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Title: Extending a dashboard meta-model to account for users’ characteristics and goals for enhancing personalization
Authors: Vázquez-Ingelmo, A.
García-Peñalvo, F. J.
Therón, R
Conde, M. Á.
Keywords: Information Dashboards
Information Visualization
User Model
Issue Date: 27-Jun-2019
Citation: A. Vázquez-Ingelmo, F. J. García-Peñalvo, R. Therón and M. Á. Conde, "Extending a dashboard meta-model to account for users’ characteristics and goals for enhancing personalization," in Proceedings of LASI-SPAIN 2019. Learning Analytics Summer Institute Spain 2019: Learning Analytics in Higher Education (Vigo, Spain, June 27-28, 2019), M. Caeiro-Rodríguez, Á. Hernández-García and P. J. Muñoz-Merino, Eds. CEUR Workshop Proceedings Series, no. 2415, pp. 35-42, Aachen, Germany:, 2019.
Abstract: Information dashboards are useful tools for exploiting datasets and support decision-making processes. However, these tools are not trivial to design and build. Information dashboards not only involve a set of visualizations and handlers to manage the presented data, but also a set of users that will potentially benefit from the knowledge generated by interacting with the data. It is important to know and understand the requirements of the final users of a dashboard because they will influence the design processes. But several user profiles can be involved, making these processes even more complicated. This paper identifies and discusses why it is essential to include the final users when modeling a dashboard. Through meta-modeling, different characteristics of potential users are structured, thus obtaining a meta-model that dissects not only technical and functional features of a dashboard (from an abstract point of view) but also the different aspects of the final users that will make use of it. By identifying these user characteristics and by arranging them into a meta-model, software engineering paradigms such as model-driven development or software product lines can employ it as an input for generating concrete dashboard products. This approach could be useful for generating Learning Analytics dashboards that take into account the users' motivations, beliefs, and knowledge.
ISSN: 1613-0073
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