Publications
Permanent URI for this collectionhttps://repositorio.grial.eu/handle/123456789/34
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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 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 Enabling adaptability in web forms based on user characteristics detection through A/B testing and machine learning(IEEE, 2018-02-14) Cruz-Benito, J.; Vázquez-Ingelmo, A.; Sánchez-Prieto, J. C.; Therón, R.; García-Peñalvo, F. J.; Martín-González, M.This paper presents an original study with the aim of improving users' performance in completing large questionnaires through adaptability in web forms. Such adaptability is based on the application of machine-learning procedures and an A/B testing approach. To detect the user preferences, behavior, and the optimal version of the forms for all kinds of users, researchers built predictive models using machine-learning algorithms (trained with data from more than 3000 users who participated previously in the questionnaires), extracting the most relevant factors that describe the models, and clustering the users based on their similar characteristics and these factors. Based on these groups and their performance in the system, the researchers generated heuristic rules between the different versions of the web forms to guide users to the most adequate version (modifying the user interface and user experience) for them. To validate the approach and con rm the improvements, the authors tested these redirection rules on a group of more than 1000 users. The results with this cohort of users were better than those achieved without redirection rules at the initial stage. Besides these promising results, the paper proposes a future study that would enhance the process (or automate it) as well as push its application to other eldsItem How different versions of layout and complexity of web forms affect users after they start it? A pilot experience(Springer, 2018-04-14) Cruz-Benito, Juan; Sánchez-Prieto, J. C.; Vázquez Ingelmo, A.; Therón, R.; García-Peñalvo, Francisco J.; Martín-González, M.This paper presents a research work that analyzes the effect of redirecting users between two different versions of a web form after they have started the questionnaire. In this case, we used a web form proposed by the Spanish Observatory for Employability and Employment (OEEU) that is designed to gather information from Spanish graduates. These two versions are different as follows: one of them is very simple and the other one includes several changes that appeared in the literature related to users’ trust, usability/user experience and layout design. To test the effect of redirecting users between both versions of the web form, we used a group of users that already have started the questionnaire and redirect them to the other version; this is, we changed the web form version they use to the other version and measure how this change affects them. This experiment has shown some promising results, which lead to enhance and extend the experience to bigger populations and other kind of changes in the user interfacesItem Comunidades de Aprendizaje en Redes Sociales y su Relación con los MOOC(2016-06) Cruz-Benito, Juan; Borrás-Gené, Oriol; García-Peñalvo, Francisco J.; Fidalgo Blanco, Á.; Therón, R.Item Extending MOOC ecosystems using web services and software architectures(In P. Ponsa, J. A. Román, & D. Guasch (Eds.), Actas del XVI Congreso Internacional de Interacción Persona‐Ordenador. Interacción 2015 (Vilanova i la Geltrú, Barcelona, España, 7 al 9 de Septiembre de 2015) (pp. 438-444), 2015-09-07) Cruz-Benito, Juan; Borrás-Gené, Oriol; García Peñalvo, Francisco J.; Fidalgo Blanco, Ángel; Therón, RobertoThis paper present a research project that tries to extend the MOOC ecosystems by integrating external tools like social networks. This integration is developed by using a software architecture that mediate between the different systems and platforms establishing communication workflows and analyzing the information retrieved. This kind of system is applied in a real case, and it allows teachers and managers of the MOOC platform to get enhanced information and insights about users interaction with contents and MOOC tools, as well as some metrics impossible to retrieve or calculate manually in this kind of eLearning platforms with high amounts of users.