Publications

Permanent URI for this collectionhttps://repositorio.grial.eu/handle/123456789/34

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Subject: search.filters.subject.data visualization

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    Learning Analytics in Spanish K-12 levels: A Systematic Literature Review
    (2023-12-27) Donate-Beby, B.; García-Peñalvo, F. J.; Amo-Filva, D.
    Learning analytics is defined as the measurement, collection, analysis, and presentation of data about learners and their contexts to understand and optimize learning and the environments in which it occurs. Although their usefulness could be fundamental to recognize students’ learning processes, there is no clear framework on the current state of development of learning analytics in the K-12 Spanish territory. The present work aims to increase knowledge on the empirical frame of the question through a Systematic Literature Review (SLR). The methodology follows the indications provided by the PRISMA procedure. As a result, 16 papers have been selected and analyzed using different research indicators. The most significant findings within the selected papers are a lack of research where teachers have maintained an active role in the development of Learning Analytics in the natural educational context. Also, it has been found a tendency for the prediction and improvement of student engagement and performance on Game Learning Analytics in different knowledge or competencies.
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    Towards a Technological Ecosystem to Provide Information Dashboards as a Service: A Dynamic Proposal for Supplying Dashboards Adapted to Specific Scenarios
    (MDPI, 2021-04-05) Vázquez-Ingelmo, A.; García-Peñalvo, F. J.; Therón, R.
    Data are crucial to improve decision-making and obtain greater benefits in any type of activity. However, the large amount of information generated by new technologies has made data analysis and knowledge generation a complex task. Numerous tools have emerged to facilitate this generation of knowledge, such as dashboards. Although dashboards are useful tools, their effectiveness can be affected by poor design or by not taking into account the context in which they are placed. Therefore, it is necessary to design and create custom dashboards according to the audience and data domain. This paper presents an application of the software product line paradigm and the integration of this approach into a web service to allow users to request source code for customized information dashboards. The main goal is to introduce the idea of creating a holistic ecosystem of different services to craft and integrate information visualizations in a variety of contexts. One of the contexts that can be especially favored by this approach is the educational context, where learning analytics, data analysis of student performance, and didactic tools are becoming very relevant. Three different use cases of this approach are presented to illustrate the benefits of the developed generative service.
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    Representing Data Visualization Goals and Tasks Through Meta-Modeling to Tailor Information Dashboards
    (MDPI, 2020-03-30) Vázquez-Ingelmo, A.; García-Peñalvo, F. J.; Therón, R.; Conde, M. Á.
    Information dashboards are everywhere. They support knowledge discovery in a huge variety of contexts and domains. Although powerful, these tools can be complex, not only for the end-users but also for developers and designers. Information dashboards encode complex datasets into different visual marks to ease knowledge discovery. Choosing a wrong design could compromise the entire dashboard’s effectiveness, selecting the appropriate encoding or configuration for each potential context, user, or data domain is a crucial task. For these reasons, there is a necessity to automatize the recommendation of visualizations and dashboard configurations to deliver tools adapted to their context. Recommendations can be based on different aspects, such as user characteristics, the data domain, or the goals and tasks that will be achieved or carried out through the visualizations. This work presents a dashboard meta-model that abstracts all these factors and the integration of a visualization task taxonomy to account for the different actions that can be performed with information dashboards. This meta-model has been used to design a domain specific language to specify dashboards requirements in a structured way. The ultimate goal is to obtain a dashboard generation pipeline to deliver dashboards adapted to any context, such as the educational context, in which a lot of data are generated, and there are several actors involved (students, teachers, managers, etc.) that would want to reach different insights regarding their learning performance or learning methodologies.