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Item Showcasing sentiment classification and word prediction in the quantum natural processing area(CEUR-WS.org, 2023-12-05) Peral-García, D.; Cruz-Benito, J.; García-Peñalvo, F. J.The advent of quantum computers makes it possible to perform quantum computations in different areas like machine learning, finance, or chemistry. This paper showcases one of the emerging areas under quantum machine learning, quantum natural language processing. We present two quantum natural language processing tasks, sentiment classification and missing word prediction in a sentence. We show how these tasks can be achieved even in real quantum computers using the two main libraries in this subfield, DisCoPy, and lambeq.Item Desarrollo de herramientas y métodos automatizados para la simulación y mejora de procesos de computación cuántica basados en inteligencia artificial(Jornada de seguimiento de la actividad investigadora 2021-2022 USAL, 2022-05-24) Peral García, D.; Cruz-Benito, J.; García-Peñalvo, F. J.Con la premisa de alcanzar velocidades de cálculo exponencialmente supe-riores a la computación clásica, la computación cuántica está evolucionan-do rápidamente para convertirse en una de las áreas más populares de la in-geniería informática. Aunque, hasta hace poco, la mayor parte del trabajo en computación cuántica era puramente teórico o sus simulaciones eran im-plementadas en hardware clásico, la aparición de los denominados disposi-tivos Noisy Intermediate Scale Quantum (NISQ) ha permitido la ejecución de estos trabajos en computadores cuánticos. Recientemente, el área de apli-cación de los algoritmos cuánticos se ha ampliado de forma muy notable y ha proporcionado métodos prometedores en áreas como la química, la reso-lución de sistemas de ecuaciones lineales, las simulaciones físicas y el aprendizaje automático. Una de las áreas más emergentes ha sido el campo del aprendizaje automático, con los dispositivos y algoritmos cuánticos existentes, ya se han producido algunos avances como la creación de redes neuronales con capas cuánticasItem 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 AI-Driven Assessment of Students: Current Uses and Research Trends(Springer, 2020-07-19) Sánchez-Prieto, J. C.; Gamazo, A.; Cruz-Benito, J.; Therón, R.; García-Peñalvo, F. J.During the last decade, the use of AIs is being incorporated into the educational field whether to support the analysis of human behavior in teaching-learning contexts, as didactic resource combined with other technologies or as a tool for the assessment of the students. This proposal presents a Systematic Literature Review and mapping study on the use of AIs for the assessment of students that aims to provide a general overview of the state of the art and identify the current areas of research by answering 6 research questions related with the evolution of the field, and the geographic and thematic distribution of the studies. As a result of the selection process this study identified 20 papers focused on the research topic in the repositories SCOPUS and Web of Science from an initial amount of 129. The analysis of the papers allowed the identification of three main thematic categories: assessment of student behaviors, assessment of student sentiments and assessment of student achievement as well as several gaps in the literature and future research lines addressed in the discussion.Item Assessed by Machines: Development of a TAM-Based Tool to Measure AI-based Assessment Acceptance Among Students(2020-12-05) Sánchez-Prieto, J. C.; Cruz-Benito, J.; Therón, R.; García-Peñalvo, F. J.In recent years, the use of more and more technology in education has been a trend. The shift of traditional learning procedures into more online and tech-ish approaches has contributed to a context that can favor integrating Artificial-Intelligence-based or algorithm-based assessment of learning. Even more, with the current acceleration because of the COVID-19 pandemic, more and more learning processes are becoming online and are incorporating technologies related to automatize assessment or help instructors in the process. While we are in an initial stage of that integration, it is the moment to reflect on the students' perceptions of being assessed by a non-conscious software entity like a machine learning model or any other artificial intelligence application. As a result of the paper, we present a TAM-based model and a ready-to-use instrument based on five aspects concerning understanding technology adoption like the AI-based assessment on education. These aspects are perceived usefulness, perceived ease of use, attitude towards use, behavioral intention, and actual use. The paper's outcomes can be relevant to the research community since there is a lack of this kind of proposal in the literature.Item How to Measure Teachers’ Acceptance of AI-driven Assessment in eLearning: A TAM-based Proposal(ACM, 2019-10-16) Sánchez-Prieto, J. C.; Cruz-Benito, J.; Therón, R.; García-Peñalvo, F. J.The use of AI is becoming a growing reality the educational field. One of the activities in which it is beginning to be implemented is the assessment of student achievement. This way, we can find in the literature an increasing number of investigations focused on the possibilities offered by the adoption of AI-driven assessment. However, the use of AI is also a source of concern that raises suspicions in some sectors of our society. In this context, knowing the position of the teachers towards this topic is critical to guarantee the successful development of the field. This paper intends to fill a research gap in the literature by offering a technology adoption model based on TAM to study the factors that condition the use of AI-driven assessment among teachers. To present this model we offer a background on the use of AI in education and the technology acceptance among teachers, as well as the definition of the eight constructs and the relational hypotheses included. Finally, the possibilities of the model and future lines of research are discussed.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 Scaffolding the OEEU's Data-Driven Ecosystem to Analyze the Employability of Spanish Graduates(IGI Global, 2018-01-01) Vázquez-Ingelmo, A.; Cruz-Benito, J.; García-Peñalvo, F. J.; Martín-González, M.This chapter outlines the technological evolution experimented by the Observatory for University Employability and Employment’s information system to become a data-driven technological ecosystem. This observatory collects data from more than 50 Spanish universities and their graduate students (bachelor’s degree, master’s degree) with the goal of measuring the factors that lead to students’ employability and employment. The goals pursued by the observatory need a strong technological support to gather, process, and disseminate the related data. The system that supports these tasks has evolved from a standard (traditional) information system to a data-driven ecosystem, which provides remarkable benefits covering the observatory’s requirements. The benefits, the foundations, and the way the data-driven ecosystem is built will be described throughout the chapter, as well as how the information obtained is exploited in order to provide insights about the employment and employability variables.Item Sistemas guiados por datos para analizar, apoyar y mejorar la interacción y experiencia de los usuarios(Grupo GRIAL, 2017-09-03) Cruz-Benito, J.Resumen extendido en español de la tesis "On data-driven systems analyzing, supporting and enhancing users’ interaction and experience". Las áreas de investigación sobre la Interacción Persona-Ordenador y las Arquitecturas Software han sido tratadas tradicionalmente por separado. A lo largo de los años, muchos autores se han esforzado en unir ambos campos para construir mejores sistemas software. Una de las brechas comunes entre la ingeniería del software y la usabilidad es la falta de estrategias para aplicar principios de usabilidad en una arquitectura software desde las fases de diseño inicial. La inclusión de estos principios desde las fases tempranas del diseño del software pueden ayudar a remitir cambios arquitectónicos posteriores que buscan incluir requisitos relacionados con la experiencia de usuario. La mezcla de ambas áreas (la Interacción Persona-ordenador y las arquitecturas software) podría contribuir a construir mejor software interactivo que incluyan lo mejor de los sistemas informáticos y de los diseños centrados en el usuario. En esa combinación, las arquitecturas software deberían contener una estructura fundamental y las ideas básicas del sistema para ofrecer la calidad deseada en base a unas decisiones de diseño adecuadas. Por otro lado, la información almacenada en cualquier sistema informático representa un oportunidad de extraer conocimiento sobre el sistema en sí mismo, sus componentes, el software que incluye, los usuarios, o la interacción que ocurre internamente entre todos los actores que participan en el mismo. El conocimiento obtenido de la información generada en un entorno software puede usarse para mejorar el sistema en sí, su software, la experiencia de los usuarios y los resultados del mismo. Por tanto, la combinación de las áreas de Descubrimiento de Conocimiento y la Interacción Persona-Ordenador ofrece unas condiciones ideales para tratar con los retos que supone la Interacción Persona-Ordenador. La Interacción Persona-Ordenador se centra en la inteligencia humana, mientras que el Descubrimiento de Conocimiento se centra en la inteligencia computacional; la combinación de ambas puede ayudar a soportar la inteligencia humana con la inteligencia computacional para descubrir nuevas evidencias y resultados en un mundo repleto de datos. Esta Tesis Doctoral trata con ese tipo de retos: cómo aproximaciones como las arquitecturas software guiadas por datos (usando técnicas de Descubrimiento de Conocimiento) pueden ayudar a mejorar la interacción de los usuarios y su experiencia dentro de un sistema interactivo. Específicamente, trata sobre cómo mejorar los procesos de interacción persona-ordenador de distintos tipos de usuarios y actores para mejorar distintos aspectos como la experiencia de los usuarios o la facilidad para completar una tarea concreta. La investigación que se presenta está soportada por diversas acciones de investigación y experimentos. Entre las acciones de investigación se incluye una revisión y mapeo sistemático de la literatura que pretende encontrar cómo se han usado las arquitecturas software en la literatura para soportar, analizar o mejorar la interacción entre personas y ordenadores. Estas acciones también incluyen el trabajo en cuatro escenarios de investigación distintos que presentan retos comunes en el área de conocimiento de la Interacción Persona-Ordenador. Los casos de estudio que encajan en cada uno de estos escenarios han sido seleccionados por los retos que presentan en relación a la Interacción Persona-Ordenador y por la accesibilidad del autor a los mismos. Los cuatro casos de estudio fueron: un laboratorio educativo dentro de un mundo virtual, un Curso On-line Masivo y Abierto y las redes sociales usadas en relación por los estudiantes para discutir y aprender, un sistema software que incluye formularios web muy extensos y un entorno donde programadores desarrollan código en el ámbito de la computación cuántica. El desarrollo de estas experiencias ha requerido la revisión de más de 2700 artículos científicos (solo durante la fase de revisión), el análisis de la interacción de 6000 usuarios entre los cuatro contextos distintos o el análisis de 500000 programas que emplean código relacionado con la computación cuántica. Como resultados de estas experiencia, se presentan diversas soluciones relacionadas con los artefactos software mínimos que se necesitan en una arquitectura software que incluya el soporte, análisis o mejora de la interacción entre personas y ordenadores, el comportamiento que deben tener, las características deseadas en dichas arquitecturas extendidas, algunos flujos de trabajo y aproximaciones para el análisis, o los distintos tipos de refuerzo que se puede proporcionar a los usuarios para mejorar su interacción y experiencia. Los resultados obtenidos permiten concluir que, aunque no es una práctica habitual en la literatura, los entornos software deben emplear el Descubrimiento de Conocimiento y los principios de los sistemas guiados por datos para analizar y responder apropiadamente a las acciones, deseos y comportamientos de los usuarios y para mejorar o soportar su interacción. Al adoptar el Descubrimiento de Conocimiento y los principios basados en datos, los sistemas software deben extender sus arquitecturas para poder afrontar los retos relacionados con la Interacción Persona-Ordenador. Finalmente, para ser capaces de responder a los problemas actuales en relación a la interacción y experiencia de los usuarios, e intentando automatizar la respuesta del software a los deseos, acciones y comportamientos de los usuarios, los sistemas interactivos deben adoptar comportamientos inteligentes a través de los procedimientos y técnicas relacionadas con la Inteligencia Artificial.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.