Automating the analysis of problem-solving activities in learning environments: the co-lab case study

The analysis of problem-solving activities carried out by students in learning settings involves studying the students' actions and assessing the solutions they have created. This analysis constitutes an ideal starting point to support an automatic intervention in the student activity by means of feedback or other means to help students build their own knowledge. In this paper, we present a model-driven framework to facilitate the automation of this problemsolving analysis and of providing feedback. This framework includes a set of authoring tools that enable software developers to specify the analysis process and its intervention mechanisms by means of visual languages. The models specified in this way are computed by the framework in order to create technological support to automate the problem-solving analysis. The use of the framework is illustrated thanks to a case study in the field of System Dynamics where problem-solving practices are analysed.The Ministerio de Educación y Ciencia (España) has partially supported this research under Project TIN2011-29542-C02-02. The authors would like to express their gratitude to Ton de Jong, Wouter R. van Joolingen and Sylvia van Borkulo (University of Twente), for supporting this research. The work reported here was done during Rafael Duque’s stay at the Department of Instructional Technology of the University of Twente

A Framework for Identifying Sequences of Interactions That Cause Usability Problems in Collaborative Systems

Collaborative systems support shared spaces, where groups of users exchange interactions. In order to ensure the usability of these systems, an intuitive interactions´ organization and that each user has awareness information to know the activity of others are necessary. Usability laboratories allow evaluators to verify these requirements. However, laboratory usability evaluations can be problematic for reproducing mobile and ubiquitous contexts, as they restrict the place and time in which the user interacts with the system. This paper presents a framework for building software support that it collects human?machine interactions in mobile and ubiquitous contexts and outputs an assessment of the system´s usability. This framework is constructed through learning that is based on neural networks, identifying sequences of interactions related to usability problems when users carry out collaborative activities. The paper includes a case study that puts the framework into action during the development process of a smartphone application that supports collaborative sport betting.This research and the APC was funded by the University of Cantabria and the Government of Cantabria through the industrial doctorate grant DI27, given to Santos Bringas. Alicia Nieto-Reyes was supported by a Spanish Ministerio de Ciencia, Innovación y Universidades grant MTM2017-86061-C2-2-P

A Convolutional Neural Network-Based Method for Human Movement Patterns Classification in Alzheimer?s Disease

Alzheimer’s disease (AD) constitutes a neurodegenerative pathology that presents mobility disorders as one of its earliest symptoms. Current smartphones integrate accelerometers that can be used to collect mobility data of Alzheimer’s patients. This paper describes a method that processes these accelerometer data and a convolutional neural network (CNN) that classifies the stage of the disease according to the mobility patterns of the patient. The method is applied in a case study with 35 Alzheimer’s patients, in which a classification success rate of 91% was obtaine

Una metodología centrada en el usuario para el desarrollo de sistemas inteligentes basados en modelos de aprendizaje profundo

Esta propuesta doctoral está orientada al análisis de los sistemas inteligentes que utilizan modelos de aprendizaje profundo como núcleo de pensamiento. Dichos sistemas, por su complejidad, son difíciles de entrenar y de mejorar continuamente, además de que suelen ser una caja negra, por lo que sus resultados son poco explicables. En la tesis se está estudiando cómo orientar estos sistemas inteligentes a los usuarios, de tal forma que puedan aprovechar mejor los modelos desarrollados para sus problemas concretos, adaptando el sistema a voluntad. Además, también se mejorará el entendimiento que tienen de los mismos mediante técnicas de explicabilidad de modelos Deep Learning, lo cual repercutirá de forma positiva en la usabilidad general del sistema. Se pretende con esto obtener una metodología para el desarrollo de este tipo de sistemas, la cual pueda ser utilizada en aplicaciones reales y trabajos futuros.Esta tesis está financiada por la Universidad de Cantabria, el Gobierno de Cantabria y el Banco Santander a través de la beca de doctorado industrial DI27, concedida a Santos Bringas en la convocatoria del Programa de Doctorados Industriales 2020

Discovering user's trends and routines from location based social networks

ABSTRACT: Location data is a powerful source of information to discover user's trends and routines. A suitable identification of the user context can be exploited to provide automatically services adapted to the user preferences. In this paper, we define a Dynamic Bayesian Network model and propose a method that processes location annotated data in order to train the model. Finally, our model enables us to predict future location contexts from the user patterns. A case study evaluates the proposal using real-world data of a location-based social network.This research was funded by Fondo Europeo de Desarrollo Regional (FEDER) and Sociedad para el Desarrollo Regional de Cantabria (SODERCAN) grant number TI16-IN-007 (within the program “I+C=+C 2016- PROYECTOS DE I+D EN EL ÁMBITO DE LAS TIC, LÍNEA SMART”), and by Ministerio de Ciencia e Innovación (MICINN), Spain grant number MTM2014-55262-P (project PAC::LFO)

Activity in the field of Human-Computer Interaction of a work team integrated in the MCFLAI research group

Se presenta la actividad en el ámbito de la Interacción Persona-Ordenador de un equipo de trabajo integrado en el grupo de investigación MCFLAI (Mathematics & Computation: Foundations, Learning, Artificial Intelligence) de la Universidad de CantabriaThe activity in the field of Human-Computer Interaction of a work team integrated in the research group MCFLAI (Mathematics & Computation: Foundations, Learning, Artificial Intelligence) of the University of Cantabria is presented

Long-term thermal sensitivity of Earth’s tropical forests

The sensitivity of tropical forest carbon to climate is a key uncertainty in predicting global climate change. Although short-term drying and warming are known to affect forests, it is unknown if such effects translate into long-term responses. Here, we analyze 590 permanent plots measured across the tropics to derive the equilibrium climate controls on forest carbon. Maximum temperature is the most important predictor of aboveground biomass (−9.1 megagrams of carbon per hectare per degree Celsius), primarily by reducing woody productivity, and has a greater impact per °C in the hottest forests (>32.2°C). Our results nevertheless reveal greater thermal resilience than observations of short-term variation imply. To realize the long-term climate adaptation potential of tropical forests requires both protecting them and stabilizing Earth’s climate

Gestión de la comunicación aplicada

La gestión de proyectos aplicados se refiere al enfoque práctico y sistemático utilizado para planificar, organizar y controlar el trabajo necesario para completar un proyecto de manera eficiente y efectiva. Esto implica identificar los objetivos, definir las tareas y actividades necesarias para completar la propuesta, asignar recursos y establecer un cronograma para el trabajo. Además, monitorear y controlar el progreso del proyecto para asegurarse de que se está avanzando de acuerdo con el plan y tomar medidas para solucionar posibles problemas o desafíos que puedan surgir

Autoantibodies against type I IFNs in patients with critical influenza pneumonia

In an international cohort of 279 patients with hypoxemic influenza pneumonia, we identified 13 patients (4.6%) with autoantibodies neutralizing IFN-alpha and/or -omega, which were previously reported to underlie 15% cases of life-threatening COVID-19 pneumonia and one third of severe adverse reactions to live-attenuated yellow fever vaccine. Autoantibodies neutralizing type I interferons (IFNs) can underlie critical COVID-19 pneumonia and yellow fever vaccine disease. We report here on 13 patients harboring autoantibodies neutralizing IFN-alpha 2 alone (five patients) or with IFN-omega (eight patients) from a cohort of 279 patients (4.7%) aged 6-73 yr with critical influenza pneumonia. Nine and four patients had antibodies neutralizing high and low concentrations, respectively, of IFN-alpha 2, and six and two patients had antibodies neutralizing high and low concentrations, respectively, of IFN-omega. The patients' autoantibodies increased influenza A virus replication in both A549 cells and reconstituted human airway epithelia. The prevalence of these antibodies was significantly higher than that in the general population for patients 70 yr of age (3.1 vs. 4.4%, P = 0.68). The risk of critical influenza was highest in patients with antibodies neutralizing high concentrations of both IFN-alpha 2 and IFN-omega (OR = 11.7, P = 1.3 x 10(-5)), especially those <70 yr old (OR = 139.9, P = 3.1 x 10(-10)). We also identified 10 patients in additional influenza patient cohorts. Autoantibodies neutralizing type I IFNs account for similar to 5% of cases of life-threatening influenza pneumonia in patients <70 yr old