A Context-aware Approach for Adapting Scientific E-Learning Objects

Abstract

E-learning has arisen as a promising solution to lifelong learning and job training. It changed the way people learn and gain knowledge. Moreover, it has been embedded in many information systems as user guides to help and support them in their workplaces. Yet, these guides have been designed in a one-size-fits-all fashion that serves generic learners irrespective of their individual contexts. The developers of Scientific Data Management Systems (SDMSs) provide all scientists with a textual guide regardless of their contexts such as learning styles and expertise levels. In this paper, we propose a proactive context-aware approach for recommending personalized scientific data management e-learning objects. The proposed approach improves the learning process of the scientific data management depending on the scientist profile and his or her environmental context. The proposed approach includes two phases. First, the approach semantically detects the scientist’s context. Secondly, a hybrid recommendation technique is introduced to provide the scientist with an adapted list of learning objects. To create a generic approach, the web service-based methodology is exploited to facilitate the integration with any SDMS.