Persona: González Boticario, Jesús
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González Boticario
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Jesús
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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 Changing technological infrastructure and services in higher education : towards a student-centred approach(2005-02-12) González Boticario, JesúsPublicación Fundamentos de la virtualización : experiencia en investigación y formación del profesorado(Facultad de Ciencias UNED, 2001-02-23) González Boticario, JesúsPublicación A domain-independent, transferable and timely analysis approach to assess student collaboration(World Scientific Publishing, 2013) Rodríguez Anaya, Antonio; González Boticario, JesúsCollaborative learning environments require intensive, regular and frequent analysis of the increasing amount of interaction data generated by students to assess that collaborative learning takes place. To support timely assessments that may benefit students and teachers the method of analysis must provide meaningful evaluations while the interactions take place. This research proposes machine learning-based techniques to infer the relationship between student collaboration and some quantitative domain-independent statistical indicators derived from large-scale evaluation analysis of student interactions. This paper (i) compares a set of metrics to identify the most suitable to assess student collaboration, (ii) reports on student evaluations of the metacognitive tools that display collaboration assessments from a new collaborative learning experience and (iii) extends previous findings to clarify modeling and usage issues. The advantages of the approach are: (1) it is based on domain-independent and generally observable features, (2) it provides regular and frequent data mining analysis with minimal teacher or student intervention, thereby supporting metacognition for the learners and corrective actions for the teachers, and (3) it can be easily transferred to other e-learning environments and include transferability features that are intended to facilitate its usage in other collaborative and social learning tools.Publicación 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 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 Exploring arduino for building educational context-aware recommender systems that deliver affective recommendations in social ubiquitous networking environments(Springer, 2014-10-10) Santos, Olga C.; González Boticario, JesúsOne of the most challenging context features to detect when making recommendations in educational scenarios is the learner’s affective state. Usually, this feature is explicitly gathered from the learner herself through questionnaires or self-reports. In this paper, we analyze if affective recommendations can be produced with a low cost approach using the open source electronics prototyping platform Arduino together with corresponding sensors and actuators. TORMES methodology (which combines user centered design methods and data mining techniques) can support the recommendations elicitation process by identifying new recommendation opportunities in these emerging social ubiquitous networking scenarios.Publicación Should Conditional Self-Driving Cars Consider the State of the Human Inside the Vehicle?(ACM, 2021-06-22) Puertas Ramírez, David; Serrano Mamolar, Ana; Martín Gómez, David; González Boticario, JesúsAutonomous vehicles with conditional automation are said to be the next step in the development of self-driving cars. The human driver still performs a critical role in them, by taking over the control of the vehicle if prompted. As the technology is still facing pending challenges, the human drivers are also required to be able to detect and react in case of Autonomous Drive System (ADS) malfunctions. Within this context, in this work we argue that to assure safety during autonomous operation the user state should be measured all the time, which is intended to support a ”fallback ready state”. From an in-depth literature review, this article identifies the human factors involved in the aforementioned ”fallback ready state” that affect the personalization of human-vehicle interaction.Publicación Fusion of physiological signals for modeling driver awareness levels in conditional autonomous vehicles using semi-supervised learning(IEEE, 2024-10-11) Fernandez Matellan, Raul; Puertas Ramírez, David; Martín Gómez, David; González Boticario, JesúsThe evolution of autonomous vehicles (AVs) requires a paradigm shift towards the integration of human factors to improve safety and efficiency at levels 2,3 and 4 of automation. This paper presents a comparison of three different fusion technologies (Low-Level fusion, Medium-Level fusion, and a hybrid fusion), highlighting the critical role of multimodal data integration and semi-supervised learning in predicting and adapting to levels of driver awareness. Our approach uses semi-supervised learning to deal with the data labelling problem, using unlabelled data to train an autoencoder and sparsely labelled data to train a 4-state classifier. Our model facilitates the fusion of data from different physiological signals, including skin electrodermal activity, heart rate, body temperature and acceleration. Using real driving data, the Medium-Level fusion approach gives the best performance, achieving 84% accuracy in predicting situations where the user may not be aware enough to take control of the vehicle. This research highlights the essential nature of fusion technologies to create adaptive and user-centred AV systems.Publicación Comparison of physiological data acquisition for modeling of drivers in autonomous vehicles(Springer Nature, 2025-04-24) Fernandez Matellan, Raul; Puertas Ramírez, David; Martín Gómez, David; González Boticario, JesúsHumans can undergo rapid emotional changes and these changes can significantly affect their ability to perform tasks. Consequently, when we develop Human-Centred Symbiotic Artificial Intelligence (HCSAI) systems to support the interaction between autonomous systems and drivers, the intelligent system controlling the vehicle must adapt to the state of the user. This symbiotic relationship highlights the importance of collaboration and cooperation between humans and agents of artificial intelligence (AI). In the field of Autonomous Vehicles (AV), measurements must be made using non-invasive devices that do not interfere with the driving task. We have therefore used wristbands to measure physiological signals. This comparison is used to select the right equipment for setting up user modelling in different levels of autonomous vehicles. We compared the accuracy, precision and ease of use of three different wristbands: Fitbit Sense2, Empatica E4 and Emotibit. We tested the performance of the bands in two different driving scenarios: SAE Level 4 environment using autonomous golf carts (iCab), and a real-world SAE Level 2 driving environment in Scotland using a Toyota Prius equipped with Comma OpenPilot technology. The Fitbit Sense2 does not allow researchers to access raw data. The Emotibit and the Empatica E4 are designed for research, so they provide access to raw data, while the Empatica E4 is easier to use than the Emotibit. The comparison calls for the development of open source codes that will facilitate integration with different operating systems and other devices, as well as an easy way to use the devices in real time.