Semantic retrieval: multiple response model for context-aware learning services

With the consistent adoption of pervasive computing thoroughly integrated into our daily learning activities, seamless learning services can be provided for learners corresponding to their needs at any time and any place. The adapted learning service is delivered to learners based on learning situations and learning response/feedback, which are success and failure in practice. But because the learning needs are changing under available rich contexts, learning services generally make the learners 'cognition overloads' and 'disoriented' under context-aware ubiquitous learning environments. In order to deliver the most suitable learning service, this paper proposes a multiple response approach to realise context-aware learning services under pervasive learning environments. Based on six learning statuses from sharable content object reference model (SCORM), six responses of learning feedback are used to reward or penalise the preferred learning service according to the learning context. Experimental results show that the proposed methods perform well in practice and the prototype system can successfully deliver the learning services adapted to the learning contextual situations of learners.