Persona: González Boticario, Jesús
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González Boticario
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Jesús
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Publicación Changing technological infrastructure and services in higher education : towards a student-centred approach(2005-02-12) González Boticario, JesúsPublicación A Machine Learning Approach to Leverage Individual Keyboard and Mouse Interaction Behavior From Multiple Users in Real-World Learning Scenarios(Browse Journals & Magazines, 2018) Salmeron Majadas, Sergio; Baker, Ryan S.; Santos, Olga C.; González Boticario, Jesús; https://orcid.org/0000-0002-0544-0887; https://orcid.org/0000-0002-3051-3232; https://orcid.org/0000-0002-9281-4209There is strong evidence that emotions influence the learning process. For this reason, we explore the relevance of individual and general mouse and keyboard interaction patterns in real-world settings while learners perform free text tasks. To this end, we have modeled users' mouse movements and keystroke dynamics with data mining techniques, building on previous related research and extending it in terms of some critical modeling issues that may have an impact on detection results. Inspired by practice in affective computing where physiological sensors are used, we argue for the creation of an interaction baseline model, as a reference point in the way how learners interact with the keyboard and mouse. To make the proposed affective model feasible, we have adopted a simplified 2-D self-labeling approach for labeling the users' affective state. Our approach to affect detection improves results when there is a small amount of data instances available and does not require additional affect-oriented tasks from the learners. Specifically, learners are only asked to self-reflect their emotional state after finishing the tasks and immediately selecting two values in the affect scale. The approach we have followed aims to distill two types of interaction patterns: 1) within-subject patterns (from a single participant) and 2) between-subject patterns (across all participants). Doing this, we aim to combine both the approaches as modeling factors, thus taking advantage of individual and general interaction patterns to predict affect.Publicación An Approach for an Affective Educational Recommendation Model(Springer, 2014-01-01) Santos, Olga C.; González Boticario, Jesús; Manjarrés Riesco, ÁngelesThere is agreement in the literature that affect influences learning. In turn, addressing affective issues in the recommendation process has shown their ability to increase the performance of recommender systems in non-educational scenarios. In our work, we combine both research lines and describe the SAERS approach to model affective educational recommendations. This affective recommendation model has been initially validated with the application of the TORMES methodology to specific educational settings. We report 29 recommendations elicited in 12 scenarios by applying this methodology. Moreover, a UML formalized version of the recommendations model which can describe the recommendations elicited is presented in the paper.Publicación Alf : un entorno abierto para el desarrollo de comunidades virtuales de trabajo y cursos adaptados a la educación superior(2005-02-23) Raffenne, Emmanuelle; Aguado, M.; Arroyo, D.; Cordova, M. A.; Guzmán Sánchez, José Luis; Hermira, S.; Ortíz, J.; Pesquera, A.; Morales, R.; Romojaro Gómez, Héctor; Valiente, S.; Carmona, G.; Tejedor, D.; Alejo, J. A.; García Saiz, Tomás; González Boticario, Jesús; Pastor Vargas, RafaelAlf, entorno de trabajo, comunidades virtuales, enseñanza superiorPublicación MAMIPEC - Affective modeling in inclusive personalized educational scenarios(IEEE Technical Committee on Learning Technology,, 2012) Santos, Olga C.; González Boticario, Jesús; Arevalillo Herráez, Miguel; Saneiro Silva, María del Mar; Cabestrero Alonso, Raúl; Campo Adrián, María del Campo; Manjarrés Riesco, Ángeles; Moreno Clarí, Paloma; Quirós Expósito, Pilar; Salmeron Majadas, SergioThere is agreement in the literature that affect influences learning. In turn, addressing affective issues in the recommendation process has shown their ability to increase the performance of recommender systems in non-educational scenarios. In our work, we combine both research lines and describe the SAERS approach to model affective educational recommendations. This affective recommendation model has been initially validated with the application of the TORMES methodology to specific educational settings. We report 29 recommendations elicited in 12 scenarios by applying this methodology. Moreover, a UML formalized version of the recommendations model which can describe the recommendations elicited is presented in the paper.Publicación Fundamentos de la virtualización : experiencia en investigación y formación del profesorado(Facultad de Ciencias UNED, 2001-02-23) González Boticario, JesúsPublicación Challenges for Inclusive Affective Detection in Educational Scenarios(Springer Nature, 2013) Santos, Olga C.; Rodríguez Ascaso, Alejandro; González Boticario, Jesús; Salmeron Majadas, Sergio; Quirós Expósito, Pilar; Cabestrero Alonso, RaúlThere exist diverse challenges for inclusive emotions detection in educational scenarios. In order to gain some insight about the difficulties and limitations of them, we have analyzed requirements, accommodations and tasks that need to be adapted for an experiment where people with different functional profiles have taken part. Adaptations took into consideration logistics, tasks involved and user interaction techniques. The main aim was to verify to what extent the same approach, measurements and technological infrastructure already used in previous experiments were adequate for inducing emotions elicited from the execution of the experiment tasks. In the paper, we discuss the experiment arrangements needed to cope with people with different functional profiles, which include adaptations on the analysis and results. Such analysis was validated in a pilot experiment with 3 visually impaired participants.Publicación Improving autonomous vehicle automation through human-system interaction(EUROSIS) Fernandez Matellan, Raul; Martin Gomez, David; Tena Gago, David; Puertas Ramírez, David; González Boticario, JesúsSelf-driving cars (a.k.a. Autonomous Vehicles) have many challenges to tackle before having them fully deployed in our roads and cities. A critical one, which has been somehow neglected till recently, is to consider the driver in the system-user loop of vehicle performance. The purpose here is to tackle some of the current pending challenges involved in scaling up the level of autonomy of these systems. We have designed two user-vehicle experiences in two different sites with a common methodology that serves as an umbrella to collect all features required to model the driver-user. These two sites allow us to contrast and fine-tune this modelling issue. The approach consists in following a Learning Apprentice approach, where both the user behaviour and the system behaviour are learned and improved in a symbiotic ecosystem. This paper focuses on discussing the advantages of this approach and the main issues that require further research.Publicación Impact of Physiological Signals Acquisition in the Emotional Support Provided in Learning Scenarios(MDPI, 2019-10-17) Uría Rivas, Raúl; Rodriguez Sanchez, Cristina; Santos, Olga C.; Vaquero, Joaquin; Jesus G. Boticario; González Boticario, Jesús; https://orcid.org/0000-0001-9243-2166; https://orcid.org/0000-0002-9281-4209; https://orcid.org/0000-0002-6976-0564Physiological sensors can be used to detect changes in the emotional state of users with affective computing. This has lately been applied in the educational domain, aimed to better support learners during the learning process. For this purpose, we have developed the AICARP (Ambient Intelligence Context-aware Affective Recommender Platform) infrastructure, which detects changes in the emotional state of the user and provides personalized multisensorial support to help manage the emotional state by taking advantage of ambient intelligence features. We have developed a third version of this infrastructure, AICARP.V3, which addresses several problems detected in the data acquisition stage of the second version, (i.e., intrusion of the pulse sensor, poor resolution and low signal to noise ratio in the galvanic skin response sensor and slow response time of the temperature sensor) and extends the capabilities to integrate new actuators. This improved incorporates a new acquisition platform (shield) called PhyAS (Physiological Acquisition Shield), which reduces the number of control units to only one, and supports both gathering physiological signals with better precision and delivering multisensory feedback with more flexibility, by means of new actuators that can be added/discarded on top of just that single shield. The improvements in the quality of the acquired signals allow better recognition of the emotional states. Thereof, AICARP.V3 gives a more accurate personalized emotional support to the user, based on a rule-based approach that triggers multisensorial feedback, if necessary. This represents progress in solving an open problem: develop systems that perform as effectively as a human expert in a complex task such as the recognition of emotional statesPublicación Stakeholder Perspectives on the Ethics of AI in Distance-Based Higher Education(Athabasca University, 2023) Holmes, Wayne; Iniesto, Francisco; Anastopoulou, Stamatina; González Boticario, Jesús; https://orcid.org/0000-0002-8352-1594; https://orcid.org/0000-0003-3946-3056; https://orcid.org/0000-0003-2550-2237Increasingly, Artificial Intelligence (AI) is having an impact on distance-based higher education, where it is revealing multiple ethical issues. However, to date, there has been limited research addressing the perspectives of key stakeholders about these developments. The study presented in this paper sought to address this gap by investigating the perspectives of three key groups of stakeholders in distance-based higher education: students, teachers, and institutions. Empirical data collected in two workshops and a survey helped identify what concerns these stakeholders had about the ethics of AI in distance-based higher education. A theoretical framework for the ethics of AI in education was used to analyse that data and helped identify what was missing. In this exploratory study, there was no attempt to prioritise issues as more, or less, important. Instead, the value of the study reported in this paper derives from (a) the breadth and detail of the issues that have been identified, and (b) their categorisation in a unifying framework. Together these provide a foundation for future research and may also usefully inform future institutional implementation and practice.