In this paper, we present our results related to the definition of a methodology that combines augmented reality (AR) with semantic techniques for the creation of digital stories associated with museum exhibitions. In contrast to traditional AR approaches, we augment real-world elements by supplementing contents of a museum exhibition with additional inputs that provide new and different meanings. In this way we augment a cultural resource with respect to both its presentation and meaning. The methodology is framed in the cultural re-mediation theory and is grounded on a set of ontologies aimed at modelling a cultural resource and correlating it with external multimedia objects and resources. To provide an easy tool for the creation of museum narratives, the methodology makes use of a set of recognised practices widely adopted by museum curators that have been formalised through inference rules. The defined methodology has been experimented in a scenario related to Flemish paintings to validate the augmentation of cultural objects with two different approaches, the first basing on similarities and the second on dissimilarities
In the MOOC environments, the students feel to be alone in the process of choosing courses leading to their learning needs and work objectives. They perceive also to be controllers of their progresses with respect to calendars, fruition, assessment results. Students come into the MOOC environments to develop or enhance professional competences, to earn formative credits and to achieve certifications to get more employment opportunities, but the statistics underline high level of drop-out and few released useful credits and final certifications. These problems are mainly related to the difficulty to guarantee the "teaching presence" in courses with thousands of learners having different background and to the ineffective assessment methods for a meaningful learning process looking at the objectives and giving feedbacks for individual learning paths construction. The work, in particular, exploits the adaptation and personalization features of IWT platform in order to provide ARWE (Adaptive Remedial Work Environment) in order to fill the lack of a one-to-one tutoring mitigating the drop-out problem in MOOCs. The main original contribution of this work concerns the definition of an approach to automatically generate quizzes, exploiting a semantic-based method, in order to populate the e-Testing tool existing in ARWE, decreasing, de facto, the effort for instructors in the assessment authoring phase.
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