Persona: Santos, Olga C.
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ocsantos@dia.uned.es
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0000-0002-9281-4209
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Santos
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Olga C.
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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 Inclusive personalized e-Learning based on affective adaptive support(Springer Nature, 2013) Salmeron Majadas, Sergio; Santos, Olga C.; González Boticario, JesúsEmotions and learning are closely related. In the PhD research presented in this paper, that relation has to be taken advantage of. With this aim, within the framework of affective computing, the main goal proposed is modeling learner’s affective state in order to support adaptive features and provide an inclusive personalized e-learning experience. At the first stage of this research, emotion detection is the principal issue to cope with. A multimodal approach has been proposed, so gathering data from diverse sources to feed data mining systems able to supply emotional information is being the current ongoing work. On the next stages, the results of these data mining systems will be used to enhance learner models and based on these, offer a better e-learning experience to improve learner’s results.Publicación Supporting growers with recommendations in redvides: some human aspects involved(Springer Nature, 2014-10-10) Santos, Olga C.; Salmeron Majadas, Sergio; González Boticario, JesúsThis 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.Publicación Harmonizing Ethical Principles: Feedback Generation Approaches in Modeling Human Factors for Assisted Psychomotor Systems(Association for Computing Machinery, Inc, 2024-06-28) Portaz Collado, Miguel Ángel; Manjarrés Riesco, Ángeles; Santos, Olga C.; Agencia Estatal de Investigación (España)As the demand for personalized and adaptive learning experiences increase, there is a urgent need for providing effective feedback mechanisms within critical systems, such as in psychomotor learning systems. This proposal introduces an approach for the integration of retrieval-augmented generation tools to provide comprehensive and insightful feedback to users. By combining the strengths of retrieval-based techniques and generative models, these tools offer the potential to enhance learning outcomes by delivering tailored feedback that is both informative and engaging. The proposal also emphasises the importance of incorporating explainability and transparency concepts. Following the hybrid intelligence paradigm it is possible to ensure that the feedback provided by these tools is not only accurate but also understandable to humans. This approach fosters trust and promotes a deeper understanding of the psychomotor learning process, empowering users and facilitators to make informed decisions about the psychomotor learning path. The hybrid intelligence paradigm, which combines the strengths of both human and artificial intelligence, plays a crucial role in the deployment of these solutions. By taking advantage of the cognitive capabilities of human experts alongside the computational power of artificial intelligence algorithms, it is possible to offer personalised feedback that takes into account both technical accuracy and pedagogical effectiveness. Through these collaborative efforts it is also possible to create learning environments that are inclusive, adaptable, and beneficial to lifelong learning. In conclusion, this proposal introduces retrieval-augmented generation tools for providing feedback in psychomotor learning systems, which represents a significant step towards in its personalization, and whose ethical implications align with the new regulations on the implementation of intelligent technologies in critical systems.Publicación 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.Publicación Exploring cognitive models to augment explainability in Deep Knowledge Tracing(ACM Digital Library, 2023-06-13) Labra, Concha; Santos, Olga C.; https://orcid.org/0009-0004-3499-6106; https://orcid.org/0000-0002-9281-4209Adaptive learning systems allow a personalized adaptation based on the characteristics of the student. Tracing the progress of knowledge and skills during the learning process through cognitive models is essential so that these systems can make appropriate decisions when carrying out personalization. This is the objective of Knowledge Tracing, which studies how to infer a cognitive model from the answers given to a sequence of questions or exercises. The incorporation of Deep Learning techniques in this field has given rise to Deep Knowledge Tracing (DKT) which usually has excellent predictive outcomes. The problem is that this increase in accuracy comes with a lack of explainability since Deep Learning models can be considered black boxes from which it is difficult to build interpretations or explanations. By contrast, traditional Knowledge Tracing methods are based on underlying learning models and provide a solid basis for explainability. In this paper we describe an ongoing research to build DKT models with a good trade-off between accuracy and explainability. To this end, we propose to use a loss function based on a mixup approach where the ground truth is a mix between the dataset labels and the predictions of a surrogate explainable model. The approach has potential to improve, not only explainability through the use of the surrogate, but also accuracy thanks to regularization effects. We will validate the approach by exploring, for different cognitive models, the trade-off curve that is obtained by plotting accuracy against explainability for different mixup values.Publicación 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 Towards Human-Centric Psychomotor Recommender Systems(Association for Computing Machinery, Inc, 2023-06-16) Portaz Collado, Miguel Ángel; Manjarrés Riesco, Ángeles; Santos, Olga C.; Agencia Estatal de Investigación (España)Recommender Systems have been developed for years to guide the interaction of the users with systems in very diverse domains where information overload exists aimed to help humans in decision making. In order to better support the humans, the more the system knows about the user, the more useful recommendations the user can receive. In this sense, there is a need to explore which are the intrinsic human aspects that should be taken into account in each case when building the user models that provide the personalization. Moreover, there is a need to define and apply methodologies, guidelines and frameworks to develop this kind of systems in order to tackle the challenges of current artificial intelligence applications including issues such as ethics, transparency, explainability and sustainability. For our research, we have chosen the psychomotor domain. To provide some insights into this problem, in this paper we present the research directions we are exploring to apply a human-centric approach when developing the iBAID (intelligent Basket AID) psychomotor system, which aims to recommend the physical activities and movements to perform when training in basketball, either to improve the technique, to recover from an injury or even to keep active when getting older.Publicación AI-Powered Psychomotor Learning through basketball practice: Opportunities and Challenges(Springer Nature Switzerland, 2024-06-23) Portaz Collado, Miguel Ángel; Cabestrero Alonso, Raúl; Quirós Expósito, Pilar; Santos, Olga C.; Santoianni, Flavia; Giannini, Gianluca; Ciasullo, AlessandroThis chapter delves into the dynamic landscape of systems designed for the human centered learning of motor skills, with a primary focus on their application in the context of basketball. As technology continues to advance, opportunities emerge for innovative solutions that enhance skill acquisition, performance analysis, and overall proficiency in sports. The opportunities presented by cutting-edge systems, such as sensor-based technologies, offer new dimensions for sport psychologist, coaches, athletes and learners alike. These systems can provide real-time feedback and personalized training regimens, revolutionizing the traditional approaches to skill development. Specifically within the realm of basketball, this chapter addresses how these technologies can enhance shooting skills by improving spatial agility, initial burst speed, and directional responsiveness. However, along with these opportunities come significant challenges, such as the adaptability of technology across diverse skill levels, the need for robust data security in performance analytics, and the potential over-reliance on technology to the detriment of fundamental coaching. Balancing the integration of technology with the human centered elements is crucial to ensure that these systems genuinely enhance the learning experience without diminishing the importance of hands-on coaching and the inherent nuances of the sport.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.