Metodología y herramienta TIC de apoyo a la acción tutorial en el marco del EEES

Uno de los aspectos que ha cobrado mayor protagonismo desde la aplicación del Espacio Europeo de Educación Superior (EEES) es la atención tutorial al alumno como forma de incidir positivamente en su proceso de aprendizaje. Por ello, dentro del campo de la innovación educativa se están definiendo metodologías docentes y herramientas TIC de apoyo a la acción tutorial. Estas metodologías y herramientas pretenden mejorar el servicio de atención a los alumnos contribuyendo entre otros aspectos a la sistematización de tareas implicadas en la acción tutorial y a la automatización de algunas de ellas mediante la utilización de herramientas informáticas. Este trabajo propone la utilización en el marco del EEES de una metodología de acción tutorial apoyada en herramientas TIC para la reserva y atención de tutorías, en concreto, la herramienta TutorialAction, que facilita la reserva de tutorías por parte del alumno y el registro de las mismas por parte del profesor. La investigación ha tenido como objetivos, en primer lugar, poner a disposición de alumnos y profesores la metodología y herramienta software definidas explorando aspectos de interés sobre la acción tutorial que permitan afinar ambas, y en segundo lugar determinar la influencia de la acción tutorial en el rendimiento académico en consonancia con el EEES. La muestra analizada corresponde a estudiantes y profesores del Grado en Ingeniería del Software de la Universidad Politécnica de Madrid durante los cursos académicos 2011-12 y 2012-13. Los instrumentos utilizados en la investigación han incluido dos cuestionarios, uno orientado a conocer la opinión de los alumnos y otro la de los profesores; así como los datos registrados por la herramienta TutorialAction sobre reserva, asistencia y atención de tutorías y las calificaciones obtenidas por los alumnos que componen la muestra. Los resultados obtenidos reflejan una mayor asistencia a tutorías en las asignaturas objeto de estudio y una valoración positiva sobre la utilización de la herramienta TutorialAction. Los resultados también han permitido identificar aspectos como la duración media de tutorías, el tipo de tutorías predominante, así como su periodicidad. Por otra parte, en línea con los objetivos de esta investigación se ha encontrado una correlación moderada, positiva y significativa entre la asistencia a tutorías y la calificación obtenida por los alumnos. Estos resultados permiten concluir que la metodología y herramienta presentadas en este trabajo resultan de utilidad para implementar una acción tutorial de calidad entre profesor y alumno en el marco del EEES, así como que la asistencia a tutorías constituye un factor relevante que incide positivamente en los resultados académicos del alumno

Motivation in engineering education: a framework supported by evaluation instruments and enhancement resources

Motivation is a critical factor in the academic performance, especially in the EHEA context, where the active learning must be promoted. In the case of engineering education, it is particularly necessary to care the student motivation by several reasons. The difficulty of engineering degrees can hinder the motivation of students and it can affect the high levels of absenteeism and dropping out. Thus, it is important to work up frameworks to evaluate and enhance, in addition to technical competences, other competences such as motivation. This paper presents the definition of a motivational framework composed by several instruments, resources, mechanisms and technologies. It allows teachers and academic institutions to evaluate and enhance the motivation of their students. But the most innovative characteristic of the framework lies in the student side; it also allows students to self-evaluate and enhance their motivation by performing questionnaires, recommendations and complementary training activities. In order to validate the proposed solutions, a case study has been successfully performed with 152 students of the Technical University of Madrid. The empirical experience has enabled to confirm the usefulness of the provided framework and to explore motivational aspects related with the engineering education

Extended Variability Models, Algebra, and Arithmetic

Although classic variability models have been traditionally used to specify members of a product-line, their level of expressiveness was quite limited. Several extensions have been proposed, like numerical features, complex cardinalities and feature and configuration attributes. However, modern tools often provide limited support to these extensions. Imposing variability modelling restrictions into general theories enables off-the-self automated reasoners to analyse extended variability models. While one could argue that those general theories are less reasoning efficient, in practice happen the same if we extend traditional solvers. In contrast, general theories provide new properties with the potential to a) improve reasoning efficiency above extending traditional solvers, and b) provide exotic analyses that uncover new properties of the variability models and feature and configuration spaces. Examples of this could be the functions commutativity property, (reasoning) functors composition, and the fundamental theorem of calculus applied to feature or configuration space.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Detecting Feature Influences to Quality Attributes in Large and Partially Measured Spaces using Smart Sampling and Dynamic Learning

Publicación Journal First siendo el original: Munoz, D. J., Pinto, M., & Fuentes, L. (2023). Detecting feature influences to quality attributes in large and partially measured spaces using smart sampling and dynamic learning. Knowledge-Based Systems, 270, 110558.Emergent application domains (e.g., Edge Computing/Cloud /B5G systems) are complex to be built manually. They are characterised by high variability and are modelled by large \textit{Variability Models} (VMs), leading to large configuration spaces. Due to the high number of variants present in such systems, it is challenging to find the best-ranked product regarding particular Quality Attributes (QAs) in a short time. Moreover, measuring QAs sometimes is not trivial, requiring a lot of time and resources, as is the case of the energy footprint of software systems -- the focus of this paper. Hence, we need a mechanism to analyse how features and their interactions influence energy footprint, but without measuring all configurations. While practical, sampling and predictive techniques base their accuracy on uniform spaces or some initial domain knowledge, which are not always possible to achieve. Indeed, analysing the energy footprint of products in large configuration spaces raises specific requirements that we explore in this work. This paper presents SAVRUS (Smart Analyser of Variability Requirements in Unknown Spaces), an approach for sampling and dynamic statistical learning without relying on initial domain knowledge of large and partially QA-measured spaces. SAVRUS reports the degree to which features and pairwise interactions influence a particular QA, like energy efficiency. We validate and evaluate SAVRUS with a selection of likewise systems, which define large searching spaces containing scattered measurements.Trabajo financiado por el programa de I+D H2020 de la UE bajo el acuerdo DAEMON 101017109, por los proyectos también co-financiados por fondos FEDER \emph{IRIS} PID2021-122812OB-I00, y \emph{LEIA} UMA18-FEDERIA-157, y la ayuda PRE2019-087496 del Ministerio de Ciencia e Innovación. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Detecting feature influences to quality attributes in large and partially measured spaces using smart sampling and dynamic learning

Emergent application domains (e.g., Edge Computing/Cloud/B5G systems) are complex to be built manually. They are characterised by high variability and are modelled by large Variability Models (VMs), leading to large configuration spaces. Due to the high number of variants present in such systems, it is challenging to find the best-ranked product regarding particular Quality Attributes (QAs) in a short time. Moreover, measuring QAs sometimes is not trivial, requiring a lot of time and resources, as is the case of the energy footprint of software systems — the focus of this paper. Hence, we need a mechanism to analyse how features and their interactions influence energy footprint, but without measuring all configurations. While practical, sampling and predictive techniques base their accuracy on uniform spaces or some initial domain knowledge, which are not always possible to achieve. Indeed, analysing the energy footprint of products in large configuration spaces raises specific requirements that we explore in this work. This paper presents SAVRUS (Smart Analyser of Variability Requirements in Unknown Spaces), an approach for sampling and dynamic statistical learning without relying on initial domain knowledge of large and partially QA-measured spaces. SAVRUS reports the degree to which features and pairwise interactions influence a particular QA, like energy efficiency. We validate and evaluate SAVRUS with a selection of likewise systems, which define large searching spaces containing scattered measurements.Funding for open access charge: Universidad de Málaga / CBUA. This work is supported by the European Union’s H2020 re search and innovation programme under grant agreement DAEMON H2020-101017109, by the projects IRIS PID2021-12281 2OB-I00 (co-financed by FEDER funds), Rhea P18-FR-1081 (MCI/AEI/ FEDER, UE), and LEIA UMA18-FEDERIA-157, and the PRE2019-087496 grant from the Ministerio de Ciencia e Innovación, Spain

Defining Categorical Reasoning of Numerical Feature Models with Feature-Wise and Variant-Wise Quality Attributes

Automatic analysis of variability is an important stage of Software Product Line (SPL) engineering. Incorporating quality information into this stage poses a significant challenge. However, quality-aware automated analysis tools are rare, mainly because in existing solutions variability and quality information are not unified under the same model. In this paper, we make use of the Quality Variability Model (QVM), based on Category Theory (CT), to redefine reasoning operations. We start defining and composing the six most commonoperations in SPL, but now as quality-based queries, which tend to be unavailable in other approaches. Consequently, QVM supports interactions between variant-wise and feature-wise quality attributes. As a proof of concept,we present, implement and execute the operations as lambda reasoning for CQL IDE – the state-of-theart CT tool.Munoz, Pinto and Fuentes work is supported by the European Union’s H2020 research and innovation programme under grant agreement DAEMON 101017109, by the projects co-financed by FEDER funds LEIA UMA18-FEDERJA-15, MEDEA RTI2018-099213-B-I00 and Rhea P18-FR-1081 and the PRE2019-087496 grant from the Ministerio de Ciencia e Innovación

Transforming numerical feature models into propositional formulas and the universal variability language

Real-world Software Product Lines (SPLs) need Numerical Feature Models (NFMs) whose features have not only boolean values that satisfy boolean constraints but also have numeric attributes that satisfy arithmetic constraints. An essential operation on NFMs finds near-optimal performing products, which requires counting the number of SPL products. Typical constraint satisfaction solvers perform poorly on counting and sampling. Nemo (Numbers, features, models) is a tool that supports NFMs by bit-blasting, the technique that encodes arithmetic expressions as boolean clauses. The newest version, Nemo2, translates NFMs to propositional formulas and the Universal Variability Language (UVL). By doing so, products can be counted efficiently by #SAT and Binary Decision Tree solvers, enabling finding near-optimal products. This article evaluates Nemo2 with a large set of synthetic and colossal real-world NFMs, including complex arithmetic constraints and counting and sampling experiments. We empirically demonstrate the viability of Nemo2 when counting and sampling large and complex SPLs.Munoz, Pinto and Fuentes work is supported by the European Union’s H2020 research and innovation programme under grant agreement DAEMON 101017109, by the projects co-financed by FEDER, Spain funds LEIA UMA18-FEDERJA-15, IRIS PID2021- 122812OB-I00 (MCI/AEI), and the PRE2019-087496 grant from the Ministerio de Ciencia e Innovación. Funding for open access charge: Universidad de Málaga / CBUA

Módulo Moodle para la gestión automatizada de tutorías

La tutoría es un recurso del que disponen los estudiantes en el desarrollo de su proceso formativo y académico, que en muchos casos está siendo infrautilizada. La existencia de herramientas software orientadas a facilitar la asistencia a tutorías permite un mejor aprovechamiento de dicho recurso. Este hecho ha podido constatarse en trabajos previos a éste, que han conducido a la creación y utilización de la aplicación web TutorialAction, que permite la gestión automatizada de tutorías. OBJETIVOS: Este trabajo persigue la incorporación a campus virtuales de un módulo de gestión automatizada de tutorías, que facilite a estudiantes la reserva de tutorías, y a profesores, la gestión de la información asociada a las mismas. METODOLOGÍA: Siguiendo principios y prácticas de las metodología ágiles de desarrollo software se ha creado un módulo de acción tutorial como extensión de la plataforma Moodle. Se ha elegido Moodle puesto que es una de la opciones más utilizadas para la creación de campus virtuales. RESULTADOS: El módulo creado está siendo utilizado de forma experimental por estudiantes y profesores de grado en la ETS de Ingeniería de Sistemas Informáticos de la Universidad Politécnica de Madrid. En opinión de los estudiantes y profesores participantes la utilización dentro del campus virtual del módulo de gestión de tutorías representa una mejora notable respecto al sistema clásico de asistencia a tutorías. CONCLUSIONES: Los resultados permiten concluir que la disposición de un servicio de reserva de tutoría para estudiantes dentro del campus virtual permitirá una mayor utilización del recurso de la tutoría

Automated data analysis for static structural health monitoring of masonry heritage structures

This is the peer reviewed version of the following article: [Makoond, N, Pelà, L, Molins, C, Roca, P, Alarcón, D. Automated data analysis for static structural health monitoring of masonry heritage structures. Struct Control Health Monit. 2020; 27:e2581. https://doi.org/10.1002/stc.2581], which has been published in final form at https://onlinelibrary.wiley.com/doi/epdf/10.1002/stc.2581. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.Masonry heritage structures are often affected by slow irreversible deterioration mechanisms that can jeopardise structural stability in the foreseeable future. Static structural health monitoring (SHM), aimed at the continuous measurement of key slow-varying parameters, has the potential to identify such mechanisms at a very early stage. This can greatly facilitate the implementation of adequate preventive and remedial measures, which can be critical to ensure that such structures are preserved for generations to come. However, because monitored parameters usually experience reversible seasonal variations of the same order of magnitude as changes caused by active mechanisms, identification of the latter is often a difficult task. This paper presents a fully integrated automated data analysis procedure for complete static SHM systems utilising dynamic linear regression models to filter out the effects caused by environmental variations. The method does not only produce estimated evolution rates but also classifies monitored responses in predefined evolution states. The procedure has successfully been used to identify vulnerable areas in two important medieval heritage structures in Spain, namely, the cathedral of Mallorca and the church of the monastery of Sant Cugat.Ajuntament de Sant Cugat through a project aimed at monitoring the Monastery of Sant Cugat, ref. num. C-10764. Ministry of Education, Culture and Sports of the Spanish Government through a project aimed at studying the structural condition of Mallorca Cathedral, ref. num. 2/131400106ca - 5/030300592 EF. AGAUR agency of the Generalitat de Catalunya and European Social Fund, through a predoctoral grant awarded to the corresponding author. Ministry of Science, Innovation and Universities of the Spanish Government and European Regional Development Fund through the SEVERUS project, ref. num. RTI2018-099589-B-100.Peer ReviewedPostprint (author's final draft

Experiments on a scale model of a monolithic concrete spar for floating wind turbines

Preliminary studies of a concept consisting of a monolithic concrete SPAR platform were presented in 2014. The studies were performed in the framework of the AFOSP KIC-InnoEnergy project (Alternative Floating Platform Designs for Offshore Wind Towers using Low Cost Materials) showing significant costs reduction. The experimental phase of the project was developed during 2014. The experiments comprised a set of hydrodynamic tests performed in the CIEM wave flume facility at the Universitat Politècnica de Catalunya (UPC), with a 1:100 scale model assuming Froude similitude. The complete experimental campaign included free decay tests, a set of 22 regular wave trains of different periods to determine the RAO’s and another set of 21 regular and irregular wave trains in conjunction with a mechanical wind device, simulating the mean thrust force exerted by the wind turbine. To adjust the weight of the whole system, a set of adjustable weights inside de scale model were designed assuring such properties, particularly the pitch/roll inertia. The scaled model of the mooring system was carefully studied because the constraints in width of the flume facility. A mechanical wind device was also specifically designed to ensure an averaged force at the top of the model, simulating the effect of the mean rotor thrust force. A detailed description of the methodology for the experimental campaign and a summary of the experimental results are presented.Peer ReviewedPostprint (author’s final draft