Learning Sustainability with EPS@ISEP – Development of a Water Disinfection System

Proceedings of the 8th International Symposium on Project Approaches in Engineering Education (PAEE), Guimarães, 2016

Remédiation cognitive portant sur les aspects fluide et cristallisé de l’intelligence auprès d'adolescents présentant un fonctionnement intellectuel limite : approche par le jeu

L’objectif principal de cette thèse était de créer, d’implanter et d’évaluer l’efficacité d’un programme de remédiation cognitive, intervenant de façon comparable sur les aspects fluide (Gf) et cristallisé (Gc) de l’intelligence, au sein d’une population d’intérêt clinique, les adolescents présentant un fonctionnement intellectuel limite (FIL). Compte tenu de la forte prévalence de ce trouble, le programme de remédiation GAME (Gains et Apprentissages Multiples pour Enfant) s’est développé autour de jeux disponibles dans le commerce afin de faciliter l’accès et l’implantation de ce programme dans divers milieux. Le premier article de cette thèse, réalisé sous forme de revue systématique de la littérature, avait pour objectif de faire le point sur les études publiées utilisant le jeu comme outil de remédiation cognitive dans la population pédiatrique. L’efficacité, ainsi que la qualité du paradigme utilisé ont été évaluées, et des recommandations sur les aspects méthodologiques à respecter lors de ce type d’étude ont été proposées. Cet article a permis une meilleure compréhension des écueils à éviter et des points forts méthodologiques à intégrer lors de la création du programme de remédiation GAME. Certaines mises en garde méthodologiques relevées dans cet article ont permis d’améliorer la qualité du programme de remédiation cognitive développé dans ce projet de thèse. Compte tenu du peu d’études présentes dans la littérature scientifique concernant la population présentant un FIL (70

Self-regulated learning in computer programming : strategies students adopted during an assignment

The SimProgramming teaching approach has the goal to help students overcome their learning difficulties in the transition from entry-level to advanced computer programming and prepare them for real-world labour environments, adopting learning strategies. It immerses learners in a businesslike learning environment, where students develop a problem-based learning activity with a specific set of tasks, one of which is filling weekly individual forms. We conducted thematic analysis of 401 weekly forms, to identify the students’ strategies for self-regulation of learning during assignment. The students are adopting different strategies in each phase of the approach. The early phases are devoted to organization and planning, later phases focus on applying theoretical knowledge and hands-on programming. Based on the results, we recommend the development of educational practices to help students conduct self-reflection of their performance during tasks.

Optimizing last mile delivery using public transport with multiagent based control

The majority of research work carried out in the field of Operations-Research uses methods and algorithms to optimize the pick-up and delivery problem. Most studies aim to solve the vehicle routing problem, to accommodate optimum delivery orders, vehicles etc. This paper focuses on green logistics approach, where existing Public Transport infrastructure capability of a city is used for the delivery of small and medium sized packaged goods thus, helping improve the situation of urban congestion and greenhouse gas emissions reduction. It carried out a study to investigate the feasibility of the proposed multi-agent based simulation model, for efficiency of cost, time and energy consumption. Multimodal Dijkstra Shortest Path algorithm and Nested Monte Carlo Search have been employed for a two-phase algorithmic approach used for generation of time based cost matrix. The quality of the tour is dependent on the efficiency of the search algorithm implemented for plan generation and route planning. The results reveal a definite advantage of using Public Transportation over existing delivery approaches in terms of energy efficiency.

Self-regulated learning in higher education : strategies adopted by computer programming students

Trabalho apresentado em PAEE/ALE’2016, 8th International Symposium on Project Approaches in Engineering Education (PAEE) and 14th Active Learning in Engineering Education Workshop (ALE)

An agent-based simulation of in-store customer experiences

Agent-based modelling and simulation offers a new and exciting way of understanding the world of work. In this paper we describe the development of an agent-based simulation model, designed to help to understand the relationship between human resource management practices and retail productivity. We report on the current development of our simulation model which includes new features concerning the evolution of customers over time. To test some of these features we have conducted a series of experiments dealing with customer pool sizes, standard and noise reduction modes, and the spread of the word of mouth. Our multidisciplinary research team draws upon expertise from work psychologists and computer scientists. Despite the fact we are working within a relatively novel and complex domain, it is clear that intelligent agents offer potential for fostering sustainable organisational capabilities in the future.

The development of an eBook for teaching and learning mathematics

Mobile devices, smartphones, phablets and tablets, are widely avail‐ able. This is a generation of digital natives. We cannot ignore that they are no longer the same students for which the education system was designed tradition‐ ally. Studying math is many times a cumbersome task. But this can be changed if the teacher takes advantage of the technology that is currently available. We are working in the use of different tools to extend the classroom in a blended learning model. In this paper, it is presented the development of an eBook for teaching mathematics to secondary students. It is developed with the free and open standard EPUB 3 that is available for Android and iOS platforms. This specification supports video embedded in the eBook. In this paper it is shown how to take advantage of this feature, making videos available about lectures and problems resolutions, which is especially interesting for learning mathematics.

Yield-SAFE model improvements. Milestone Report 29 (6.4)

The Yield-SAFE model is a parameter-sparse, process-based dynamic model for predicting resource capture, growth, and production in agroforestry systems that has been frequently used by various research organisations in recent years. Within the AGFORWARD project, the model has been enhanced to more accurately predict the delivery of ecosystem services provided by agroforestry systems relative to forestry and arable systems. This report also summar izes the new developments made in the model which were partially implemented during AGFORWARD modelling workshops held in 1) Monchique in Portugal in May 2015, 2) Kriopigi in Greece in June 2015, 3) Lisbon in Portugal in November 2015 and 4) Lisbon in Febru ary 2016 .

Estudio comparativo entre las metodologías de enseñanza–aprendizaje tradicional y el modelo constructivista fundamentado en el aprendizaje basado en problemas en las clases de educación física

El presente trabajo tiene por objetivo realizar un estudio comparativo entre las metodologías de enseñanza-aprendizaje tradicional y el modelo constructivista fundamentado en el Aprendizaje Basado en Problemas (ABP) en las clases de Educación Física, con los alumnos de los décimos años de Educación General Básica de la Escuela José Rafael Arízaga de la ciudad de Cuenca. El estudio se realizó con la finalidad de constatar la problemática, inicialmente se aplicaron fichas de observación a los docentes de Educación Física de la institución. Posteriormente, para el análisis comparativo se escogieron dos grupos. El décimo año de EGB paralelo A (grupo 1 denominado grupo control) y el décimo B (grupo 2 experimental). El primer grupo trabajó con el docente de la institución desarrollando los temas de los bloques curriculares: Movimientos Naturales, Juegos y Movimiento Formativo, Artístico y Expresivo, el segundo desarrolló los mismos temas pero el proceso de enseñanza aprendizaje se fundamentó en el ABP. Los resultados de la aplicación de las dos metodologías se evidenciaron en las evaluaciones a los estudiantes (Destrezas con criterio de desempeño). El análisis de los resultados obtenidos durante todo el proceso de evaluación, permitió comparar las metodologías evidenciando de ésta manera las ventajas y desventajas de su aplicación en las clases de Educación Física.

Trajectory Privacy Preservation in Mobile Wireless Sensor Networks

In recent years, there has been an enormous growth of location-aware devices, such as GPS embedded cell phones, mobile sensors and radio-frequency identification tags. The age of combining sensing, processing and communication in one device, gives rise to a vast number of applications leading to endless possibilities and a realization of mobile Wireless Sensor Network (mWSN) applications. As computing, sensing and communication become more ubiquitous, trajectory privacy becomes a critical piece of information and an important factor for commercial success. While on the move, sensor nodes continuously transmit data streams of sensed values and spatiotemporal information, known as ``trajectory information". If adversaries can intercept this information, they can monitor the trajectory path and capture the location of the source node. This research stems from the recognition that the wide applicability of mWSNs will remain elusive unless a trajectory privacy preservation mechanism is developed. The outcome seeks to lay a firm foundation in the field of trajectory privacy preservation in mWSNs against external and internal trajectory privacy attacks. First, to prevent external attacks, we particularly investigated a context-based trajectory privacy-aware routing protocol to prevent the eavesdropping attack. Traditional shortest-path oriented routing algorithms give adversaries the possibility to locate the target node in a certain area. We designed the novel privacy-aware routing phase and utilized the trajectory dissimilarity between mobile nodes to mislead adversaries about the location where the message started its journey. Second, to detect internal attacks, we developed a software-based attestation solution to detect compromised nodes. We created the dynamic attestation node chain among neighboring nodes to examine the memory checksum of suspicious nodes. The computation time for memory traversal had been improved compared to the previous work. Finally, we revisited the trust issue in trajectory privacy preservation mechanism designs. We used Bayesian game theory to model and analyze cooperative, selfish and malicious nodes' behaviors in trajectory privacy preservation activities.

Efectos de una intervención educativa en los conocimientos y comportamientos relacionados con la fotoprotección durante la práctica de la actividad física en los estudiantes de un colegio público de Bogotá D.C., Colombia

Introducción: La incidencia del cáncer de piel melanoma y no melanoma es un problema de salud pública a nivel mundial. El incremento en la incidencia del cáncer de piel en los últimos años se debe a múltiples factores como: cambios en los estilos de vida, el envejecimiento de la población, cambios ambientales, el desconocimiento a la exposición a la radiación ultravioleta (RUV) durante la práctica de actividad física sin elementos de fotoprotección, siendo éste último reconocido como el principal factor de riesgo. Objetivo: Evaluar los efectos de una intervención educativa en los conocimientos y comportamientos relacionados con la fotoprotección durante la práctica de la actividad física en estudiantes de un colegio público de Bogotá D.C., Colombia. Métodos: Estudio de intervención, antes y después, no controlado en 281 estudiantes de los grados noveno, décimo y once de estratos 1-3 de un colegio público de Bogotá, con seguimiento a 1, 3 y 6 meses post-intervención. Se evaluaron los conocimientos y los hábitos de fotoprotección mediante un cuestionario Cancer Awareness Measure (CAM) y el modelo Transteórico de cambio comportamental de Prochaska y Di Clemente. El estudio se realizó durante el primer semestre de 2015 con 4 sesiones educativas de 60 minutos apoyadas con material audiovisual y pedagógico, acorde a la Guía para la Comunicación Educativa en el marco el control del cáncer publicada por el Instituto Nacional de Cancerología. Resultados: Del grupo de estudiantes que participaron del estudio, el 52,3% eran hombres, el promedio de edad fue de 15,46 ± 1,2 años. El tipo de piel predominante fue la trigueña con 65,8%. La intervención educativa produjo cambios significativos en los conocimientos de foto protección, finalizado el seguimiento al sexto mes. En cuanto a la prevención los estudiantes refirieron tener conocimiento de cómo examinar su piel en el momento basal (12,5% n=35), presentándose un aumento significativo de 62,6% (n=211) al sexto mes (p<0,05). Conclusión: El estudio demostró la efectividad de la intervención educativa, evidenciando cambios significativos en los conocimientos en fotoprotección y comportamientos preventivos del cáncer de piel durante la práctica de la actividad física en estudiantes de un colegio público de Bogotá D.C., Colombia.

El videojuego League of legends y su efecto en memoria de trabajo visual y solución de problemas

El propósito de este estudio es medir los efectos que tiene el videojuego League of Legends en los procesos cognitivos de memoria de trabajo visual (MVT) y solución de problemas (SP). Para medir dichos efectos se implementó un diseño pre test-post con un grupo experimental y uno control, compuestos cada uno por siete participantes, en donde se evaluaron los procesos previamente mencionados utilizando los cubos de Corsi para MVT y las matrices del WAIS III para SP. Después de realizar los respectivos entrenamientos se encontraron resultados significativos en los diferentes momentos de aplicación. En el grupo experimental se encontraron diferencias en la variable dependiente SP, mientras que en el grupo control en MVT, pero no en la interacción entre grupos ni diferencias entre grupos, lo que sugiere un efecto de familiarización a la prueba.

How should we measure online learning activity?

The proliferation of Web-based learning objects makes finding and evaluating resources a considerable hurdle for learners to overcome. While established learning analytics methods provide feedback that can aid learner evaluation of learning resources, the adequacy and reliability of these methods is questioned. Because engagement with online learning is different from other Web activity, it is important to establish pedagogically relevant measures that can aid the development of distinct, automated analysis systems. Content analysis is often used to examine online discussion in educational settings, but these instruments are rarely compared with each other which leads to uncertainty regarding their validity and reliability. In this study, participation in Massive Open Online Course (MOOC) comment forums was evaluated using four different analytical approaches: the Digital Artefacts for Learning Engagement (DiAL-e) framework, Bloom's Taxonomy, Structure of Observed Learning Outcomes (SOLO) and Community of Inquiry (CoI). Results from this study indicate that different approaches to measuring cognitive activity are closely correlated and are distinct from typical interaction measures. This suggests that computational approaches to pedagogical analysis may provide useful insights into learning processes.

Tensor-Train decomposition for image classification problems

In these last years a great effort has been put in the development of new techniques for automatic object classification, also due to the consequences in many applications such as medical imaging or driverless cars. To this end, several mathematical models have been developed from logistic regression to neural networks. A crucial aspect of these so called classification algorithms is the use of algebraic tools to represent and approximate the input data. In this thesis, we examine two different models for image classification based on a particular tensor decomposition named Tensor-Train (TT) decomposition. The use of tensor approaches preserves the multidimensional structure of the data and the neighboring relations among pixels. Furthermore the Tensor-Train, differently from other tensor decompositions, does not suffer from the curse of dimensionality making it an extremely powerful strategy when dealing with high-dimensional data. It also allows data compression when combined with truncation strategies that reduce memory requirements without spoiling classification performance. The first model we propose is based on a direct decomposition of the database by means of the TT decomposition to find basis vectors used to classify a new object. The second model is a tensor dictionary learning model, based on the TT decomposition where the terms of the decomposition are estimated using a proximal alternating linearized minimization algorithm with a spectral stepsize.

Data-stream driven Fuzzy-granular approaches for system maintenance

Intelligent systems are currently inherent to the society, supporting a synergistic human-machine collaboration. Beyond economical and climate factors, energy consumption is strongly affected by the performance of computing systems. The quality of software functioning may invalidate any improvement attempt. In addition, data-driven machine learning algorithms are the basis for human-centered applications, being their interpretability one of the most important features of computational systems. Software maintenance is a critical discipline to support automatic and life-long system operation. As most software registers its inner events by means of logs, log analysis is an approach to keep system operation. Logs are characterized as Big data assembled in large-flow streams, being unstructured, heterogeneous, imprecise, and uncertain. This thesis addresses fuzzy and neuro-granular methods to provide maintenance solutions applied to anomaly detection (AD) and log parsing (LP), dealing with data uncertainty, identifying ideal time periods for detailed software analyses. LP provides deeper semantics interpretation of the anomalous occurrences. The solutions evolve over time and are general-purpose, being highly applicable, scalable, and maintainable. Granular classification models, namely, Fuzzy set-Based evolving Model (FBeM), evolving Granular Neural Network (eGNN), and evolving Gaussian Fuzzy Classifier (eGFC), are compared considering the AD problem. The evolving Log Parsing (eLP) method is proposed to approach the automatic parsing applied to system logs. All the methods perform recursive mechanisms to create, update, merge, and delete information granules according with the data behavior. For the first time in the evolving intelligent systems literature, the proposed method, eLP, is able to process streams of words and sentences. Essentially, regarding to AD accuracy, FBeM achieved (85.64+-3.69)%; eGNN reached (96.17+-0.78)%; eGFC obtained (92.48+-1.21)%; and eLP reached (96.05+-1.04)%. Besides being competitive, eLP particularly generates a log grammar, and presents a higher level of model interpretability.