GRIAL resources
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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 Automatic generation of software interfaces for supporting decision-making processes. An application of domain engineering & machine learning(Grupo GRIAL, 2022-07-26) Vázquez-Ingelmo, A.Data analysis is a key process to foster knowledge generation in particular domains or fields of study. With a strong informative foundation derived from the analysis of collected data, decision-makers can make strategic choices with the aim of obtaining valuable benefits in their specific areas of action. However, given the steady growth of data volumes, data analysis needs to rely on powerful tools to enable knowledge extraction. Information dashboards offer a software solution to analyze large volumes of data visually to identify patterns and relations and make decisions according to the presented information. But decision-makers may have different goals and, consequently, different necessities regarding their dashboards. Moreover, the variety of data sources, structures, and domains can hamper the design and implementation of these tools. This Ph.D. Thesis tackles the challenge of improving the development process of information dashboards and data visualizations while enhancing their quality and features in terms of personalization, usability, and flexibility, among others. Several research activities have been carried out to support this thesis. First, a systematic literature mapping and review was performed to analyze different methodologies and solutions related to the automatic generation of tailored information dashboards. The outcomes of the review led to the selection of a model-driven approach in combination with the software product line paradigm to deal with the automatic generation of information dashboards. In this context, a meta-model was developed following a domain engineering approach. This meta-model represents the skeleton of information dashboards and data visualizations through the abstraction of their components and features and has been the backbone of the subsequent generative pipeline of these tools. The meta-model and generative pipeline have been tested through their integration in different scenarios, both theoretical and practical. Regarding the theoretical dimension of the research, the meta-model has been successfully integrated with other meta-model to support knowledge generation in learning ecosystems, and as a framework to conceptualize and instantiate information dashboards in different domains. In terms of the practical applications, the focus has been put on how to transform the meta-model into an instance adapted to a specific context, and how to finally transform this later model into code, i.e., the final, functional product. These practical scenarios involved the automatic generation of dashboards in the context of a Ph.D. Programme, the application of Artificial Intelligence algorithms in the process, and the development of a graphical instantiation platform that combines the meta-model and the generative pipeline into a visual generation system. Finally, different case studies have been conducted in the employment and employability, health, and education domains. The number of applications of the meta-model in theoretical and practical dimensions and domains is also a result itself. Every outcome associated to this thesis is driven by the dashboard meta-model, which also proves its versatility and flexibility when it comes to conceptualize, generate, and capture knowledge related to dashboards and data visualizations.Item Analyzing the software architectures supporting HCI/HMI processes through a systematic review of the literature(2019-03-22) Cruz-Benito, J.; García-Peñalvo, F. J.; Therón, R.Many researchers have dealt with Human-Computer Interaction or Human-Machine Interaction by building or designing software architectures that facilitate the users’ interaction or recognize users’ inputs to the generate proper responses. Many studies include these approaches in different research areas: from research in healthcare to mobile environments, robotics, etc. Interaction is seen as a critical concept, and the work for its improvement is a crucial factor for many platforms, systems, and business domains. The goal of this manuscript is to present a systematic review of the literature to identify, analyze and classify the published approaches to support or enhance Human-Computer Interaction or Human-Machine Interaction from the perspective of software architectures. The method followed is the systematic review following the guidelines related to Systematic Literature Reviews methods such as the one proposed by Kitchenham and other authors in the field of software engineering. As results, this study identified 39 papers that included software architectures to improve or analyze Human-Computer Interaction or Human-Machine Interaction. Three main approaches were found on software architectures: layered architectures, modular architectures, and architectures based on software agents, but they lacked standardization and were mainly ad-hoc solutions. The primary interfaces covered were those related to Graphical User Interfaces (GUIs) and multimodal/natural ones. The primary application domain detected were in multimodal systems. The main purpose of most of the papers was to support multimodal interaction. Some conclusions achieved are that the generic solutions to support or analyze HCI/HMI processes are still rare in the literature. Despite many works dealing with this topic and its issues and challenges, it is necessary to keep on improving the research in this area through the application of standard techniques and solutions, exploring new ways of analyzing and interpreting interaction, escaping from ad-hoc solutions or evaluating the solutions proposed.Item On data-driven systems analyzing, supporting and enhancing users’ interaction and experience(Grupo GRIAL, 2018-09-03) Cruz-Benito, J.The research areas of Human-Computer Interaction and Software Architectures have been traditionally treated separately, but in the literature, many authors made efforts to merge them to build better software systems. One of the common gaps between software engineering and usability is the lack of strategies to apply usability principles in the initial design of software architectures. Including these principles since the early phases of software design would help to avoid later architectural changes to include user experience requirements. The combination of both fields (software architectures and Human-Computer Interaction) would contribute to building better interactive software that should include the best from both the systems and user-centered designs. In that combination, the software architectures should enclose the fundamental structure and ideas of the system to offer the desired quality based on sound design decisions. Moreover, the information kept within a system is an opportunity to extract knowledge about the system itself, its components, the software included, the users or the interaction occurring inside. The knowledge gained from the information generated in a software environment can be used to improve the system itself, its software, the users’ experience, and the results. So, the combination of the areas of Knowledge Discovery and Human-Computer Interaction offers ideal conditions to address Human-Computer-Interaction-related challenges. The Human-Computer Interaction focuses on human intelligence, the Knowledge Discovery in computational intelligence, and the combination of both can raise the support of human intelligence with machine intelligence to discover new insights in a world crowded of data. This Ph.D. Thesis deals with these kinds of challenges: how approaches like data-driven software architectures (using Knowledge Discovery techniques) can help to improve the users' interaction and experience within an interactive system. Specifically, it deals with how to improve the human-computer interaction processes of different kind of stakeholders to improve different aspects such as the user experience or the easiness to accomplish a specific task. Several research actions and experiments support this investigation. These research actions included performing a systematic literature review and mapping of the literature that was aimed at finding how the software architectures in the literature have been used to support, analyze or enhance the human-computer interaction. Also, the actions included work on four different research scenarios that presented common challenges in the Human-Computer Interaction knowledge area. The case studies that fit into the scenarios selected were chosen based on the Human-Computer Interaction challenges they present, and on the authors’ accessibility to them. The four case studies were: an educational laboratory virtual world, a Massive Open Online Course and the social networks where the students discuss and learn, a system that includes very large web forms, and an environment where programmers develop code in the context of quantum computing. The development of the experiences involved the review of more than 2700 papers (only in the literature review phase), the analysis of the interaction of 6000 users in four different contexts or the analysis of 500,000 quantum computing programs. As outcomes from the experiences, some solutions are presented regarding the minimal software artifacts to include in software architectures, the behavior they should exhibit, the features desired in the extended software architecture, some analytic workflows and approaches to use, or the different kinds of feedback needed to reinforce the users’ interaction and experience. The results achieved led to the conclusion that, despite this is not a standard practice in the literature, the software environments should embrace Knowledge Discovery and data-driven principles to analyze and respond appropriately to the users’ needs and improve or support the interaction. To adopt Knowledge Discovery and data-driven principles, the software environments need to extend their software architectures to cover also the challenges related to Human-Computer Interaction. Finally, to tackle the current challenges related to the users’ interaction and experience and aiming to automate the software response to users’ actions, desires, and behaviors, the interactive systems should also include intelligent behaviors through embracing the Artificial Intelligence procedures and techniques.Item 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 Presentation of the paper “Improving success/completion ratio in large surveys: a proposal based on usability and engagement” in HCII 2017(Grupo GRIAL, 2017-07-12) Cruz-Benito, Juan; Therón, R.; García-Peñalvo, Francisco J.; Sánchez-Prieto, J. C.; Vázquez-Ingelmo, A.; Martín-González, M.; Martínez, J. M.This is the presentation of the paper entitled “Improving success/completion ratio in large surveys: a proposal based on usability and engagement” in the Emerging interactive systems for education session at the HCI International 2017 Conference, held in Vancouver, Canada, 9 - 14 July 2017. This paper presents a research focused on improve the success/completion ratio in large surveys. In this case, the large survey is the questionnaire produced by the Spanish Observatory for University Employability and Employment. This questionnaire is composed by about 32 and 60 questions and between 86 and 181 variables to be measured. The research is based on the previous experience of a past questionnaire proposed also by the Observatory composed also by a large amount of questions and variables to be measured (63-92 questions and 176-279 variables). After analysing the target population of the questionnaire (also comparing with the tar-get population of the previous questionnaire) and reviewing the literature, the researchers have designed 11 proposals for changes related to the questionnaire that could improve the users’ completion and success ratios (changes that could improve the users’ trust in the questionnaire, the questionnaire usability and user experience or the users’ engagement to the questionnaire). These changes are planned to be applied in the questionnaire in two main different experiments based on A/B test methodologies that will allow researchers to measure the effect of the changes in different populations and in an incremental way. The proposed changes have been assessed by five experts through an evaluation questionnaire. In this questionnaire, researchers gathered the score of each expert regarding to the pertinence, relevance and clarity of each change proposed. Regarding the results of this evaluation questionnaire, the reviewers fully supported 8 out of the 11 changes proposals, so they could be introduced in the questionnaire with no variation. On the other hand, 3 of the proposed changes or improvements are not fully supported by the experts (they have not received a score in the top first quartile of the 1-7 Likert scale). These changes will not be discarded immediately, because despite they have not received a Q1 score, they received a score within the second quartile of that 1-7 Likert scale, so could be reviewed to be enhanced to fit the OEEU’s context.Item Presentation of the paper “Software architectures supporting Human-Computer Interaction analysis: A Literature Review”(2016-07) Cruz-Benito, Juan; Therón, Roberto; García-Peñalvo, Francisco J.This is the presentation of the paper entitled “Software architectures supporting Human-Computer Interaction analysis: A Literature Review” in the Third International Conference, LCT 2016, Held as Part of HCI International 2016, Toronto, ON, Canada, July 17-22, 2016