Persona:
González Boticario, Jesús

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0000-0003-4949-9220
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González Boticario
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Jesús
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2012 - 201913
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Fecha de finalización: 2019 × Fecha de inicio: 2010 ×

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Mostrando 1 - 10 de 13
  • Miniatura
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    BIG-AFF: Exploring low cost and low intrusive infrastructures for affective computing in secondary schools
    (ACM, 2017-07-09) González Boticario, Jesús; Santos, Olga C.; Cabestrero Alonso, Raúl; Quirós Expósito, Pilar; Salmeron Majadas, Sergio; Uría Rivas, Raúl; Arevalillo Herráez, Miguel; Ferri, Francesc J.
    Recent research has provided solid evidence that emotions strongly affect motivation and engagement, and hence play an important role in learning. In BIG-AFF project, we build on the hypothesis that ``it is possible to provide learners with a personalised support that enriches their learning process and experience by using low intrusive (and low cost) devices to capture affective multimodal data that include cognitive, behavioural and physiological information''. In order to deal with the affect management complete cycle, thus covering affect detection, modelling and feedback, there is lack of standards and consolidated methodologies. Being our goal to develop realistic affect-aware learning environments, we are exploring different approaches on how these can be supported by either by traditional non-intrusive interaction sources or low intrusive and inexpensive sensing devices. In this work we describe the main issues involved in two user studies carried out with high school learners, highlight some open problems that arose when designing the corresponding experimental settings. In particular, the studies involved varied nature of information sources and each focused on one of the approaches. Our experience reflects the need to develop an extensive knowledge about the organization of this type of experiences that consider user-centric development and evaluation methodologies.
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    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/0000-0002-9281-4209
    There 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.
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    Aprendizaje basado en tareas compartidas: una metodología consolidada para fomentar la involucración en el aprendizaje "online"
    (Universidad Nacional de Educación a Distancia (España). Instituto Universitario de Educación a Distancia (IUED), 2019) González Boticario, Jesús
    Cualesquiera que sean los medios y circunstancias presentes en los procesos de enseñanza y aprendizaje, un objetivo siempre presente es lograr un aprendizaje en el que el estudiante se involucre realmente, a través de su trabajo y el de otros con los que comparte el proceso. Desde 2001 hasta la fecha se ha venido puliendo una estrategia de aprendizaje en cursos virtuales basada en tareas y en colaboración, aplicada en una gran variedad de escenarios, incluyendo asignaturas, cursos esporádicos y MOOC s. El planteamiento se basa en potenciar la adquisición de competencias de colaboración e interacción social. Se aprende construyendo documentación compartida y elaborando soluciones a partir de situaciones y casos reales de aplicación relacionados con la materia. Todo se realiza a través de la red y las fuentes de información abiertas son la base del proceso. Se resumen aquí algunos aspectos de interés en asignaturas de grado, máster y curso orientado a la investigación y doctorado. Los resultados son variados precisamente por la propia naturaleza de dichos cursos, siendo las asignaturas de grado en las que se observa mayor éxito.
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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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    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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    A domain-independent, transferable and timely analysis approach to assess student collaboration
    (World Scientific Publishing, 2013) Rodríguez Anaya, Antonio; González Boticario, Jesús
    Collaborative 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.
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    Supporting growers with recommendations in redvides: some human aspects involved
    (Springer Nature, 2014-10-10) Santos, Olga C.; Salmeron Majadas, Sergio; González Boticario, Jesús
    This paper discusses some human aspects that are to be considered when designing recommendations for RedVides, a cloud based networking environment that collects the status of the crop with sensors and can take decisions through corresponding actuators. The goal behind is to support growers in decision making processes, which can be benefited from collaborations among growers and with other stakeholders.
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    Some insights into the impact of affective information when delivering feedback to students
    (Taylor and Francis Group, 2018-07-26) Cabestrero Alonso, Raúl; Quirós Expósito, Pilar; Santos, Olga C.; Salmeron Majadas, Sergio; Uría Rivas, Raúl; González Boticario, Jesús; Arnau, David; Arevalillo Herráez, Miguel; Ferri, Francesc J.
    The relation between affect-driven feedback and engagement on a given task has been largely investigated. This relation can be used to make personalised instructional decisions and/or modify the affect content within the feedback. However, although it is generally assumed that providing encouraging feedback to students should help them adopt a state of flow, there are instances where those messages might result counterproductive. In this paper, we present a case study with 48 secondary school students using an Intelligent Tutoring System for arithmetical word problem solving. This system, which makes some common assumptions on how to relate affective state with performance, takes into account subjective (user's affective state) and objective information (previous problem performance) to decide the upcoming difficulty levels and the type of affective feedback to be delivered. Surprisingly, results revealed that feedback was more effective when no emotional content was included, and lead to the conclusion that purely instructional and concise help messages are more important than the emotional reinforcement contained therein. This finding shows that this is still an open issue. Different settings present different constraints generating related compounding factors that affect obtained results. This research confirms that new approaches are required to determine when, how and where affect-driven feedback is needed. Affect-driven feedback, engagement and their mutual relation have been largely investigated. Student's interactions combined with their emotional state can be used to make personalised instructional decisions and/or modify the affect content within the feedback, aiming to entice engagement on the task. However, although it is generally assumed that providing encouraging feedback to the students should help them adopt a state of flow, there are instances where those encouraging messages might result counterproductive. In this paper, we analyze these issues in terms of a case study with 48 secondary school students using an Intelligent Tutoring System for arithmetical word problem solving. This system, which makes some common assumptions on how to relate affective state with performance, takes into account subjective (user's affective state) and objective (previous problem performance) information to decide the difficulty level of the next exercise and the type of affective feedback to be delivered. Surprisingly, findings revealed that feedback was more effective when no emotional content was included in the messages, and lead to the conclusion that purely instructional and concise help messages are more important than the emotional reinforcement contained therein. This finding, which coincides with related work, shows that this is still an open issue. Different settings present different constraints and there are related compounding factors that affect obtained results, such as the message's contents and their target, how to measure the effect of the message on engagement through affective variables considering other issues involved, and to what extent engagement can be manipulated solely in terms of affective feedback. The contribution here is that this research confirms that new approaches are needed to determine when, how and where affect-driven feedback is needed. In particular, based on our previous experience in developing educational recommender systems, we suggest the combination of user-centred design methodologies with data mining methods to yield a more effective feedback.
  • Miniatura
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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.
  • Miniatura
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    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, Jesús
    One 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.