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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 KoopaML: Application for receiving and processing DICOM images(CEUR-WS.org, 2023-12-05) Fraile-Sanchón, R.; Vázquez-Ingelmo, A.; García-Holgado, A.; García-Peñalvo, F. J.AI algorithms application to medical data has gained relevance due to their powerful benefits among different research tasks. However, medical data is heterogeneous and diverse, and these algorithms need technological support to tackle these data management challenges. KoopaML enables users to unify medical data, especially DICOM images and apply AI algorithms to them in a straightforward way through an online web application. This work presents a new feature in the KoopaML platform: a Machine Learning platform to assist non-expert users in defining and applying ML pipelines. The feature comprises the reception, storage, and management of DICOM images. These images are received through a connection with a PACS (Picture Archiving Communication System) system already configured by users on the platform and, after storing the images, it is possible to apply AI algorithms to them and make modifications or annotations.Item Usability Study of a Pilot Database Interface for Consulting Open Educational Resources in the Context of the ENCORE Project(Springer, 2023-07-23) Vázquez-Ingelmo, A.; García-Holgado, A.; García-Peñalvo, F. J.; Chiarello, F.Open educational resources (OER) are materials such as textbooks, lessons, and other teaching and learning tools that are freely accessible for use. OER are gaining popularity as a means for educators to give their students access to high-quality, economical educational materials. OER can encourage sharing infor-mation and resources throughout the educational community while also helping lower the cost of education for both students and teachers. In this context, the ENCORE project seeks, among other goals, to assist students and workers in acquiring the skills necessary to deal with economic, ecological, and technolog-ical challenges as well as to address the skills gap between the supply of educa-tional institutions and the demand of employers and assist educators in staying abreast of the constantly changing landscape of skills. One of the first steps to reach the project’s goals is to build a robust database that contains quality OERs linked to green, digital, and entrepreneurial (GDE) skills. A graphical interface has been developed to retrieve and display information about the OERs, and, in turn, to make these resources available for any stakeholder. However, due to the significant quantity of information, it is important to develop an interface that enhances user experience. This work presents a usability study of the ENCORE project’s OER database interface carried out through a System Usability Scale (SUS) questionnaire, as well as future interface improvements based on the results.Item Designing Learning Paths with Open Educational Resources: An Investigation in Model-Driven Engineering(IEEE, 2023-06-20) Bucchiarone, A.; Vázquez-Ingelmo, A.; Schiavo, G.; García-Holgado, A.; García-Peñalvo, F. J.; Zschaler, S.This paper presents a methodology for supporting educators and learners in designing and delivering learning paths using Open Educational Resources (OERs). While OERs provide free and unlimited access to high-quality learning resources, their scattered nature presents significant challenges in finding relevant and high-quality materials. Furthermore, the lack of a centralized repository for OERs makes it difficult to ensure the accuracy and quality of the materials being queried. To address these issues, the paper presents the ENCORE methodology that provides software components, or ENCORE enablers, to enable educators to include relevant OERs that target specific skills in their learning paths. The methodology also leverages notebook interfaces and gamification mechanisms to promote stu- dents’ learning engagement. The paper illustrates the ENCORE methodology through a case study, where the methodology is applied to an OER repository of educational resources developed by the expert network on model-driven engineering (MDEnet). The case study demonstrates that designing the database and enablers as independent but holistic components enables the use of OERs to accomplish a wider range of educational goals, such as supporting the creation of learning paths. The paper concludes with indications on how to extend the ENCORE methodology to further enhance the creation and delivery of personalized learning experiences, supporting the reuse of open educational resources and the automatic generation of personalized learning paths.Item Are Textual Recommendations Enough? Guiding Physicians Toward the Design of Machine Learning Pipelines Through a Visual Platform(Springer, 2023-09-05) Vázquez-Ingelmo, A.; García-Holgado, A.; García-Peñalvo, F. J.; Pérez-Sánchez, P.; Antúnez-Muiños, P.; Sánchez-Puente, A.; Vicente-Palacios, V.; Dorado-Díaz, P. I.; Sánchez, P. L.The prevalence of artificial intelligence (AI) in our daily lives is often exaggerated by the media, leading to a positive public perception while overlook-ing potential problems. In the field of medicine, it is crucial to educate future health-care professionals on the advantages and disadvantages of AI and to emphasize the importance of creating fair, ethical, and reproducible models. The KoopaML platform was developed to provide an educational and user-friendly interface for inexperienced users to create AI pipelines. This study analyzes the quantitative and interaction data gathered from a usability test involving physicians from the University Hospital of Salamanca, with the aim of identifying new interaction paradigms to improve the platform’s usability. The results shown that the plat-form is difficult to learn for inexperienced users due to its contents related to AI. Following these results, a set of improvements are proposed for the next version of KoopaML, focusing on reducing the interactions needed to create the pipelines.Item Testing and Improvements of KoopaML: A Platform to Ease the Development of Machine Learning Pipelines in the Medical Domain(Springer, 2023-05-01) Vázquez-Ingelmo, A.; García-Holgado, A.; García-Peñalvo, F. J.; Fraile-Sanchón, R.; Pérez-Sánchez, P.; Antúnez-Muiños, P.; Sánchez-Puente, A.; Vicente-Palacios, V.; Dorado-Díaz, P. I.; Cruz-González, I.; Sánchez, P. L.Machine Learning (ML) applications in complex domains, such as the medical domain, can be highly beneficial, but also hazardous if some concepts are overlooked. In this context, however, health professionals denote expertise in their domain, but they often lack skills in terms of ML. In this sense, to leverage ML applications in the medical domain, it is important to combine both domain expertise and ML-related skills. In previous works, we tackled this challenge in the health context through a visual platform (KoopaML) that enables lay users to build ML pipelines. The present work describes the challenges derived from the first version of the platform and the prototypes for the new features designed to address them. The prototypes have been validated by two experts, obtaining highly valuable feedback.Item Flexible Heuristics for Supporting RecommendationsWithin an AI Platform Aimed at Non-expert Users(Springer, 2023-05-01) Vázquez-Ingelmo, A.; García-Holgado, A.; García-Peñalvo, F. J.; Andrés-Fraile, E.; Pérez-Sánchez, P.; Antúnez-Muiños, P.; Sánchez-Puente, A.; Vicente-Palacios, V.; Dorado-Díaz, P. I.; Cruz-González, I.; Sánchez, P. L.The use of Machine Learning (ML) to resolve complex tasks has become popular in several contexts. While these approaches are very effective and have many related benefits, they are still very tricky for the general audi-ence. In this sense, expert knowledge is crucial to apply ML algorithms properly and to avoid potential issues. However, in some situations, it is not possible to rely on experts to guide the development of ML pipelines. To tackle this issue, we present an approach to provide customized heuristics and recommendations through a graphical platform to build ML pipelines, namely KoopaML, focused on the medical domain. With this approach, we aim not only at providing an easy way to apply ML for non-expert users, but also at providing a learning experience for them to understand how these methods work.Item Recursos Educativos Abiertos para mejorar la protección de datos de los estudiantes en las escuelas(Servicio de Publicaciones Universidad de Zaragoza, 2023-10-18) Amo-Filva, D.; Fonseca-Escudero, D.; Sanchez-Sepulveda, M. V.; Hasti, H.; Aguayo Mauri, S.; García-Holgado, A.; García-Holgado, L.; Vázquez-Ingelmo, A.; García-Peñalvo, F. J.; Orehovački, T.; Krašna, M.; Pesek, I.; Marchetti, E.; Valente, A.; Witfelt, C.; Ružić, I.; Fraoua, K. E.; Moreira, F.; Santos Pereira, C.; Paes, C.Las escuelas están recurriendo a software de terceros que se ejecuta en la nube. Este cambio presenta desafíos, problemas y preocupaciones únicas relacionadas con la privacidad y la seguridad de los datos de los estudiantes. El proyecto SPADATAS promueve el uso responsable de las tecnologías digitales y mejorar la protección de datos en las prácticas de gestión de datos académicos dentro de los entornos educativos. Uno de nuestros objetivos es abordar esas preocupaciones sobre privacidad y seguridad de datos, particularmente en los procesos de tratamiento de datos académicos. Para aumentar la conciencia y mejorar la protección de datos en las escuelas, realizamos una búsqueda exhaustiva de recursos en línea y abiertos relevantes. Este trabajo presenta la metodología utilizada y los resultados. Existe una gran cantidad de recursos para las escuelas, pero se requiere un análisis meticuloso para discernir cuáles son los más efectivos para mejorar la protección de datosItem What Do We Mean by GenAI? A Systematic Mapping of The Evolution, Trends, and Techniques Involved in Generative AI(2023-08-01) García-Peñalvo, F. J.; Vázquez-Ingelmo, A.Artificial Intelligence has become a focal point of interest across various sectors due to its ability to generate creative and realistic outputs. A specific subset, generative artificial intelligence, has seen significant growth, particularly in late 2022. Tools like ChatGPT, Dall-E, or Midjourney have democratized access to Large Language Models, enabling the creation of human-like content. However, the concept 'Generative Artificial Intelligence' lacks a universally accepted definition, leading to potential misunderstandings. While a model that produces any output can be technically seen as generative, the Artificial Intelligent research community often reserves the term for complex models that generate high-quality, human-like material. This paper presents a literature mapping of AI-driven content generation, analyzing 631 solutions published over the last five years to better understand and characterize the Generative Artificial Intelligence landscape. Our findings suggest a dichotomy in the understanding and application of the term "Generative AI". While the broader public often interprets "Generative AI" as AI-driven creation of tangible content, the AI research community mainly discusses generative implementations with an emphasis on the models in use, without explicitly categorizing their work under the term "Generative AI".Item Explainable Rules and Heuristics in AI Algorithm Recommendation Approaches—A Systematic Literature Review and Mapping Study(Tech Science Press, 2023-02-06) García-Peñalvo, F. J.; Vázquez-Ingelmo, A.; García-Holgado, A.The exponential use of artificial intelligence (AI) to solve and automated complex tasks has catapulted its popularity generating some challenges that need to be addressed. While AI is a powerful means to discover interesting patterns and obtain predictive models, the use of these algorithms comes with a great responsibility, as an incomplete or unbalanced set of training data or an unproper interpretation of the models’ outcomes could result in misleading conclusions that ultimately could become very dangerous. For these reasons, it is important to rely on expert knowledge when applying these methods. However, not every user can count on this specific expertise; non-AI-expert users could also benefit from applying these powerful algorithms to their domain problems, but they need basic guidelines to obtain the most out of AI models. The goal of this work is to present a systematic review of the literature to analyze studies whose outcomes are explainable rules and heuristics to select suitable AI algorithms given a set of input features. The systematic review follows the methodology proposed by Kitchenham and other authors in the field of software engineering. As a result, 9 papers that tackle AI algorithm recommendation through tangible and traceable rules and heuristics were collected. The reduced number of retrieved papers suggests a lack of reporting explicit rules and heuristics when testing the suitability and performance of AI algorithms.