Abstract. Flexible and personalized instruction is one of the most important requirements to next generation intelligent educational systems. The intelligence of any e-learning system is thus measured by its ability to sense, aggregate and use, the various contextual elements to characterize the learner, and to react accordingly by providing a set of customized learning services. In this paper we propose a proactive context aware mobile learning system on the Semantic Web. The contribution of this work is a combined model using both a probabilistic learning technique and an ontology-based approach to enable intelligent context processing and management. The system uses a Naïve Bayesian classifier to recognize high level contexts in terms of their constituent atomic context elements. Recognized contexts are then interpreted as triggers of actions yielding a Web service composition. This is achieved by reasoning on the ontological description of atomic context elements participating in the high level context.
In a similar study, Benlamri et al. REF have developed a combined model based on probabilistic learning techniques and ontologies to enable context processing and management in a mobile learning environment.
Proactive Mobile Learning on the Semantic Web
UbiComp 2016
Computer Science