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Permanent URI for this collectionhttps://repositorio.grial.eu/handle/123456789/34
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Item A proposal to measure the understanding of data visualization elements in visual analytics applications(CEUR-WS.org, 2022-10-09) Vázquez-Ingelmo, A.; García-Peñalvo, F. J.; Therón, R.; Byrd, V.; Camba, J. D.Data visualizations and information dashboards are useful but complex tools. They must be fully understood to draw proper insights and to avoid misleading conclusions. However, several elements and factors are involved in this domain, which makes it difficult to learn. In previous works, we proposed a meta-model to capture the primitive elements that compose visualizations and dashboards. This meta-model has served as a framework for conducting data visualization research, but also to develop a graphical tool for generating data visualizations and dashboards. This tool (namely MetaViz) enables users to create data visualizations through fine-grained components based on the entities represented in the meta-model. The main goal of the system is to provide a learning experience in which users can freely add and configure elements to understand how they influence the final display. This work describes work in progress to validate the pedagogical value of MetaViz in terms of the understanding of data visualization concepts.Item Content-validation questionnaire of a meta-model to ease the learning of data visualization concepts(CEUR-WS.org, 2022-10-09) Vázquez-Ingelmo, A.; García-Holgado, A.; García-Peñalvo, F. J.; Therón, R.; Colomo-Palacios, R.Data visualizations and dashboards are powerful means to convey information to large audiences. However, the design and understanding of these tools are not straightforward because several factors are involved. It is essential to rely on theoretical frameworks to design and implement data visualizations for these reasons. In this context, we propose a meta-model to identify and arrange the main characteristics and elements of data visualizations and dashboards. The proposed meta-model provides a powerful artifact to generate information visualizations and dashboards automatically, but also a learning resource to understand how data visualizations elements interact and influence each other. However, it is necessary to validate this artifact to ensure its quality and usefulness. In this paper, we present a work-in-progress or a quality assessment and content validation of the me-ta-model to seek weaknesses and tackle them in subsequent iterations.Item Advances in the use of domain engineering to support feature identification and generation of information visualizations(ACM, 2020-10-21) Vázquez-Ingelmo, A.; García-Peñalvo, F. J.; Therón, R.Information visualization tools are widely used to better understand large and complex datasets. However, to make the most out of them, it is necessary to rely on proper designs that consider not only the data to be displayed, but also the audience and the context. There are tools that already allow users to configure their displays without requiring programming skills, but this research project aims at exploring the automatic generation of information visualizations and dashboards in order to avoid the configuration process, and select the most suitable features of these tools taking into account their contexts. To address this problem, a domain engineering, and machine learning approach is proposed.Item Specifying information dashboards’ interactive features through meta-model instantiation(CEUR-WS.org, 2020-09-19) Vázquez-Ingelmo, A.; García-Peñalvo, F. J.; Therón, R.; García-Holgado, A.Information dashboards1 can be leveraged to make informed decisions with the goal of improving policies, processes, and results in different contexts. However, the design process of these tools can be convoluted, given the variety of profiles that can be involved in decision-making processes. The educative context is one of the contexts that can benefit from the use of information dashboards, but given the diversity of actors within this area (teachers, managers, students, researchers, etc.), it is necessary to take into account different factors to deliver useful and effective tools. This work describes an approach to generate information dashboards with interactivity capabilities in different contexts through meta-modeling. Having the possibility of specifying interaction patterns within the generative workflow makes the personalization process more fine-grained, allowing to match very specific requirements from the user. An example of application within the context of Learning Analytics is presented to demonstrate the viability of this approach.Item Automatic generation of software interfaces for supporting decision-making processes. An application of domain engineering and machine learning(ACM, 2019-10-16) Vázquez-Ingelmo, A.; García-Peñalvo, F. J.; Therón, R.Information dashboards are sophisticated tools. Although they enable users to reach useful insights and support their decision-making challenges, a good design process is essential to obtain powerful tools. Users need to be part of these design processes, as they will be the consumers of the information displayed. But users are very diverse and can have different goals, beliefs, preferences, etc., and creating a new dashboard for each potential user is not viable. There exist several tools that allow users to configure their displays without requiring programming skills. However, users might not exactly know what they want to visualize or explore, also becoming the configuration process a tedious task. This research project aims to explore the automatic generation of user interfaces for supporting these decision-making processes. To tackle these challenges, a domain engineering, and machine learning approach is taken. The main goal is to automatize the design process of dashboards by learning from the context, including the end-users and the target data to be displayed.Item Capturing high-level requirements of information dashboards’ components through meta-modeling(ACM, 2019-10-16) Vázquez-Ingelmo, A.; García-Peñalvo, F. J.; Therón, R.Information dashboards are increasing their sophistication to match new necessities and adapt to the high quantities of generated data nowadays. These tools support visual analysis, knowledge generation, and thus, are crucial systems to assist decision-making processes. However, the design and development processes are complex, because several perspectives and components can be involved. Tailoring capabilities are focused on providing individualized dashboards without affecting the time-to-market through the decrease of the development processes' time. Among the methods used to configure these tools, the software product lines paradigm and model-driven development can be found. These paradigms benefit from the study of the target domain and the abstraction of features, obtaining high-level models that can be instantiated into concrete models. This paper presents a dashboard meta-model that aims to be applicable to any dashboard. Through domain engineering, different features of these tools are identified and arranged into abstract structures and relationships to gain a better understanding of the domain. The goal of the meta-model is to obtain a framework for instantiating any dashboard to adapt them to different contexts and user profiles. One of the contexts in which dashboards are gaining relevance is Learning Analytics, as learning dashboards are powerful tools for assisting teachers and students in their learning activities. To illustrate the instantiation process of the presented meta-model, a small example within this relevant context (Learning Analytics) is also provided.Item Extending a dashboard meta-model to account for users’ characteristics and goals for enhancing personalization(CEUR-WS.org, 2019-06-27) Vázquez-Ingelmo, A.; García-Peñalvo, F. J.; Therón, R; Conde, M. Á.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.Item Generation of Customized Dashboards Through Software Product Line Paradigms to Analyse University Employment and Employability Data(CEUR-WS.org, 2018-08-31) Vázquez-Ingelmo, A.; García-Peñalvo, F. J.; Therón, R.University employment and, specifically, employability has steadily gained relevance nowadays as the study of these fields can lead to improvement in the quality of life of individual citizens. However, the empirical research is still insufficient to make significant decisions within this domain. It is necessary to rely on powerful tools in order to reach insights about university employment and employability. Information dashboards have become a key software tool to reach insights and make informed decisions about a specific topic, domain or field of study. Nevertheless, dashboards’ users can have several requirements that differ from each other (including displayed information itself, design features or even functionalities), and it is necessary to take into account all of these specifications, allowing users to exploit data with its own necessities and aiming to its own goals. Applying software product line paradigms, it is plausible to face different requirements regarding information dashboards’ development in an efficient, scalable and maintainable way. To validate this approach, a case study is presented in the context of the Spanish Observatory of Employability and University Employment, an organization that aims to become an information reference for these fields.Item Application of domain engineering to generate customized information dashboards(Springer, 2018-07-18) Vázquez-Ingelmo, A.; García-Peñalvo, F. J.; Therón, R.Information dashboards play a key role in analyzing and visualizing data about a specific topic or domain. In essence, these dashboards display in-formation and enable users to reach insights and make informed decisions. However, end users can have several necessities that are different from each other, including the displayed information itself or other design features. For these reasons, a domain engineering approach is proposed in order to produce customized dashboards adapted to singular requirements of every involved user (or group of users) by the parameterization of features, presentation components and data sources, obtaining a software product line of information dashboards. The creation of a product line would in-crease productivity, maintainability and traceability regarding the evolu-tion of the dashboards’ requirements. To validate this approach, a case study of its application in the context of the Spanish Observatory of Em-ployability and University Employment ecosystem is described, where us-ers (Spanish universities and administrators) will control their own dash-boards to reach insights about the employability of their graduates.