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Item 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.Item Generative Artificial Intelligence in Education: From Deceptive to Disruptive(Universidad Internacional de la Rioja, 2024-03-12) Alier, M.; García-Peñalvo, F. J.; Camba, J. D.Generative Artificial Intelligence (GenAI) has emerged as a promising technology that can create original content, such as text, images, and sound. The use of GenAI in educational settings is becoming increasingly popular and offers a range of opportunities and challenges. This special issue explores the management and integration of GenAI in educational settings, including the ethical considerations, best practices, and opportunities. The potential of GenAI in education is vast. By using algorithms and data, GenAI can create original content that can be used to augment traditional teaching methods, creating a more interactive and personalized learning experience. In addition, GenAI can be utilized as an assessment tool and for providing feedback to students using generated content. For instance, it can be used to create custom quizzes, generate essay prompts, or even grade essays. The use of GenAI as an assessment tool can reduce the workload of teachers and help students receive prompt feedback on their work. Incorporating GenAI in educational settings also poses challenges related to academic integrity. With availability of GenAI models, students can use them to study or complete their homework assignments, which can raise concerns about the authenticity and authorship of the delivered work. Therefore, it is important to ensure that academic standards are maintained, and the originality of the student's work is preserved. This issue highlights the need for implementing ethical practices in the use of GenAI models and ensuring that the technology is used to support and not replace the student's learning experience.Item Evaluating the Effectiveness of Human-Centered AI Systems in Education(Departamento de Informática y Automática. Universidad de Salamanca, 2024-03-01) Shoeibi, N.; Therón, R.; García-Peñalvo, F. J.This thesis examines how AI can improve human-computer interaction (HCI) and user experience in education. A systematic litera-ture review (SLR) and LATILL case study show how AI can be used in education. The SLR examines existing literature to determine how AI af-fects user experience and HCI in education, highlighting personalization and adaptability of learning experiences, improved task performance, and improved user experience for teachers and students. AI implementation in education faces obstacles. Using CEFR levels and linguistic traits, the LATILL project uses a user-centered design to give students personali-zed guidance and support. It transforms language instruction and fosters engaging and successful learning by encouraging educator collaboration and resource sharing. This study emphasizes the importance of user ex-perience and HCI principles in designing AI-driven educational systems. AI and user-centered design can improve learning, student engagement, and educational outcomes.Item Questionário de opinião com universitários/as sobre os estudos superiores em ciência, tecnologia, engenharia e matemática (QSTEMHE)(2024-02) Verdugo-Castro, Sonia; García-Holgado, Alicia; Sánchez-Gómez, Mª Cruz; Milfont Shzu, Maura AngélicaEm alguns países existe segregação por razão de gênero em algumas áreas de estudos, um exemplo disto são os setores da Ciência, da Tecnologia, da Engenharia e da Matemática, conhecidas pelo acrônimo STEM. Por esse motivo, foi elaborado o Questionário de opinião com universitários/as sobre os estudos superiores em ciência, tecnologia, engenharia e matemática (QSTEMHE), que foi validado empiricamente (Verdugo-Castro et al., 2022b) e faz parte da tese de doutorado de Sonia Verdugo-Castro, na Universidade de Salamanca (Verdugo-Castro, 2022). O objetivo perseguido com a aplicação do questionário QSTEMHE é descobrir os estereótipos de gênero que os estudantes universitários têm sobre os estudos superiores em STEM, uma vez que os fatores que afetam essa diferença de gênero tenham sido identificados (Verdugo-Castro, García-Holgado, et al., 2023). O questionário é composto de quatro blocos, dois dos quais contêm perguntas sociodemográficas e contextuais, outro contém perguntas abertas (Verdugo-Castro, Sánchez-Gómez, et al., 2023) e outro bloco contém itens nos quais são feitas afirmações às quais se deve responder com o grau de concordância ou discordância sobre a opinião que se tem como estudante universitário em relação a estudos superiores relacionados a ciência, tecnologia, engenharia e matemática (Verdugo-Castro et al., 2020, 2022a). Esta iniciativa faz parte de um projeto de pesquisa da Universidade de Brasília em parceria com a Universidade de Salamanca - Espanha com o objetivo de analisar a percepção dos estudantes universitários a respeito das áreas STEM para a detecção do problema e para a condução na elaboração de estratégias para a redução da lacuna de gênero nestas áreas. O questionário foi construído sob as dimensões associadas ao estudo de gênero (D3); atitudes (D4) e interesses (D1); percepções e autopercepções (D2), e expectativas sobre as Ciências (D5) (Verdugo-Castro et al., 2022b). No que diz respeito a algumas perguntas vinculadas a gênero, o estudo trata desde o respeito e a inclusão de diferentes identidades de gênero, sabendo que o gênero não se limita a classificação binária de homem e mulher, no entanto algumas perguntas fazem referência a homens e mulheres devido a tendência histórica de segregação entre ambos os gêneros no âmbito do estudo. A versão em espanhol do questionário, desenvolvida no âmbito da tese de doutorado de Sonia Verdugo-Castro (Verdugo-Castro, 2022), obteve um parecer favorável do Comitê de Bioética (CBE) da Universidade de Salamanca, com o número de registro 557. Além disso, é importante dizer que, apesar desta iniciativa ter respaldo no artigo 1, parágrafo único, inciso I e V da resolução CNS N˚. 510, de 7 de abril de 2016, o presente instrumento obteve a aprovação do Comitê de Ética da Universidade de Brasília (CAAE: 58603420.8.0000.5540; n.do parecer consubstanciado do CEP: 5.908.089, data: 23/02/2023). Por último, como o instrumento foi submetido a procedimentos de validação, alguns itens foram removidos. É por essa razão que, nesta versão final apresentada, ao lado dos códigos identificadores de cada item, em alguns deles, há uma referência em itálico e em cinza, entre parênteses, que está associada aos valores identificadores do questionário em sua primeira versão.Item Cuestionario de opinión con universitarios/as sobre los estudios superiores en ciencia, tecnología, ingeniería y matemáticas (QSTEMHE)(Grupo GRIAL, 2024-02) Verdugo-Castro, Sonia; García-Holgado, Alicia; Sánchez-Gómez, Mª CruzEn algunos países existe segregación por razón de género en algunas áreas de estudio, un caso es el sector de la ciencia, la tecnología, la ingeniería y las matemáticas (STEM). Por este motivo, se ha diseñado el Cuestionario de opinión con universitarios/as sobre los estudios superiores en ciencia, tecnología, ingeniería y matemáticas (QSTEMHE, del acrónimo en inglés). El instrumento ha sido validado empíricamente (Verdugo-Castro et al., 2022b) y forma parte de una investigación realizada a través de la tesis doctoral de Sonia Verdugo-Castro, en la Universidad de Salamanca (Verdugo-Castro, 2022). El objetivo que se persigue con la aplicación del cuestionario QSTEMHE es conocer los estereotipos de género que tiene el estudiantado universitario sobre los estudios superiores STEM, una vez que se han identificado los factores que inciden sobre dicha brecha de género (Verdugo-Castro, García-Holgado, et al., 2023). El cuestionario está compuesto por cuatro bloques, en dos de ellos se recogen preguntas sociodemográficas y contextuales, en otro se plantean preguntas abiertas (Verdugo-Castro, Sánchez-Gómez, et al., 2023) y en otro bloque se recogen los ítems en los que se realiza afirmaciones a las que se debe responder con el grado de acuerdo o desacuerdo acerca de la opinión que se tiene como universitario/a en relación con los estudios superiores relacionados con la ciencia, la tecnología, la ingeniería y las matemáticas (Verdugo-Castro et al., 2020, 2022a). Las dimensiones a las que se asocian los ítems de opinión validados son: Ideología de Género (D3), Intereses (D1), Actitudes (D4), Percepción y Autopercepción (D2) y Expectativas sobre la Ciencia (D5) (Verdugo-Castro et al., 2022b). En relación con algunas preguntas vinculadas al género, se ha tratado la investigación desde el respeto y la inclusión de las diferentes identidades de género, sabiendo que el género no se limita a la clasificación binaria de hombre y mujer, si bien algunas preguntas hacen referencia a los hombres y a las mujeres por la tendencia histórica de segregación entre ambos géneros en el ámbito del estudio. En la aplicación del cuestionario en el marco de la tesis doctoral de Sonia Verdugo-Castro (Verdugo-Castro, 2022), los datos obtenidos se han tratado de forma agregada y anónima, una vez que se ha obtenido el informe favorable del Comité de Bioética (CBE) de la Universidad de Salamanca, con el nº de registro 557. Finalmente, dado que el instrumento ha sido sometido a procedimientos de validación, se han suprimido algunos ítems. Es por este motivo que, en esta versión final presentada, junto a los códigos identificadores de cada ítem, en algunos de ellos, hay una referencia en letra cursiva y en gris, entre paréntesis, que se asocia a los valores identificativos del cuestionario en su primera versión.Item Questionnaire with university students on STEM studies in Higher Education (QSTEMHE)(GRIAL Research Group, 2024-02) Verdugo-Castro, Sonia; García-Holgado, Alicia; Sánchez-Gómez, Mª CruzIn some countries, there is gender segregation in some fields of study, one case being the science, technology, engineering, and mathematics (STEM) sector. For this reason, the Questionnaire with university students on STEM studies in Higher Education (QSTEMHE) has been designed. The instrument has been empirically validated (Verdugo-Castro et al., 2022b). It is part of the research carried out through Sonia Verdugo-Castro's doctoral thesis at the University of Salamanca (Verdugo-Castro, 2022). The objective pursued with the application of the QSTEMHE questionnaire is to find out the gender stereotypes that university students have about higher STEM studies once the factors that affect this gender gap have been identified (Verdugo-Castro, García-Holgado, et al., 2023). The questionnaire is composed of four blocks, two of which contain socio-demographic and contextual questions, another contains open questions (Verdugo-Castro, Sánchez-Gómez, et al., 2023) and another block contains items in which statements are made to which one must respond with the degree of agreement or disagreement about the opinion that one has as a university student about higher studies related to science, technology, engineering and mathematics (Verdugo-Castro et al., 2020, 2022a). The dimensions to which the validated opinion items are associated are Gender Ideology (D3), Interests (D1), Attitudes (D4), Perception and Self-perception (D2) and Expectations about Science (D5) (Verdugo-Castro et al., 2022b). Concerning some questions linked to gender, the research has been approached with respect and inclusion of the different gender identities, knowing that gender is not limited to the binary classification of male and female. However, some questions refer to men and women due to the historical tendency of segregation between the two genders in the field of study. In the application of the questionnaire in the framework of the doctoral thesis of Sonia Verdugo-Castro (Verdugo-Castro, 2022), the data obtained have been processed in an aggregated and anonymous way once the favourable report of the Bioethics Committee (CBE) of the University of Salamanca has been obtained, with registration number 557. Finally, some items have been deleted since the instrument has been subjected to validation procedures. For this reason, in this final version presented, next to the codes identifying each item, in some of them, there is a reference in italics and in grey, in brackets, which is associated with the identifying values of the questionnaire in its first version.Item Cómo afecta la inteligencia artificial generativa a los procesos de evaluación(2024-01-12) García-Peñalvo, F. J.La irrupción de la inteligencia artificial generativa (IAG) en la educación exige redefinir los procesos de evaluación. Muchas tareas evaluativas ahora pueden ser realizadas por IAG, lo que subraya la necesidad de equilibrar tecnología y pedagogía, redefiniendo e innovando en los métodos de evaluación y un uso ético y educativo responsable de estas herramientas.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 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.