A Context-aware Approach for Personalized Mobile Self-Assessment

With the increasing development of mobile technologies, the learning environment is currently undergoing a major shift. Access to contextual information in a mobile learning environment aims to meet the needs of learning and assessment personalization according to various learners' profiles and a range of learning contexts. Semantic Web technologies have been applied in recent years with different purposes in education. But, their applications for generating useful personalized mobile assessment resources have not been researched enough so far. In this paper, an approach making use of semantic Web technologies to support personalized self-assessment in mobile environments is described. Assessment techniques are formalized with First Order Logic rules which allow to personalize assessment activities. We also propose an algorithm for semantic assessment resources retrieval. Finally, a Mobile Semantic Web Assessment Personalization system is presented. The qualitative and quantitative evaluation of the proposed system is also provided.