Mejorando la evaluación de juegos serios mediante el uso de analíticas de aprendizaje

Este trabajo de tesis propone el uso de analíticas de aprendizaje para mejorar la evaluación en videojuegos educativos o juegos serios. El trabajo aborda dos aspectos necesarios para una evaluación de aprendizaje efectiva: 1) los requisitos necesarios en el diseño de juegos serios; 2) la infraestructura y el modelo de análisis necesario para las analíticas de aprendizaje. Estos dos trabajos, combinados, marcan un camino para aplicar analíticas de aprendizaje a juegos serios de manera efectiva. Esta aplicación se fundamenta en proporcionar a profesores e instructores resultados de aprendizaje (o niveles de conocimiento) de sus alumnos tras interactuar con un juego serio. El proceso empieza por el diseño de un juego serio efectivo, capaz de capturar, a partir de las interacciones de los estudiantes, el proceso de aprendizaje. A partir de ahí, se define un modelo de captura de datos que recoja estas interacciones. Después, estas interacciones son analizadas para obtener resultados de aprendizaje, y, finalmente, son mostradas a través de visualizaciones a los profesores e instructores, que pueden tomar acción a partir de ahí. El diseño de juego serios se aborda desarrollando una metodología que modela los aspectos fundamentales en el desarrollo e implementación de un juego serio, desde su incepción inicial a partir de objetivos educativos, pasando por su validación formal con usuarios expertos y terminando con el despliegue final, en el que los usuarios objetivo interactúan con el juego para lograr sus objetivos educativos. Esta metodología se ve acompañada, para cada uno de sus pasos, de las consideraciones necesarias para habilitar la evaluación automática del juego serio, y de un patrón de diseño destinado a estructurar la manera en la que los juegos serios presentan los contenidos educativos a sus jugadores..

Evolución de las poblaciones y aspectos de conservación del endemismo valenciano en peligro de extinción Limonium perplexum

Limonium perplexum Sáez & Rosselló (Plumbaginaceae) es un endemismo exclusivo de la Comunidad Valenciana (Sáez & Rosselló, 1999), únicamente presente en los acantilados de la Sierra de Irta (Peñíscola, Castellón) (Gómez-Serrano et al., 2005), donde se localiza la única población nativa conocida hasta la fecha, dentro de la microrreserva de flora denominada "Torre de la Badum"

Effectiveness of an intervention for improving drug prescription in primary care patients with multimorbidity and polypharmacy:Study protocol of a cluster randomized clinical trial (Multi-PAP project)

This study was funded by the Fondo de Investigaciones Sanitarias ISCIII (Grant Numbers PI15/00276, PI15/00572, PI15/00996), REDISSEC (Project Numbers RD12/0001/0012, RD16/0001/0005), and the European Regional Development Fund ("A way to build Europe").Background: Multimorbidity is associated with negative effects both on people's health and on healthcare systems. A key problem linked to multimorbidity is polypharmacy, which in turn is associated with increased risk of partly preventable adverse effects, including mortality. The Ariadne principles describe a model of care based on a thorough assessment of diseases, treatments (and potential interactions), clinical status, context and preferences of patients with multimorbidity, with the aim of prioritizing and sharing realistic treatment goals that guide an individualized management. The aim of this study is to evaluate the effectiveness of a complex intervention that implements the Ariadne principles in a population of young-old patients with multimorbidity and polypharmacy. The intervention seeks to improve the appropriateness of prescribing in primary care (PC), as measured by the medication appropriateness index (MAI) score at 6 and 12months, as compared with usual care. Methods/Design: Design:pragmatic cluster randomized clinical trial. Unit of randomization: family physician (FP). Unit of analysis: patient. Scope: PC health centres in three autonomous communities: Aragon, Madrid, and Andalusia (Spain). Population: patients aged 65-74years with multimorbidity (≥3 chronic diseases) and polypharmacy (≥5 drugs prescribed in ≥3months). Sample size: n=400 (200 per study arm). Intervention: complex intervention based on the implementation of the Ariadne principles with two components: (1) FP training and (2) FP-patient interview. Outcomes: MAI score, health services use, quality of life (Euroqol 5D-5L), pharmacotherapy and adherence to treatment (Morisky-Green, Haynes-Sackett), and clinical and socio-demographic variables. Statistical analysis: primary outcome is the difference in MAI score between T0 and T1 and corresponding 95% confidence interval. Adjustment for confounding factors will be performed by multilevel analysis. All analyses will be carried out in accordance with the intention-to-treat principle. Discussion: It is essential to provide evidence concerning interventions on PC patients with polypharmacy and multimorbidity, conducted in the context of routine clinical practice, and involving young-old patients with significant potential for preventing negative health outcomes. Trial registration: Clinicaltrials.gov, NCT02866799Publisher PDFPeer reviewe

The evolution of the ventilatory ratio is a prognostic factor in mechanically ventilated COVID-19 ARDS patients

Background: Mortality due to COVID-19 is high, especially in patients requiring mechanical ventilation. The purpose of the study is to investigate associations between mortality and variables measured during the first three days of mechanical ventilation in patients with COVID-19 intubated at ICU admission. Methods: Multicenter, observational, cohort study includes consecutive patients with COVID-19 admitted to 44 Spanish ICUs between February 25 and July 31, 2020, who required intubation at ICU admission and mechanical ventilation for more than three days. We collected demographic and clinical data prior to admission; information about clinical evolution at days 1 and 3 of mechanical ventilation; and outcomes. Results: Of the 2,095 patients with COVID-19 admitted to the ICU, 1,118 (53.3%) were intubated at day 1 and remained under mechanical ventilation at day three. From days 1 to 3, PaO2/FiO2 increased from 115.6 [80.0-171.2] to 180.0 [135.4-227.9] mmHg and the ventilatory ratio from 1.73 [1.33-2.25] to 1.96 [1.61-2.40]. In-hospital mortality was 38.7%. A higher increase between ICU admission and day 3 in the ventilatory ratio (OR 1.04 [CI 1.01-1.07], p = 0.030) and creatinine levels (OR 1.05 [CI 1.01-1.09], p = 0.005) and a lower increase in platelet counts (OR 0.96 [CI 0.93-1.00], p = 0.037) were independently associated with a higher risk of death. No association between mortality and the PaO2/FiO2 variation was observed (OR 0.99 [CI 0.95 to 1.02], p = 0.47). Conclusions: Higher ventilatory ratio and its increase at day 3 is associated with mortality in patients with COVID-19 receiving mechanical ventilation at ICU admission. No association was found in the PaO2/FiO2 variation

Key Factors Associated With Pulmonary Sequelae in the Follow-Up of Critically Ill COVID-19 Patients

Introduction: Critical COVID-19 survivors have a high risk of respiratory sequelae. Therefore, we aimed to identify key factors associated with altered lung function and CT scan abnormalities at a follow-up visit in a cohort of critical COVID-19 survivors. Methods: Multicenter ambispective observational study in 52 Spanish intensive care units. Up to 1327 PCR-confirmed critical COVID-19 patients had sociodemographic, anthropometric, comorbidity and lifestyle characteristics collected at hospital admission; clinical and biological parameters throughout hospital stay; and, lung function and CT scan at a follow-up visit. Results: The median [p25–p75] time from discharge to follow-up was 3.57 [2.77–4.92] months. Median age was 60 [53–67] years, 27.8% women. The mean (SD) percentage of predicted diffusing lung capacity for carbon monoxide (DLCO) at follow-up was 72.02 (18.33)% predicted, with 66% of patients having DLCO < 80% and 24% having DLCO < 60%. CT scan showed persistent pulmonary infiltrates, fibrotic lesions, and emphysema in 33%, 25% and 6% of patients, respectively. Key variables associated with DLCO < 60% were chronic lung disease (CLD) (OR: 1.86 (1.18–2.92)), duration of invasive mechanical ventilation (IMV) (OR: 1.56 (1.37–1.77)), age (OR [per-1-SD] (95%CI): 1.39 (1.18–1.63)), urea (OR: 1.16 (0.97–1.39)) and estimated glomerular filtration rate at ICU admission (OR: 0.88 (0.73–1.06)). Bacterial pneumonia (1.62 (1.11–2.35)) and duration of ventilation (NIMV (1.23 (1.06–1.42), IMV (1.21 (1.01–1.45)) and prone positioning (1.17 (0.98–1.39)) were associated with fibrotic lesions. Conclusion: Age and CLD, reflecting patients’ baseline vulnerability, and markers of COVID-19 severity, such as duration of IMV and renal failure, were key factors associated with impaired DLCO and CT abnormalities

Clustering COVID-19 ARDS patients through the first days of ICU admission. An analysis of the CIBERESUCICOVID Cohort

Background Acute respiratory distress syndrome (ARDS) can be classified into sub-phenotypes according to different inflammatory/clinical status. Prognostic enrichment was achieved by grouping patients into hypoinflammatory or hyperinflammatory sub-phenotypes, even though the time of analysis may change the classification according to treatment response or disease evolution. We aimed to evaluate when patients can be clustered in more than 1 group, and how they may change the clustering of patients using data of baseline or day 3, and the prognosis of patients according to their evolution by changing or not the cluster.Methods Multicenter, observational prospective, and retrospective study of patients admitted due to ARDS related to COVID-19 infection in Spain. Patients were grouped according to a clustering mixed-type data algorithm (k-prototypes) using continuous and categorical readily available variables at baseline and day 3.Results Of 6205 patients, 3743 (60%) were included in the study. According to silhouette analysis, patients were grouped in two clusters. At baseline, 1402 (37%) patients were included in cluster 1 and 2341(63%) in cluster 2. On day 3, 1557(42%) patients were included in cluster 1 and 2086 (57%) in cluster 2. The patients included in cluster 2 were older and more frequently hypertensive and had a higher prevalence of shock, organ dysfunction, inflammatory biomarkers, and worst respiratory indexes at both time points. The 90-day mortality was higher in cluster 2 at both clustering processes (43.8% [n = 1025] versus 27.3% [n = 383] at baseline, and 49% [n = 1023] versus 20.6% [n = 321] on day 3). Four hundred and fifty-eight (33%) patients clustered in the first group were clustered in the second group on day 3. In contrast, 638 (27%) patients clustered in the second group were clustered in the first group on day 3.Conclusions During the first days, patients can be clustered into two groups and the process of clustering patients may change as they continue to evolve. This means that despite a vast majority of patients remaining in the same cluster, a minority reaching 33% of patients analyzed may be re-categorized into different clusters based on their progress. Such changes can significantly impact their prognosis

GLAS : framework to improve assesment in educational videogames

El proceso de evaluación es fundamental para valorar los resultados de cualquier proceso educativo. Además es un elemento crucial en la aceptación de un nuevo proceso educativo por parte de los profesores. Esta evaluación se hace aún más complicada cuando se ha de realizar sobre juegos educativos. Learning Analytics es una nueva disciplina que aboga por colectar la información derivada de la interacción de los estudiantes con cualquier material educativo on-line, para por un lado entender mejor como interactúan los estudiantes con este tipo de material, y por otro, evaluar el resultado de los procesos de aprendizaje. La mayoría de aplicaciones de Learning Analytics que se están realizando en el campo de e-learning son sobre sistemas de gestión de enseñanza o Learning Management System. Los videojuegos educativos, debido a su alto nivel interactivo, presentan el entorno ideal para este tipo de procesos. En este trabajo se presenta la aplicación del proceso de Learning Analytics sobre videojuegos educativos. Este trabajo está organizado en tres partes: en la primera, se presenta la motivación científica del planteamiento de este proyecto, una propuesta de implementación abstracta y una propuesta de implementación concreta sobre el motor de juegos educativos eAdventure como objetivo; en la segunda parte, se detalla todo el proceso de implementación llevado a cabo en la realización del proyecto; y en la tercera y última se presentan algunas de las conclusiones obtenidas y se identifican las líneas de trabajo futuro a desarrollar. [ABSTRACT] Assessment process is key to evaluate results of any educational process. Besides, it is a crucial element in the acceptance of any new assessment process by the teachers. This evaluation gets more complicated when it must be done over educational videogames. Learning Analytics is a new field that advocates of capturing all the data derived from interaction with on-line educational resources, first, to better understand how students interact with this type of resources, and second, to evaluate the educational action itself. Most of Learning Analytics research in e-learning is being deployed over Learning Management Systems. Educational videogames, due to their high interaction level, present the ideal environment for this type of analysis. In this project, application of the Learning Analytics process over educational videogames is presented. This document is organized in three parts: in part I, it is presented the scientific motivation for the project, an abstract implementation approach and a concrete implementation deployed over the eAdventure game engine; in part II, all implementation process is detailed, approaching all the project’s subsystems; and finally, in part III, some conclusions of the project are presented, and some future work to be developed

Look!: framework para aplicaciones de realidad aumentada en Android

Se presenta el framework de aplicaciones de realidad aumentada Look!, desarrollado para el sistema operativo móvil Android. Look! pretende aunar en un sólo framework funcionalidades basicas requeridas en el desarrollo de aplicaciones de realidad aumentada. El framework se valida con cuatro desarrollos prototpicos: una galera de imagenes en 3D, un mundo virtual, un juego interactivo en tres dimensiones y una aplicacion para la creacion de redes sociales con soporte para geolocalizaciónon. Adicionalmente, se han escrito tutoriales que asistan al uso de este framework, y se ha documentado sucientemente su funcionamiento por si otros equipos quisieran continuar con este desarrollo. [ABSTRACT] This document introduces the application framework for augmented reality Look!, developed for the mobile operating system Android. Look! provides basic functionalities required in the development of augmented reality applications. The framework is validated with four prototypic developments: a 3D image gallery, a virtual world, a 3D game, and a location-based social network. Additionally, tutorials assisting the development of applications through this framework have been written, and it is suficiently documented in case this work was continued