Persona: Manjarrés Riesco, Ángeles
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amanja@dia.uned.es
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0000-0001-5441-3642
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Manjarrés Riesco
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Ángeles
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Publicación An Inclusive and Sustainable Artificial Intelligence Strategy for Europe Based on Human Rights(Institute of Electrical and Electronics Engineers (IEEE), 2021-03-15) Fernández Aller, Celia; Manjarrés Riesco, Ángeles; Pastor Escudero, David; Pickin, Simon James; Fernández de Velasco, Artuto; Ausín Díez, Txetxu; Ministerio de Economía, Comercio y Empresa (MINECO); Comunidad de Madrid (CAM)The United Nations (UN) 2030 Agenda and other movements toward setting global goals such as the Paris Agreement and the European Green Deal/U.S. Green New Deal are laying the groundwork for a transformation beyond purely market-based economics toward sustainability and inclusiveness [1], in which technological innovation and, in particular, artificial intelligence (AI) can play a central role. The European Union (EU) is committed to the 2030 Agenda and the sustainable development goals (SDGs), which the UN itself has recognized cannot be achieved without a people-focused, science-based, digital revolution [2]. This commitment to the 2030 Agenda should entail promoting an inclusive and sustainable AI strategy, rather than a strategy with a narrow focus on competitiveness [3], [4]. In order for AI to contribute to achieving the SDGs, a systemic approach to the development of AI solutions is required [5]-[9]. Conversely, the SDGs provide an ideal framework to test the desirability of AI solutions [10]. Europe's multicultural character and its framework of international collaboration give it a head start toward becoming a global reference in the promotion of an inclusive and sustainable AI. Sharing the experiences and practices of such a European AI could make a significant contribution to achieving the SDGsPublicación DISCO PAL: Diachronic Spanish sonnet corpus with psychological and affective labels(Springer, 2021-10-13) Barbado, Alberto; Fresno Fernández, Víctor Diego; Manjarrés Riesco, Ángeles; Ros Muñoz, SalvadorNowadays, there are many applications of text mining over corpora from different languages. However, most of them are based on texts in prose, lacking applications that work with poetry texts. An example of an application of text mining in poetry is the usage of features derived from their individual words in order to capture the lexical, sublexical and interlexical meaning, and infer the General Affective Meaning (GAM) of the text. However, even though this proposal has been proved as useful for poetry in some languages, there is a lack of studies for both Spanish poetry and for highly-structured poetic compositions such as sonnets. This article presents a study over an annotated corpus of Spanish sonnets, in order to analyse if it is possible to build features from their individual words for predicting their GAM. The purpose of this is to model sonnets at an affective level. The article also analyses the relationship between the GAM of the sonnets and the content itself. For this, we consider the content from a psychological perspective, dentifying with tags when a sonnet is related to a specific term. Then, we study how GAM changes according to each of those psychological terms. The corpus used contains 274 Spanish sonnets from authors of different centuries, from fifteenth to nineteenth. This corpus was annotated by different domain experts. The experts annotated the poems with affective and lexico-semantic features, as well as with domain concepts that belong to psychology. Thanks to this, the corpus of sonnets can be used in different applications, such as poetry recommender systems, per- sonality text mining studies of the authors, or the usage of poetry for therapeutic purposes.Publicación Using LSTM to Identify Help Needs in Primary School Scratch Students(MDPI, 2023-11-30) Imbernón Cuadrado, Luis Eduardo; Manjarrés Riesco, Ángeles; Paz López, Félix de lafirst-in-class distance calculation method for block-based programming languages has been used in a Long Short-Term Memory (LSTM) model, with the aim of identifying when a primary school student needs help while he/she carries out Scratch exercises. This model has been trained twice: the first time taking into account the gender of the students, and the second time excluding it. The accuracy of the model that includes gender is 99.2%, while that of the model that excludes gender is 91.1%. We conclude that taking into account gender in training this model can lead to overfitting, due to the under-representation of girls among the students participating in the experiences, making the model less able to identify when a student needs help. We also conclude that avoiding gender bias is a major challenge in research on educational systems for learning computational thinking skills, and that it necessarily involves effective and motivating gender-sensitive instructional design.Publicación Virtual Service-Learning in Higher Education. A Theoretical Framework for Enhancing its Development(Frontiers Media, 2021-03-08) García Gutiérrez, Juan; Ruiz Corbella, Marta; Manjarrés Riesco, ÁngelesThe last decade have witnessed the unprecedented development of information and communication technologies. This has, in turn, enabled the growth and development of other sectors, such as, for example, that of distance and on-line learning. In this context of technological expansion in education it is appropriate to reflect pedagogically about technological resources and their educational purpose. That is, how to deploy the available technological resources and media in a fashion consistent with the desired educational objectives and aims. Virtual Service-Learning has emerged as a particular modality of this methodology that combines and reinforces two elements: technology applied to education and service as a pedagogic tool. This format then, reveals itself as an appropriate methodology through which to channel both technical and pedagogic innovation. In this work and, taking as reference a study focusing on two virtual Service-Learning projects, we will address the construction of a theoretical framework that will allow us to understand and improve the development of these practices through this pedagogic modality.Publicación Requirements Elicitation Based on Psycho-Pedagogical Theatre for Context-Sensitive Affective Educational Recommender Systems(Institute of Electrical and Electronics Engineers (IEEE), 2023-07-20) Belver Fernández, Marcos; Manjarrés Riesco, Ángeles; Barbarelli, Alejandra; Agencia Estatal de Investigación (AEI)Educational Recommender Systems (ERSs), intelligent tutoring systems that adapt their pedagogical recommendations to each student, are becoming increasingly common. Context-Sensitive Affective Educational Recommender Systems (CSAERSs) personalize the recommendations according to a learning context with multiple dimensions, including the affective dimension and the personality traits of the user. To date, in the field of educational technology, there is little or no research that focuses on offering context-sensitive, personalized, psycho-pedagogical affective support to distance-learning students in real time. Nor do there seem to be any proposals for approaches to the knowledge engineering (term which encompasses knowledge acquisition and knowledge representation) of these systems, in which the relation between the user and his or her context is crucial. There is little work on a systematic approach to the requirements-elicitation phase and to the use of ontologies in the development and validation of ERSs, in general, and CSAERSs, in particular. In this article, we report on a student-centred requirements-elicitation methodology that uses psycho-pedagogical theatre in combination with student surveys. We then illustrate its application in the design and validation of an ontology, together with a semantic-similarity function, that could serve as the nucleus of a CSAERS.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 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 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 AI4Eq: For a True Global Village Not for Global Pillage(Institute of Electrical and Electronics Engineers (IEEE), 2021-03-15) Manjarrés Riesco, Ángeles; Pickin, Simon James; Artaso, Miguel A; Gibbons, Elizabeth; Ministerio de Economía, Comercio y Empresa (MINECO); Comunidad de Madrid (CAM)The last few years have seen a large number of initiatives on artificial intelligence (AI) ethics: intergovernmental-institution initiatives such as “Ethics Guidelines for Trustworthy AI” from the high-level expert group on AI of the European Commission [1] or the Organisation for Economic Cooperation and Development (OECD) Council Recommendation on Artificial Intelligence [2], government initiatives such as that of the U.K. Parliament Select Committee on Artificial Intelligence [3], industry initiatives on AI ethical codes such as those of Google, IBM, Microsoft, and Intel, academic initiatives such as the Montreal declaration for the responsible development of AI [4], the Stanford University 100 Year Study on AI [5] or the Alan Turing Institute's “Understanding Artificial Intelligence Ethics and Safety” [6], and finally professional body initiatives such as the IEEE Global Initiative on Ethics of Autonomous/Intelligent Systems (A/IS) [7]. These initiatives, while acknowledging the potential of A/IS technologies to contribute to global socioeconomic solutions, highlight the increasing challenges posed by these technologies in the ethical, moral, legal, humanitarian, and sociopolitical domains.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.