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    ModelViz: A Model-Driven Engineering Approach for Visual Analytics System Design
    (IEEE, 2024-03-29) Khakpour, A.; Vázquez-Ingelmo, A.; Colomo-Palacios, R.; García-Peñalvo, F. J.; Martini, A.
    Visual analytics systems should be able to consolidate data from disparate sources, conduct exploratory analysis, create visualizations that suit different users, and integrate seamlessly with decision-making activities to support data-driven decision-making. However, current mainstream visual analytics solutions often lack support for all these requirements. To address this gap, we propose the use of model-driven engineering to design visual analytics systems. To demonstrate the feasibility of this approach, we developed a Domain-Specific Modeling Language (DSML) named ModelViz to design visual analytics systems for consumer goods supply chain applications. Furthermore, we present the work of our DSML, using data from a manufacturing company as a case study. Finally, we evaluated ModelViz quantitatively by comparing it with other similar works from the literature. Our results demonstrate that this approach meets the requirements and provides a promising direction for designing visual analytics systems by considering domain-specific aspects to help achieve business goals.
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    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.
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    Following up the progress of doctoral students and advisors’ workload through data visualizations: a case study in a PhD program
    (CEUR-WS.org, 2021-07-07) Vázquez-Ingelmo, A.; García-Holgado, A.; Hernández-Payo, H.; García-Peñalvo, F. J.; Therón, R.
    One of the most important aspects to consider during the development of a PhD is the students’ progress, both for their advisors and the students themselves. However, several achievements of different natures are involved during a PhD (research stays, publications, seminars, research plans, etc.). For these reasons, we propose a set of data visualizations to support decision-making processes in a PhD program. A preliminary requirement elicitation process was carried out to obtain a design basis for the implementation and integration of these tools in the PhD portal. Once the visualizations were implemented, a usability study was performed to measure the perceived usability of the newly added PhD portal functionalities. This paper presents the design process and usability study outcomes of applying data visualizations to the learning outcomes of the PhD Programme in Education in the Knowledge Society at the University of Salamanca.
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    Aggregation Bias: A Proposal to Raise Awareness Regarding Inclusion in Visual Analytics
    (Springer, 2020-04-01) Vázquez-Ingelmo, A.; García-Peñalvo, F. J.; Therón, R.
    Data is a powerful tool to make informed decisions. They can be used to design products, to segment the market, and to design policies. However, trusting so much in data can have its drawbacks. Sometimes a set of indicators can conceal the reality behind them, leading to biased decisions that could be very harmful to underrepresented individuals, for example. It is challenging to ensure unbiased decision-making processes because people have their own beliefs and characteristics and be unaware of them. However, visual tools can assist decision-making processes and raise awareness regarding potential data issues. This work describes a proposal to fight biases related to aggregated data by detecting issues during visual analysis and highlighting them, trying to avoid drawing inaccurate conclusions.
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    A Meta-modeling Approach to Take into Account Data Domain Characteristics and Relationships in Information Visualizations
    (Springer, 2021-03-30) Vázquez-Ingelmo, A.; García-Holgado, A.; García-Peñalvo, F. J.; Therón, R.
    Visual explanations are powerful means to convey information to large audiences. However, the design of information visualizations is a complex task, because a lot of factors are involved (the audience profile, the data domain, etc.). The complexity of this task can lead to poor designs that could make users reach wrong conclusions from the visualized data. This work illustrates the process of identifying features that could make an information visualization confusing or even misleading with the goal of arranging them into a meta-model. The meta-model provides a powerful resource to automatically generate information visu-alizations and dashboards that take into account not only the input data, but also the audience’s characteristics, the available data domain knowledge and even the data context.