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Permanent URI for this collectionhttps://repositorio.grial.eu/handle/123456789/34
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Item Data Literacy Questionnaire for Educators Creators(Grupo GRIAL, 2024-01-03) Donate-Beby, B.; García-Peñalvo, F. J.; Amo-Filvà, D.This questionnaire emerges within an increasingly digitized education context, driven by the exponential growth of Artificial Intelligence (AI). Generative Artificial Intelligence facilitates educational activities, providing teaching productivity support, critical thinking, and personalized learning. Nevertheless, data literacy is a necessary element for the effective use of AI, as needing more required knowledge would help in selecting the appropriate model for a specific task or understanding the ethical and privacy issues involved in data usage. Thus, the ability to process, organize, analyze, and comprehend data is known as data literacy, enabling the detection of errors in datasets and evaluating the quality and reliability of results generated by AI. Educational data management has significantly improved teaching-learning processes. Given the importance of this advancement, a self-assessment questionnaire on data literacy for Primary and Secondary School teachers is presented. This instrument aims to enhance the development of relevant competencies in data management, effectively providing educators and researchers with an evaluation tool to identify needs and areas for improvement. This report is also available in Spanish.Item Cuestionario de alfabetización de datos para el profesorado(Grupo GRIAL, 2024-01-02) Donate-Beby, B.; García-Peñalvo, F. J.; Amo-Filvà, D.Este cuestionario surge en el contexto de una enseñanza cada vez más digitalizada, impulsado por el crecimiento exponencial de la Inteligencia Artificial (IA). La Inteligencia Artificial Generativa facilita la actividad educativa, proporcionando un soporte para la productividad docente, su pensamiento crítico y la personalización del aprendizaje. Sin embargo, la alfabetización de datos constituye un elemento necesario para hacer un uso efectivo de la IA dado que, sin los conocimientos necesarios, no se podría elegir del modelo adecuado para una tarea específica, o comprender las cuestiones éticas y de privacidad que involucran el uso de datos. Así, la alfabetización de datos puede ser definida como la habilidad de procesar, organizar, analizar y comprender datos, permitiendo detectar errores en los conjuntos de datos, para evaluar la calidad y confiabilidad de los resultados emitidos por la IA. En la actualidad, el uso de la tecnología educativa, incluyendo el manejo de datos educativos, ha evidenciado mejoras significativas en el proceso de enseñanza-aprendizaje. Dada la importancia de este avance, se presenta un cuestionario de autoevaluación en alfabetización de datos dirigido a docentes de Educación Primaria y Secundaria. Este instrumento tiene como objetivo potenciar el desarrollo de competencias clave en el manejo de datos, brindando a educadores e investigadores una herramienta de evaluación que permita identificar necesidades y áreas de mejora de manera efectiva.Item Unplugged institutions: towards a localization of the cloud for Learning Analytics privacy enhancement(CEUR-WS.org, 2022-10-09) Amo-Filvà, D.; Fonseca, D.; Alier, M.; García-Peñalvo, F. J.; Casañ, M. J.The debate on privacy issues in Learning Analytics processes has been going on for a long time. In academic terms, various researchers attempted to identify the origin of the problem, provide solutions, and propose alternatives. However, the problem is complex, not yet solved, and increasingly pressing and serious. We reflect on cloud computing technologies as a generator of privacy issues and new derivatives. We assume that the technology used in the cloud is aggravating the problem, not Learning Analytics itself. Considering data capitalism, we argue that it is hopelessly impossible to solve the privacy problem, nor even mitigate it, when educational institutions use data ubiquity services in the cloud. We point to the paradox of Learning Analytics as the in-compatibility factor with third-party cloud computing services, where the latter is the link to all the associated privacy issues. To mitigate privacy issues, we propose the deconstruction of cloud computing for its localization. The localization is the basis of a new concept related to the disconnection of educational institutions from the cloud. New technological perspectives, legal frameworks, and social, cultural, and political changes are required.Item Connecting domain-specific features to source code: Towards the automatization of dashboard generation(2019-10-31) Vázquez-Ingelmo, A.; García-Peñalvo, F. J.; Therón, R.; Amo-Filvà, D.; Fonseca-Escudero, D.Dashboards are useful tools for generating knowledge and support decision-making processes, but the extended use of technologies and the increasingly available data asks for user-friendly tools that allow any user profile to exploit their data. Building tailored dashboards for any potential user profile would involve several resources and long development times, taking into account that dashboards can be framed in very different contexts that should be studied during the design processes to provide practical tools. This situation leads to the necessity of searching for methodologies that could accelerate these processes. The software product line paradigm is one recurrent method that can decrease the time-to-market of products by reusing generic core assets that can be tuned or configured to meet specific requirements. However, although this paradigm can solve issues regarding development times, the configuration of the dashboard is still a complex challenge; users' goals, datasets, and context must be thoroughly studied to obtain a dashboard that fulfills the users' necessities and that fosters insight delivery. This paper outlines the benefits and a potential approach to automatically configuring information dashboards by leveraging domain commonalities and code templates. The main goal is to test the functionality of a workflow that can connect external algorithms, such as artificial intelligence algorithms, to infer dashboard features and feed a generator based on the software product line paradigm