Personalized feedback for self assessment in lifelong learning environments based on semantic web

•We model feedback as part of assessment process in lifelong learning environment.•We provide feedback formal description to provide personalized assessment objects.•We describe an algorithm for feedback personalization process.•We specify semantic models based on IMS/QTI to generate personalized feedback.•We design flexible architecture for feedback personalization based on semantic Web.

Feedback constitutes an important component of assessment in learning environments, as it allows learners to evaluate their progress in the learning process and helps tutors to personalize learning content according to learners' needs and profiles.In this paper we propose an intelligent personalized feedback framework based on Semantic Web technologies, one that provides personalized feedback for self-assessment and that is appropriate for Lifelong Learning environment. The framework takes into consideration the level of complexity of each question in a self assessment test in order to decide on the type of individual feedback required. This process provides accurate information about the learner's level based on the result of their own participation in the assessment. The framework is based on semantic models for user information and feedback generation that ensure interoperability and reuse of Assessment and learning resources.In our approach, a personalized feedback framework based on web services uses the information contained in the learner's profile to proactively assist them by suggesting personalized feedback and helping them overcome their shortcomings in a particular field of knowledge.