8 research outputs found

    Video Augmentation in Education: in-context support for learners through prerequisite graphs

    Get PDF
    The field of education is experiencing a massive digitisation process that has been ongoing for the past decade. The role played by distance learning and Video-Based Learning, which is even more reinforced by the pandemic crisis, has become an established reality. However, the typical features of video consumption, such as sequential viewing and viewing time proportional to duration, often lead to sub-optimal conditions for the use of video lessons in the process of acquisition, retrieval and consolidation of learning contents. Video augmentation can prove to be an effective support to learners, allowing a more flexible exploration of contents, a better understanding of concepts and relationships between concepts and an optimization of time required for video consumption at different stages of the learning process. This thesis focuses therefore on the study of methods for: 1) enhancing video capabilities through video augmentation features; 2) extracting concept and relationships from video materials; 3) developing intelligent user interfaces based on the knowledge extracted. The main research goal is to understand to what extent video augmentation can improve the learning experience. This research goal inspired the design of EDURELL Framework, within which two applications were developed to enable the testing of augmented methods and their provision. The novelty of this work lies in using the knowledge within the video, without exploiting external materials, to exploit its educational potential. The enhancement of the user interface takes place through various support features among which in particular a map that progressively highlights the prerequisite relationships between the concepts as they are explained, i.e., following the advancement of the video. The proposed approach has been designed following a user-centered iterative approach and the results in terms of effect and impact on video comprehension and learning experience make a contribution to the research in this field

    Archivi digitali e Archeologia: il data-model per il MOD (MappaOpenData)

    No full text
    Nell'elaborato si propone una analisi delle best practices applicate al mondo della digitalizzazione dei dati archeologici, archivistica e divulgazione. Nel caso di studio è stato proposto un modello-dati che supporti il MOD (MappaOpenData) nato in seno al progetto Mappa, finanziato dalla Regione Toscana, realizzato dall'Università di Pis

    Integrating Terminological Tools and Semantic Archaeological Information: the ICCD RA Schema and Thesaurus.

    No full text
    This paper describes the process of mapping, translation and publication in SKOS format of the RA Thesaurus, a terminological tool developed by the Italian Ministry of Cultural Heritage (MiBACT) as a part of the official documentation used for the recording of archaeological finds. In particular, the RA Thesaurus is intended to provide unified and meaningful terminology for the description of archaeological objects according to the MiBACT official cataloguing standards. After describing the thesaurus, the logic with which it was developed and its internal structure, we report the various phases of the conversion, both from a theoretical and implementation point of view, and the various technologies used for the publication of the thesaurus on the web. This work is a collaborative effort between PIN and MiBACT carried out under the ARIADNE project

    A multi-element psychosocial intervention for early psychosis (GET UP PIANO TRIAL) conducted in a catchment area of 10 million inhabitants: study protocol for a pragmatic cluster randomized controlled trial

    No full text
    Multi-element interventions for first-episode psychosis (FEP) are promising, but have mostly been conducted in non-epidemiologically representative samples, thereby raising the risk of underestimating the complexities involved in treating FEP in 'real-world' services
    corecore