Persona:
González Boticario, Jesús

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ORCID
0000-0003-4949-9220
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González Boticario
Nombre de pila
Jesús
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2001 - 200932010 - 20143
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Nivel de acceso: info:eu-repo/semantics/openAccess × search.filters.applied.f.itemDepartamento: Inteligencia Artificial ×

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Mostrando 1 - 7 de 7
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    An Approach for an Affective Educational Recommendation Model
    (Springer, 2014-01-01) Santos, Olga C.; González Boticario, Jesús; Manjarrés Riesco, Ángeles
    There 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.
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    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, Rafael
    Alf, entorno de trabajo, comunidades virtuales, enseñanza superior
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    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, Sergio
    There 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.
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    Fundamentos de la virtualización : experiencia en investigación y formación del profesorado
    (Facultad de Ciencias UNED, 2001-02-23) González Boticario, Jesús
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    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, Raúl
    There 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.
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    Improving autonomous vehicle automation through human-system interaction
    (EUROSIS) Fernandez Matellan, Raul; Martin Gomez, David; Tena Gago, David; Puertas Ramírez, David; González Boticario, Jesús
    Self-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.