Irene Epifanio
Universitat Jaume I de Castelló, Matematiques, Faculty Member
- Visit www3.uji.es/~epifanioedit
This article analyses the actions carried out in Spanish universities to achieve gender equality in scientific-technical disciplines, where women are still under-represented and there is a low level of gender mainstreaming that can affect... more
This article analyses the actions carried out in Spanish universities to achieve gender equality in scientific-technical disciplines, where women are still under-represented and there is a low level of gender mainstreaming that can affect research and innovation. In order to diagnose the situation, a survey was carried out aimed at the equality units that form part of the Network of Gender Equality Units for University Excellence (RUIGEU). The survey consisted of thirteen questions relating to: actions to favour the access and permanence of women in the PECS areas (Physics, Engineering, Computer, Science), the recognition of student work carried out with a gender perspective, the valuation of teaching and research with a gender perspective, the visibility and recognition of female researchers and actions for effective equality. This survey was anonymous and was answered by 28 units. From the answers obtained, we can extract a low level of involvement of the universities in promoting equality in this area. Furthermore, the analysis of archetypes shows that only five of the universities that participated in this study are committed to equality in the scientific-technical field and carry out actions to achieve it. These results show, on the one hand, that it is possible to implement actions to promote equality in the scientific and technical field. On the other hand, the collaboration of other institutions (Ministry of Universities, National Agency for the Evaluation of Accreditation (ANECA) and Conference of Rectors of the Spanish University (Crue)) is necessary to promote equality in all universities.
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We introduce a novel exploratory technique, termed biarchetype analysis, which extends archetype analysis to simultaneously identify archetypes of both observations and features. This innovative unsupervised machine learning tool aims to... more
We introduce a novel exploratory technique, termed biarchetype analysis, which extends archetype analysis to simultaneously identify archetypes of both observations and features. This innovative unsupervised machine learning tool aims to represent observations and features through instances of pure types, or biarchetypes, which are easily interpretable as they embody mixtures of observations and features. Furthermore, the observations and features are expressed as mixtures of the biarchetypes, which makes the structure of the data easier to understand. We propose an algorithm to solve biarchetype analysis. Although clustering is not the primary aim of this technique, biarchetype analysis is demonstrated to offer significant advantages over biclustering methods, particularly in terms of interpretability. This is attributed to biarchetypes being extreme instances, in contrast to the centroids produced by biclustering, which inherently enhances human comprehension. The application of biarchetype analysis across various machine learning challenges underscores its value, and both the source code and examples are readily accessible in R and Python at https://github.com/aleixalcacer/JA-BIAA.
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In the academic world, there are also gender inequalities, which are especially visible in certain masculinized STEM areas, such as physics and mathematics. An essential factor in correcting these inequalities is the involvement of men,... more
In the academic world, there are also gender inequalities, which are especially visible in certain masculinized STEM areas, such as physics and mathematics. An essential factor in correcting these inequalities is the involvement of men, who should act as “allies” in the university setting. Likewise, as the literature shows us, we must offer models with gender-incongruent roles to break down stereotypes and promote non-traditional behaviors. However, to date, these actions have been especially directed toward women, who generally do not hold power and therefore have less possibility of promoting change. For the first time, this work compiles, in a single document, important male physicists and mathematicians who acted as allies of women throughout history. These examples can be presented to provide male references in the teaching of physics and mathematics at university (and high school). With this initiative, we intend to contribute to incorporating the gender perspective in university teaching, since, in practice, university professors are unaware of references of alternative masculinities in the academic world. We hope that this article will be the seed to recover hidden male allies in these and other scientific fields. This can help break down stereotypes, and contrary to previous actions, this work is especially directed toward men.
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Education is essential in achieving many Sustainable development goals (SDGs). We have surveyed the students of 17 university degrees on the SDGs in two Spanish universities, to better adjust to their concerns, preferences and needs and... more
Education is essential in achieving many Sustainable development goals (SDGs). We have surveyed the students of 17 university degrees on the SDGs in two Spanish universities, to better adjust to their concerns, preferences and needs and to discover their omissions and shortfalls. Students expressed their opinions from two perspectives: that of citizenship and that of the degree they are studying, identifying the goals to which they feel they can contribute. Results show a very high self-perception in their ability to contribute to the SDGs, with significant differences depending on the degree. The goals that students consider themselves most able to influence are gender equality, good health, peace, justice, and quality education. Many of them coincide with degrees related to the SDGs themselves, but others do not. The degrees in the ICT field have the lowest self-perceived knowledge about SDGs.
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Research Interests: Cognitive Science, Functional Analysis, Principal Component Analysis, Magnetic Resonance Imaging, Independent Component Analysis, and 15 moreMedicine, Multivariate Analysis, Functional data analysis, Hippocampus, Discriminant Analysis, Humans, Linear Discriminant Analysis, Female, Male, Clinical Sciences, Aged, Functional Principal Component Analysis, Alzheimer Disease, Neurosciences, and Cognition disorders
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El objetivo de este trabajo es describir cómo hemos integrado los objetivos de desarrollo sostenible (ODS) en diversas materias de matemáticas básicas, y especialmente de estadística. Esto es muy original, ya que los trabajos sobre cómo... more
El objetivo de este trabajo es describir cómo hemos integrado los objetivos de desarrollo sostenible (ODS) en diversas materias de matemáticas básicas, y especialmente de estadística. Esto es muy original, ya que los trabajos sobre cómo integrar la responsabilidad social en el currículo matemático y la pedagogía en matemáticas son muy escasos, especialmente en la docencia universitaria [1,2]. Además, el estudiantado universitario no es muy consciente de su posible contribución al logro de los ODS [3].
La integración de los ODS ha considerado tanto el currículo visible (contenidos) como el currículo oculto, así como la metodología de enseñanza y evaluación. Algunas de las acciones desarrolladas son las siguientes:
1) la humanización de los problemas de acuerdo con los ODS. Uno de los recursos creados se puede ver en [4], acerca de problemas estadísticos sobre los ODS con datos reales. Pero se diseñaron muchos ejercicios diferentes en diversas materias matemáticas que van desde estadística, matemática aplicada y álgebra: el alumnado estima su huella ecológica y realiza varios análisis estadísticos; trabajamos con derivadas calculando la velocidad a la que aumenta el consumo de plástico; utilizamos oraciones en lógica proposicional que rompen estereotipos, etc. Los ejercicios relacionados con los ODS nos permitieron reflexionar sobre estos temas en clase.
2) el desarrollo de proyectos estadísticos relacionados con los ODS. Cada estudiante selecciona un tema relacionado con los ODS, obtiene datos y los analiza. Los y las estudiantes lo encontraron muy útil para aprender, según una encuesta que realizamos.
3) la integración de la perspectiva de género en la docencia, siguiendo las pautas de [5]: uso de lenguaje inclusivo, metodologías activas (aprendizaje cooperativo mediante el método Jigsaw, aula invertida, etc.), visualización de grupos minorizados, etc.
En el curso 2021/22 obtuvimos mejores tasas de rendimiento en nuestras asignaturas (más de 10 puntos porcentuales) que en asignaturas similares de la misma titulación. Las y los estudiantes quedaron muy satisfechos con nuestra enseñanza según las encuestas oficiales universitarias.
Estos resultados resaltan los beneficios de integrar los ODS en la enseñanza universitaria de matemáticas. Esta conclusión concuerda con la literatura, ya que una forma de motivar al estudiantado, especialmente en carreras no matemáticas, es mostrar cómo la estadística y las matemáticas aparecen en su vida diaria y en temas sociales de gran interés [6]. Se incluyen sugerencias para su implementación en otras materias matemáticas.
La integración de los ODS ha considerado tanto el currículo visible (contenidos) como el currículo oculto, así como la metodología de enseñanza y evaluación. Algunas de las acciones desarrolladas son las siguientes:
1) la humanización de los problemas de acuerdo con los ODS. Uno de los recursos creados se puede ver en [4], acerca de problemas estadísticos sobre los ODS con datos reales. Pero se diseñaron muchos ejercicios diferentes en diversas materias matemáticas que van desde estadística, matemática aplicada y álgebra: el alumnado estima su huella ecológica y realiza varios análisis estadísticos; trabajamos con derivadas calculando la velocidad a la que aumenta el consumo de plástico; utilizamos oraciones en lógica proposicional que rompen estereotipos, etc. Los ejercicios relacionados con los ODS nos permitieron reflexionar sobre estos temas en clase.
2) el desarrollo de proyectos estadísticos relacionados con los ODS. Cada estudiante selecciona un tema relacionado con los ODS, obtiene datos y los analiza. Los y las estudiantes lo encontraron muy útil para aprender, según una encuesta que realizamos.
3) la integración de la perspectiva de género en la docencia, siguiendo las pautas de [5]: uso de lenguaje inclusivo, metodologías activas (aprendizaje cooperativo mediante el método Jigsaw, aula invertida, etc.), visualización de grupos minorizados, etc.
En el curso 2021/22 obtuvimos mejores tasas de rendimiento en nuestras asignaturas (más de 10 puntos porcentuales) que en asignaturas similares de la misma titulación. Las y los estudiantes quedaron muy satisfechos con nuestra enseñanza según las encuestas oficiales universitarias.
Estos resultados resaltan los beneficios de integrar los ODS en la enseñanza universitaria de matemáticas. Esta conclusión concuerda con la literatura, ya que una forma de motivar al estudiantado, especialmente en carreras no matemáticas, es mostrar cómo la estadística y las matemáticas aparecen en su vida diaria y en temas sociales de gran interés [6]. Se incluyen sugerencias para su implementación en otras materias matemáticas.
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The aim of this work is to describe how we have integrated the sustainable development goals (SDGs) in various basic mathematics subjects, and especially statistics. This is very original, since the works on how to integrate social... more
The aim of this work is to describe how we have integrated the sustainable development goals (SDGs) in various basic mathematics subjects, and especially statistics. This is very original, since the works on how to integrate social responsibility in the mathematical curriculum and pedagogy in mathematics are very scarce, especially in university teaching [1,2]. Furthermore, university students are not very aware about their possible contribution to the SDGs achievement [3]. The SDGs integration has considered both the visible curriculum (contents) and the hidden curriculum, as well as the teaching methodology and evaluation. Some of the developed actions are as follows: 1) the humanization of problems in accordance with the SDGs. One of the resources created can be seen in [4], regarding statistical problems about SDGs with real data. But many different exercises were designed in different mathematical subjects ranging from statistics, applied mathematics and algebra: students estimate their ecological footprints and make several statistical analyses; we work with derivatives by calculating the speed at which plastic consumption increases; we use sentences in propositional logic that break stereotypes, etc. The exercises related to SDGs allowed us to reflect on these issues in class. 2) the development of statistical projects related to the SDGs. Each student selects a topic related to SDGs, obtains data, and analyzes them. Students found it very useful for learning according to a survey. 3) the integration of the gender perspective in teaching, following the guidelines by [5]: using inclusive language, active methodologies (cooperative learning by Jigsaw method, flipped classroom, etc.), visualizing minorized groups, etc. In 2021/22, we obtained better pass rates in our subjects (more than 10 percentual points) than in similar subjects of the same degree. Students were highly satisfied with our teaching according to the official university surveys. These results highlight the benefits of integrating SDGs in university teaching in mathematics. This conclusion agrees with literature, since one way of motivating students, especially in non-mathematics degrees, is to show how statistics and math appears in their daily lives and in social issues of great interest [6]. Suggestions for implementation in other mathematical subjects are included.
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Curves are complex data. Tools for visualizing, exploring, and discovering the structure of a data set of curves are valuable. In this paper, we propose a scalable methodology to solve this challenge. On the one hand, we consider two... more
Curves are complex data. Tools for visualizing, exploring, and discovering the structure of a data set of curves are valuable. In this paper, we propose a scalable methodology to solve this challenge. On the one hand, we consider two distances in the shape and size space, one well-known distance and another recently proposed, which differentiate the contribution in shape and in size of the elements considered to compute the distance. On the other hand, we use archetypoid analysis (ADA) for the first time in elastic shape analysis. ADA is a recent technique in unsupervised statistical learning, whose objective is to find a set of archetypal observations (curves in this case), in such a way that we can describe the data set as convex combinations of these archetypal curves. This makes interpretation easy, even for non-experts. Archetypal curves or pure types are extreme cases, which also facilitates human understanding. The methodology is illustrated with a simulated data set and applied to a real problem. It is important to know the distribution of foot shapes to design suitable footwear that accommodates the population. For this purpose, we apply our proposed methodology to a real data set composed of foot contours from the adult Spanish population.
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The aim of ordinal classification is to predict the ordered labels of the output from a set of observed inputs. Interval-valued data refers to data in the form of intervals. For the first time, interval-valued data and interval-valued... more
The aim of ordinal classification is to predict the ordered labels of the output from a set of observed inputs. Interval-valued data refers to data in the form of intervals. For the first time, interval-valued data and interval-valued functional data are considered as inputs in an ordinal classification problem. Six ordinal classifiers for interval data and interval-valued functional data are proposed. Three of them are parametric, one of them is based on ordinal binary decompositions and the other two are based on ordered logistic regression. The other three methods are based on the use of distances between interval data and kernels on interval data. One of the methods uses the weighted k-nearest-neighbor technique for ordinal classification. Another method considers kernel principal component analysis plus an ordinal classifier. And the sixth method, which is the method that performs best, uses a kernel-induced ordinal random forest. They are compared with naïve approaches in an extensive experimental study with synthetic and original real data sets, about human global development, and weather data. The results show that considering ordering and interval-valued information improves the accuracy. The source code and data sets are available at https://github.com/aleixalcacer/OCFIVD.
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Fovea segmentation in fluorescein angiographies is a fundamental first task in any study of ocular diseases. The importance of fovea detection is due to the fact that the nearer the centre of the fovea a lesion is, the graver this lesion... more
Fovea segmentation in fluorescein angiographies is a fundamental first task in any study of ocular diseases. The importance of fovea detection is due to the fact that the nearer the centre of the fovea a lesion is, the graver this lesion is. The proposed method is based on B-snakes and uses a greedy algorithm to minimise an appropriate energy which accurately leads to a convenient characterisation of the boundary of the foveal zone. The first initialisation step, which consists of finding the most appropriate local minimum along with a procedure to construct an initial contour involving a region growing algorithm, leads to a convenient and robust initialisation of the proposed active contour model
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Research Interests: Cognitive Science, Mathematics, Computer Science, Algorithms, Computer Graphics, and 15 moreArtificial Intelligence, Nonlinear dynamics, Data Compression, Independent Component Analysis, Medicine, Image Representation, Image coding, Image compression, Computer Simulation, Image Reconstruction, Image Enhancement, Human Visual System, Electrical And Electronic Engineering, Linear System and Response, and Linear Transformation
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Learning Management Systems (LMS) are educational tools used to plan, implement and assess the learning-teaching procedure. LMS are continuously acquiring data from students in their learning progress when they interact with the platform.... more
Learning Management Systems (LMS) are educational tools used to plan, implement and assess the learning-teaching procedure. LMS are continuously acquiring data from students in their learning progress when they interact with the platform. In this work, we use statistically-based approaches to learn about student performance solely using data from Moodle LMS, which is widely used along the educational institutions around the world. Using logged data from several subjects and degrees, our results indicate that the number of interactions with the LMS is a good indicator of student performance and can potentially be used as a measure to track students' learning progress.
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The goal of this work is to segment high resolution images of natural landscapes into different cover types. With this aim, morphological texture descriptors are used in order to avoid the limitations of spectral features. Firstly, an... more
The goal of this work is to segment high resolution images of natural landscapes into different cover types. With this aim, morphological texture descriptors are used in order to avoid the limitations of spectral features. Firstly, an unsupervised texture segmentation method is presented, since no a priori information about the textures is supplied. Secondly, the same features are utilized in a supervised case, where the textures to detect have been previously determined.
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ABSTRACT Conventional optical flow techniques provide a motion description that may be redundant for a human viewer. Computational effort may be wasted describing ‘perceptually irrelevant motions’. This inefficient behavior may also give... more
ABSTRACT Conventional optical flow techniques provide a motion description that may be redundant for a human viewer. Computational effort may be wasted describing ‘perceptually irrelevant motions’. This inefficient behavior may also give rise to false alarms and noisy flows. To solve this problem, hierarchical optical flow techniques have been proposed. They start from a low resolution motion estimate and new motion information is locally added only in certain regions. However, new motion information should be added only if it is ‘perceptually relevant’. In this work we propose a definition of ‘perceptually relevant motion information’. This definition is based on the entropy of the image representation in the human cortex (Watson JOSA 87, Daugman IEEE T.Biom.Eng. 89): an increment in motion information is perceptually relevant if it contributes to decrease the entropy of the cortex representation of the prediction error. Numerical experiments (optical flow computation and flow-based segmentation) show that applying this definition to a particular hierarchical motion estimation algorithm, more robust and meaningful flows are obtained. http://www.uv.es/vista/vistavalencia/papers/vss_poster.eps
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Curves are complex data. Tools for visualizing, exploring, and discovering the structure of a data set of curves are valuable. In this paper, we propose a scalable methodology to solve this challenge. On the one hand, we consider two... more
Curves are complex data. Tools for visualizing, exploring, and discovering the structure of a data set of curves are valuable. In this paper, we propose a scalable methodology to solve this challenge. On the one hand, we consider two distances in the shape and size space, one well-known distance and another recently proposed, which differentiate the contribution in shape and in size of the elements considered to compute the distance. On the other hand, we use archetypoid analysis (ADA) for the first time in elastic shape analysis. ADA is a recent technique in unsupervised statistical learning, whose objective is to find a set of archetypal observations (curves in this case), in such a way that we can describe the data set as convex combinations of these archetypal curves. This makes interpretation easy, even for non-experts. Archetypal curves or pure types are extreme cases, which also facilitates human understanding. The methodology is illustrated with a simulated data set and appl...
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Curves are complex data. Tools for visualizing, exploring, and discovering the structure of a data set of curves are valuable. In this paper, we propose a scalable methodology to solve this challenge. On the one hand, we consider two... more
Curves are complex data. Tools for visualizing, exploring, and discovering the structure of a data set of curves are valuable. In this paper, we propose a scalable methodology to solve this challenge. On the one hand, we consider two distances in the shape and size space, one well-known distance and another recently proposed, which differentiate the contribution in shape and in size of the elements considered to compute the distance. On the other hand, we use archetypoid analysis (ADA) for the first time in elastic shape analysis. ADA is a recent technique in unsupervised statistical learning, whose objective is to find a set of archetypal observations (curves in this case), in such a way that we can describe the data set as convex combinations of these archetypal curves. This makes interpretation easy, even for non-experts. Archetypal curves or pure types are extreme cases, which also facilitates human understanding. The methodology is illustrated with a simulated data set and applied to a real problem. It is important to know the distribution of foot shapes to design suitable footwear that accommodates the population. For this purpose, we apply our proposed methodology to a real data set composed of foot contours from the adult Spanish population.
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SIQ007 (SIQ507): Anàlisi estadística de sistemesVídeo tutorial (11/12) sobre el funcionamiento del programa de simulación ARENA (Rockwell Software).Tutorial video (11/12) about how the simulation software ARENA works. Subtitles in English... more
SIQ007 (SIQ507): Anàlisi estadística de sistemesVídeo tutorial (11/12) sobre el funcionamiento del programa de simulación ARENA (Rockwell Software).Tutorial video (11/12) about how the simulation software ARENA works. Subtitles in English are availabl
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SIQ007 (SIQ507): Anàlisi estadística de sistemesVídeo tutorial (7/12) sobre el funcionamiento del programa de simulación ARENA (Rockwell Software).Tutorial video (7/12) about how the simulation software ARENA works. Subtitles in English... more
SIQ007 (SIQ507): Anàlisi estadística de sistemesVídeo tutorial (7/12) sobre el funcionamiento del programa de simulación ARENA (Rockwell Software).Tutorial video (7/12) about how the simulation software ARENA works. Subtitles in English are availabl
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IG23: Ampliació d' EstadísticaEnunciados de exámenes de problemas de cursos pasado
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Allocation of time use is important to develop appropriate policies, especially in terms of gender equality. Individual well-being depends on many factors, including how time is spent. Therefore, knowing and analysing the time use and... more
Allocation of time use is important to develop appropriate policies, especially in terms of gender equality. Individual well-being depends on many factors, including how time is spent. Therefore, knowing and analysing the time use and workload of academic staff is relevant for academic policy making. We analyse the responses of 703 Spanish academic staff regarding different activities of paid work and household work (unpaid). We use an innovative machine learning technique in this field, archetype analysis, which we introduce step by step while exploring our data. We identify five profiles, and we examine gender inequalities. The findings indicate that there is a higher prevalence of women in the profiles with a greater workload in household activities and teaching-related activities, but the prevalence is the same in the profile with a greater workload in research activities.
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Allocation of time use is important to develop appropriate policies, especially in terms of gender equality. Individual well-being depends on many factors, including how time is spent. Therefore, knowing and analysing the time use and... more
Allocation of time use is important to develop appropriate policies, especially in terms of gender equality. Individual well-being depends on many factors, including how time is spent. Therefore, knowing and analysing the time use and workload of academic staff is relevant for academic policy making. We analyse the responses of 703 Spanish academic staff regarding different
activities of paid work and household work (unpaid). We use an innovative machine learning technique in this field, archetype analysis, which we introduce step by step while exploring our data. We identify five profiles, and we examine gender inequalities. The findings indicate that there is a higher prevalence of women in the profiles with a greater workload in household activities and teaching-related activities, but the prevalence is the same in the profile with a greater workload in research activities.
activities of paid work and household work (unpaid). We use an innovative machine learning technique in this field, archetype analysis, which we introduce step by step while exploring our data. We identify five profiles, and we examine gender inequalities. The findings indicate that there is a higher prevalence of women in the profiles with a greater workload in household activities and teaching-related activities, but the prevalence is the same in the profile with a greater workload in research activities.
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IG23: Ampliació d' EstadísticaProblema resuelto de contraste de dos proporcione
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SIQ007 (SIQ507): Anàlisi estadística de sistemesVídeo tutorial (12/12) sobre el funcionamiento del programa de simulación ARENA (Rockwell Software).Tutorial video (12/12) about how the simulation software ARENA works. Subtitles in English... more
SIQ007 (SIQ507): Anàlisi estadística de sistemesVídeo tutorial (12/12) sobre el funcionamiento del programa de simulación ARENA (Rockwell Software).Tutorial video (12/12) about how the simulation software ARENA works. Subtitles in English are availabl
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IG23: Ampliació d' Estadístic
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IG23: Ampliació d' EstadísticaResolución de un problema sobre proporciones (intervalo de confianza, tamaño muestral
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IG23: Ampliació d' EstadísticaSe explica la resolución de 4 problemas de intervalos de confianza de diferencia de medias
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SIQ007 (SIQ507): Anàlisi estadística de sistemesVídeo tutorial (9/12) sobre el funcionamiento del programa de simulación ARENA (Rockwell Software).Tutorial video (9/12) about how the simulation software ARENA works. Subtitles in English... more
SIQ007 (SIQ507): Anàlisi estadística de sistemesVídeo tutorial (9/12) sobre el funcionamiento del programa de simulación ARENA (Rockwell Software).Tutorial video (9/12) about how the simulation software ARENA works. Subtitles in English are availabl
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Problem solving is a core element in mathematical learning. The reversal error in problem solving occurs when students are able to recognize the information in the statement of comparison word problems, but they reverse the relationship... more
Problem solving is a core element in mathematical learning. The reversal error in problem solving occurs when students are able to recognize the information in the statement of comparison word problems, but they reverse the relationship between two variables when building the equations. Functional magnetic resonance images were acquired to identify for the first time the neural bases associated with the reversal error. The neuronal bases linked to this error have been used as inputs in 13 classifiers to discriminate between reversal error and non-reversal error groups. We found brain activation in bilateral fronto-parietal areas in the participants who committed reversal errors, and only left fronto-parietal activation in those who did not, suggesting that the reversal error group needed a greater cognitive demand. Instead, the non-reversal error group seems to show that they have developed solid algebraic knowledge. Additionally, the results showed brain activation in the right mid...
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Problem solving is a core element in mathematical learning. The reversal error in problem solving occurs when students are able to recognize the information in the statement of comparison word problems, but they reverse the relationship... more
Problem solving is a core element in mathematical learning. The reversal error in problem solving occurs when students are able to recognize the information in the statement of comparison word problems, but they reverse the relationship between two variables when building the equations. Functional magnetic resonance images were acquired to identify for the first time the neural bases associated with the reversal error. The neuronal bases linked to this error have been used as inputs in 13 classifiers to discriminate between reversal error and non-reversal error groups. We found brain activation in bilateral fronto-parietal areas in the participants who committed reversal errors, and only left fronto-parietal activation in those who did not, suggesting that the reversal error group needed a greater cognitive demand. Instead, the non-reversal error group seems to show that they have developed solid algebraic knowledge. Additionally, the results showed brain activation in the right middle temporal gyrus when comparing the reversal error vs non-reversal error groups. This activation would be associated with the semantic processing which is required to understand the statement and build the equation. Finally, the classifier results show that the brain areas activated could be considered good biomarkers to help us identify competent solvers.
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Guia per a una docència universitària amb perspectiva de gènere. MATEMÁTICAS. Guía para una docencia universitaria con perspectiva de género. MATHEMATICS. Guide for a university teaching with a gender perspective. Realiza recomendaciones... more
Guia per a una docència universitària amb perspectiva de gènere. MATEMÁTICAS. Guía para una docencia universitaria con perspectiva de género. MATHEMATICS. Guide for a university teaching with a gender perspective. Realiza recomendaciones que cubren todos los elementos del proceso de enseñanza aprendizaje: objetivos, resultados de aprendizaje, contenidos, metodología docente, evaluación, gestión del entorno de aprendizaje, etc. Hasta la fecha, no existía ninguna publicación con dichas características en la docencia universitaria en matemáticas. La guía también cuenta con un capítulo dedicado a revisar recursos docentes específicos para la incorporación de la perspectiva de género en matemáticas, desde recursos para realizar una docencia inclusiva, para visibilizar a las mujeres matemáticas, sobre sesgos implícitos de género y recursos para humanizar los problemas. Además, existe un capítulo dedicado a mostrar cómo realizar investigación matemática sensible al género, donde se trata tanto la igualdad de oportunidades, como el contenido propio de la investigación. Hace también un repaso sobre sesgos implícitos de género en investigación y docencia. El documento pretende que se reflexione y se tenga en cuenta la diversidad, en especial desde el punto de vista del sexo y género, pero también en lo referente a minorías en el mundo matemático, como personas LGTBIQ+, personas con diversidad funcional y, en general, personas que no se ajustan al patrón o estereotipo de "normalidad". Señala dos elementos como fundamentales, como son la empatía y la neutralización de estereotipos y sesgos implícitos. Escrita en catalán, el fichero electrónico PDF puede traducirse mediante herramientas de traducción automática. Incorporar el principio de igualdad además de mejorar la calidad docente es, sobre todo, una cuestión de justicia social. Más información en: https://www.rsme.es/2020/07/mujeres-y-matematicas-guia-para-una-docencia-universitaria-con-perspectiva-de-genero-en-matematicas/ y https://www.vives.org/programes/igualtat-genere/guies-docencia-universitaria-perspectiva-genere/
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I show how gender perspective can be implemented in mathematics teaching at university. The following aspects are taken into account: 1) an adequate management of the classroom (promoting an equal participation); 2) the visibility of the... more
I show how gender perspective can be implemented in mathematics teaching at university. The following aspects are taken into account: 1) an adequate management of the classroom (promoting an equal participation); 2) the visibility of the contributions of women and other minorized groups in these areas (with different activities, ranging from quotes to escape rooms); 3) the use of inclusive language (in all levels, oral, written, visual, etc.); 4) the methodology through active teaching (learning by doing mathematics, working in cooperative groups, by projects, etc., instead of competitiveness and individualism); 5) the contents (for example, in statistics, showing the importance of an adequate sampling, the appropriate questions in order to see all the visions, i.e. to show the importance of taking into account gender in engineering or other fields through data); 6) the work in values by humanizing the problems (some math activities can be about discrimination, climate change, etc., i.e. math for social justice); 7) the use of the computer; 8) an appropriate evaluation (assessment should be diverse to take into account the diversity of students, but we also have to take into account some implicit biases in evaluation); and, above all, 9) interpersonal relationships, where empathy is essential as well as breaking with stereotypes and implicit biases. We also show how to integrate gender perspective into math research in applied math: in the content, but also as regards to the equal opportunities in a field with few women and minority groups, in pure math. It is also available in Spanish and Catalan: https://www.researchgate.net/publication/345017864_MATEMATICAS_guias_para_una_docencia_universitaria_con_perspectiva_de_genero and https://www.researchgate.net/publication/344176485_MATEMATIQUES_Guia_per_a_una_docencia_universitaria_amb_perspectiva_de_genere_MATEMATICAS_Guia_para_una_docencia_universitaria_con_perspectiva_de_genero_MATHEMATICS_Guide_for_a_university_teaching_with
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In this paper we present different initiatives carried out by Spanish universities for the incorporation of the gender perspective in STEM disciplines. One of these initiatives is the collection of guides of the Vives University Network... more
In this paper we present different initiatives carried out by Spanish universities for the incorporation of the gender perspective in STEM disciplines. One of these initiatives is the collection of guides of the Vives University Network for university teaching. These guides cover the sections of objectives, contents, evaluation, learning environment, organizational modalities, teaching methods, and didactic resources with the aim of making women scientists visible in the discipline and eliminating the androcentric vision that predominates in science and engineering. In particular, we analyze the fields of engineering, mathematics, and physics. With the aim of being more than just a review of different initiatives, the paper unifies the fundamentals on which these initiatives are based. Thus, the general principles are well defined, and those aspects more related to each university and discipline particular cultures are identified. The comparison between initiatives will allow us to identify both successful strategies and resistances. Sometimes, the confluence of different events allows an action to become relevant or not. As a result, the paper can be used to effectively define the implementation strategy of the incorporation of gender perspective in STEM teaching at university level.
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La brecha de género existe en las áreas CTIM (Ciencia, Tecnología, Ingeniería y Matemáticas) y las niñas suelen tener menos autoconfianza en matemáticas. La ley obliga a incorporar la igualdad entre hombres y mujeres en todas las etapas... more
La brecha de género existe en las áreas CTIM (Ciencia, Tecnología, Ingeniería y Matemáticas) y las niñas suelen tener menos autoconfianza en matemáticas. La ley obliga a incorporar la igualdad entre hombres y mujeres en todas las etapas educativas, entre ellas la etapa de primaria, donde nos centramos primordialmente. Es por ello que los principales objetivos son crear una actividad lúdica para: a) reconocer el papel de las mujeres en la historia, de las matemáticas en este caso; b) mostrar referentes de mujeres matemáticas actuales; c) romper estereotipos tanto de género como aquel de «¿para qué sirven las matemáticas?» y d) aprender jugando y disfrutar de un rato divertido, desconectando de la situación que obligó al estudiantado a continuar con las clases online, pues esta actividad se realizó en mayo de 2020. En esta actividad se hace uso de la gamificación educativa, en concreto a través del diseño de una escape room digital. Fue creada a propuesta de la Comisión de Mujeres y Matemáticas de la Real Sociedad Matemática Española para conmemorar el Día Internacional de la Mujer Matemática el 12 de mayo de 2020. Esta escape room, llamada «El rescate de la maga Omega», se encuentra en formato online, en cuatro idiomas y fue realizada usando la plataforma Google forms. Dicha actividad está dirigida especialmente al estudiantado de los primeros años de primaria, aunque es recomendable para todas las edades. Hace un guiño a la situación de pandemia vivida, donde se debe rescatar a una niña maga que ha quedado encerrada en el ordenador al querer ir a visitar a sus amigas y amigos del colegio. En la escape room se han elegido mujeres matemáticas de todas las épocas y lugares, una representación de su diversidad, que muestra que cualquiera puede dedicarse a las matemáticas. Desde Hipatia de Alejandría (Egipto, África), a Emmy Noether (Alemania, Europa) o Maryam Mirzakhani (Irán, Asia), también se incluyen diversas áreas de las matemáticas, de las más aplicadas con Florence Nightingale, a las más teóricas con Sofia Kovalevskaya, de familia gitana. Otra de las matemáticas que aparecen es la afroamericana Katherine Johnson, y Ada Lovelace, conocida como la primera programadora de la historia. Se comentan sus logros de forma divulgativa, para mostrar la utilidad de las matemáticas. Uno de los ejemplos claros en la escape room es la presentación de la matemática española Ana Justel, que estudia los efectos del cambio climático desde la Antártida. En referencia a los resultados, la escape room fue realizada por decenas de miles de escolares en 2020 y continua activa. Contestaron voluntariamente un cuestionario sobre el conocimiento adquirido y su satisfacción. La mayoría (52.9%) de las personas que contestaron no conocían a ninguna de las matemáticas previamente, y los comentarios fueron muy positivos. De ahí que mediante un juego, se logró presentar a mujeres matemáticas, sus logros y dificultades que encontraron por ser mujer a lo largo de la historia. Fue una actividad realizada de manera multitudinaria.
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MATEMÀTIQUES. Guia per a una docència universitària amb perspectiva de gènere. MATEMÁTICAS. Guía para una docencia universitaria con perspectiva de género. MATHEMATICS. Guide for a university teaching with a gender perspective. Realiza... more
MATEMÀTIQUES. Guia per a una docència universitària amb perspectiva de gènere. MATEMÁTICAS. Guía para una docencia universitaria con perspectiva de género. MATHEMATICS. Guide for a university teaching with a gender perspective. Realiza recomendaciones que cubren todos los elementos del proceso de enseñanza aprendizaje: objetivos, resultados de aprendizaje, contenidos, metodología docente, evaluación, gestión del entorno de aprendizaje, etc. Hasta la fecha, no existía ninguna publicación con dichas características en la docencia universitaria en matemáticas. La guía también cuenta con un capítulo dedicado a revisar recursos docentes específicos para la incorporación de la perspectiva de género en matemáticas, desde recursos para realizar una docencia inclusiva, para visibilizar a las mujeres matemáticas, sobre sesgos implícitos de género y recursos para humanizar los problemas. Además, existe un capítulo dedicado a mostrar cómo realizar investigación matemática sensible al género, donde se trata tanto la igualdad de oportunidades, como el contenido propio de la investigación. Hace también un repaso sobre sesgos implícitos de género en investigación y docencia. El documento pretende que se reflexione y se tenga en cuenta la diversidad, en especial desde el punto de vista del sexo y género, pero también en lo referente a minorías en el mundo matemático, como personas LGTBIQ+, personas con diversidad funcional y, en general, personas que no se ajustan al patrón o estereotipo de "normalidad". Señala dos elementos como fundamentales, como son la empatía y la neutralización de estereotipos y sesgos implícitos. Escrita en catalán, el fichero electrónico PDF puede traducirse mediante herramientas de traducción automática. Incorporar el principio de igualdad además de mejorar la calidad docente es, sobre todo, una cuestión de justicia social. Más información en: https://www.rsme.es/2020/07/mujeres-y-matematicas-guia-para-una-docencia-universitaria-con-perspectiva-de-genero-en-matematicas/ y https://www.vives.org/programes/igualtat-genere/guies-docencia-universitaria-perspectiva-genere/
Research Interests:
MATEMÀTIQUES. Guia per a una docència universitària amb perspectiva de gènere. MATEMÁTICAS. Guía para una docencia universitaria con perspectiva de género. MATHEMATICS. Guide for a university teaching with a gender perspective. Realiza... more
MATEMÀTIQUES. Guia per a una docència universitària amb perspectiva de gènere. MATEMÁTICAS. Guía para una docencia universitaria con perspectiva de género. MATHEMATICS. Guide for a university teaching with a gender perspective.
Realiza recomendaciones que cubren todos los elementos del proceso de enseñanza aprendizaje: objetivos, resultados de aprendizaje, contenidos, metodología docente, evaluación, gestión del entorno de aprendizaje, etc. Hasta la fecha, no existía ninguna publicación con dichas características en la docencia universitaria en matemáticas. La guía también cuenta con un capítulo dedicado a revisar recursos docentes específicos para la incorporación de la perspectiva de género en matemáticas, desde recursos para realizar una docencia inclusiva, para visibilizar a las mujeres matemáticas, sobre sesgos implícitos de género y recursos para humanizar los problemas. Además, existe un capítulo dedicado a mostrar cómo realizar investigación matemática sensible al género, donde se trata tanto la igualdad de oportunidades, como el contenido propio de la investigación. Hace también un repaso sobre sesgos implícitos de género en investigación y docencia. El documento pretende que se reflexione y se tenga en cuenta la diversidad, en especial desde el punto de vista del sexo y género, pero también en lo referente a minorías en el mundo matemático, como personas LGTBIQ+, personas con diversidad funcional y, en general, personas que no se ajustan al patrón o estereotipo de "normalidad". Señala dos elementos como fundamentales, como son la empatía y la neutralización de estereotipos y sesgos implícitos. Escrita en catalán, el fichero electrónico PDF puede traducirse mediante herramientas de traducción automática. Incorporar el principio de igualdad además de mejorar la calidad docente es, sobre todo, una cuestión de justicia social. Más información en: https://www.rsme.es/2020/07/mujeres-y-matematicas-guia-para-una-docencia-universitaria-con-perspectiva-de-genero-en-matematicas/ y https://www.vives.org/programes/igualtat-genere/guies-docencia-universitaria-perspectiva-genere/
Realiza recomendaciones que cubren todos los elementos del proceso de enseñanza aprendizaje: objetivos, resultados de aprendizaje, contenidos, metodología docente, evaluación, gestión del entorno de aprendizaje, etc. Hasta la fecha, no existía ninguna publicación con dichas características en la docencia universitaria en matemáticas. La guía también cuenta con un capítulo dedicado a revisar recursos docentes específicos para la incorporación de la perspectiva de género en matemáticas, desde recursos para realizar una docencia inclusiva, para visibilizar a las mujeres matemáticas, sobre sesgos implícitos de género y recursos para humanizar los problemas. Además, existe un capítulo dedicado a mostrar cómo realizar investigación matemática sensible al género, donde se trata tanto la igualdad de oportunidades, como el contenido propio de la investigación. Hace también un repaso sobre sesgos implícitos de género en investigación y docencia. El documento pretende que se reflexione y se tenga en cuenta la diversidad, en especial desde el punto de vista del sexo y género, pero también en lo referente a minorías en el mundo matemático, como personas LGTBIQ+, personas con diversidad funcional y, en general, personas que no se ajustan al patrón o estereotipo de "normalidad". Señala dos elementos como fundamentales, como son la empatía y la neutralización de estereotipos y sesgos implícitos. Escrita en catalán, el fichero electrónico PDF puede traducirse mediante herramientas de traducción automática. Incorporar el principio de igualdad además de mejorar la calidad docente es, sobre todo, una cuestión de justicia social. Más información en: https://www.rsme.es/2020/07/mujeres-y-matematicas-guia-para-una-docencia-universitaria-con-perspectiva-de-genero-en-matematicas/ y https://www.vives.org/programes/igualtat-genere/guies-docencia-universitaria-perspectiva-genere/
Research Interests:
Archetype Analysis (AA) is a statistical technique that describes individuals of a sample as a convex combination of certain number of elements called archetypes, which in turn, are convex combinations of the individuals in the sample.... more
Archetype Analysis (AA) is a statistical technique that describes individuals of a sample as a convex combination of certain number of elements called archetypes, which in turn, are convex combinations of the individuals in the sample. For it’s part, Archetypoid Analysis (ADA) tries to represent each individual as a convex combination of a certain number of extreme subjects called archetypoids. It is possible to extend these techniques to functional data. This work presents an application of Functional Archetypoids Analysis (FADA) to financial time series. At the best of our knowledge, this is the first time FADA is applied in this field. The starting time series consists of daily equity prices of the S&P 500 stocks. From it, measures of volatility and profitability are generated in order to characterize listed companies. These variables are converted into functional data through a Fourier basis expansion function and bivariate FADA is applied. By representing subjects through extreme cases, this analysis facilitates the understanding of both the composition and the relationships between listed companies. Finally, a cluster methodology based on a similarity parameter is presented. Therefore, the suitability of this technique for this kind of time series is shown, as well as the robustness of the conclusions drawn.
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Although Spanish law obligates the integration of a gender perspective in university teaching, this does not happen in reality and much less in math subjects. We provide guidelines for reviewing math courses from a gender perspective. In... more
Although Spanish law obligates the integration of a gender perspective in university teaching, this does not happen in reality and much less in math subjects. We provide guidelines for reviewing math courses from a gender perspective. In particular, we focus on aspects such as: the adequate management of the classroom, the visibility of the contributions of women, the use of an inclusive language, the methodology through active teaching, the contents, the work in values by humanizing the problems, the use of the computer, an appropriate and diverse evaluation and, above all, interpersonal relationships, where empathy and breaking with stereotypes and implicit biases are fundamental. We show examples in some subjects, especially statistics in the field of Engineering and Health degrees. Results based on student participation in activities and their opinion are very satisfactory. Furthermore, feedback obtained after a course about math coeducation to other teachers is also very promising.