the quality of a simulation. To realise this potential, appropriate ways to make sense of user generated content (UGC) are needed. This work presents a novel approach, called semantic social sensing (SSS), which exploits ontologies and semantic augmentation combined with discourse analysis uncovering intentions behind the user comments. We have developed two SSS instruments enabling analysis of UGC – (a) a framework for automatic semantic analysis for capturing viewpoints (ViewS), which utilises ontologies and semantic tagging and enrichment and enables visual exploration of the conceptual spaces associated with UGC [3]; and (b) a schema for discourse analysis to identify intentions useful for simulator design [2] and inspired by research analysing communicative functions of user contributions in collaborative settings [4].