Insights from Learning Analytics for Hands-On Cloud Computing Labs in AWS

Cloud computing instruction requires hands-on experience with a myriad of distributed computing services from a public cloud provider. Tracking the progress of the students, especially for online courses, requires one to automatically gather evidence and produce learning analytics in order to further determine the behavior and performance of students. With this aim, this paper describes the experience from an online course in cloud computing with Amazon Web Services on the creation of an open-source data processing tool to systematically obtain learning analytics related to the hands-on activities carried out throughout the course. These data, combined with the data obtained from the learning management system, have allowed the better characterization of the behavior of students in the course. Insights from a population of more than 420 online students through three academic years have been assessed, the dataset has been released for increased reproducibility. The results corroborate that course length has an impact on online students dropout. In addition, a gender analysis pointed out that there are no statistically significant differences in the final marks between genders, but women show an increased degree of commitment with the activities planned in the course

Recopilación automatizada de evidencias de la realización de actividades educativas en el cloud

Resumen: En este artículo se describe una innovación educativa a través de dos experiencias docentes, para automatizar la recopilación de evidencias generadas por los alumnos durante la realización actividades educativas sobre laboratorios computacionales. Estas han sido implementadas en asignaturas del Máster de Computación Paralela y Distribuida (MCPD) de la Universitat Politècnica de València (UPV). Para ello se utiliza la plataforma ODISEA, que permite desplegar y configurar de forma dinámica Entornos Virtuales Computacionales (EVCs) (laboratorios virtuales, simuladores, herramientas software, etc.) necesarios para la puesta en marcha y ejecución de las actividades educativas sobre infraestructuras virtualizadas en un cloud, tanto privado (on-premise) como público. Como resultado, se presentan los mecanismos utilizados para la recopilación automática de evidencias con el fin de realizar un control de la gestión de abuso (uso excesivo) de recursos por parte de los alumnos, y para la creación de un portafolio digital. Además se detalla cómo éstos han sido implementados en los EVCs requeridos en cada actividad, siendo desplegados y configurados automáticamente a través de la plataforma ODISEA.Abstract: This paper describes an educational innovation driven by two teaching experiences, to automatically gather evidences of the activities of the students on computational laboratories. These have been implemented in various courses of the Master’s Degree in Parallel and Distributed Computing (MCPD) at the Universitat Politècnica de València (UPV). For that, we rely on the ODISEA platform, which allows to dynamically deploy and configure Computational Virtual Environments (CVE) (such as virtual laboratories, simulators, software tools, etc.) required to perform educational activities on top of virtual infrastructures both in public and private (on-premise) clouds. We present the approaches carried out to automatically collect the evidences, focusing on detecting abuse and misuse of computational resources and the creation of a digital portfolio. Furthermore, it is outlined how these mechanism have been implemented in the CVEs required in the activities using the capabilities provided by ODISEA platform

Recopilación Automatizada de Evidencias de la Realización de Actividades Educativas en el Cloud

En este artículo se describe una innovación educativa a través de dos experiencias docentes, para automatizar la recopilación de evidencias generadas por los alumnos durante la realización actividades educativas sobre laboratorios computacionales. Estas han sido implementadas en asignaturas del Máster de Computación Paralela y Distribuida (MCPD) de la Universitat Politècnica de València (UPV). Para ello se utiliza la plataforma ODISEA, que permite desplegar y configurar de forma dinámica Entornos Virtuales Computacionales (EVCs) (laboratorios virtuales, simuladores, herramientas software, etc.) necesarios para la puesta en marcha y ejecución de las actividades educativas sobre infraestructuras virtualizadas en un cloud, tanto privado (on-premise) como público. Como resultado, se presentan los mecanismos utilizados para la recopilación automática de evidencias con el fin de realizar un control de la gestión de abuso (uso excesivo) de recursos por parte de los alumnos, y para la creación de un portafolio digital. Además se detalla cómo éstos han sido implementados en los EVCs requeridos en cada actividad, siendo desplegados y configurados automáticamente a través de la plataforma ODISEA.This paper describes an educational innovation driven by two teaching experiences, to automatically gather evidences of the activities of the students on computational laboratories. These have been implemented in various courses of the Master’s Degree in Parallel and Distributed Computing (MCPD) at the Universitat Politècnica de València (UPV). For that, we rely on the ODISEA platform, which allows to dynamically deploy and configure Computational Virtual Environments (CVE) (such as virtual laboratories, simulators, software tools, etc.) required to perform educational activities on top of virtual infrastructures both in public and private (on-premise) clouds. We present the approaches carried out to automatically collect the evidences, focusing on detecting abuse and misuse of computational resources and the creation of a digital portfolio. Furthermore, it is outlined how these mechanism have been implemented in the CVEs required in the activities using the capabilities provided by ODISEA platform.Los autores quieren agradecer al Vicerrectorado de Estudios, Calidad y Acreditación de la Universitat Politècnica de València (UPV) por la financiación del proyecto PIME “Análisis y Evaluación de Impacto del Cloud Computing en la Gestión de entornos Virtuales Computacionales en la Enseñanza”, con referencia A014, en el cual está enmarcado este trabajo

Panel web de gestión automatizada para actividades educativas no presenciales

Esta contribución presenta un recurso docente para automatizar la gestión de actividades educativas no presenciales, que involucren laboratorios computacionales remotos de prácticas. La interacción profesor-alumno es especialmente necesaria en actividades no presenciales, como es el caso de cursos online asíncronos y asignaturas con dispensa de asistencia. Por ello, el panel web permite: i) el envío periódico de mensajes de correo electrónico personalizados para los alumnos; ii) la gestión centralizada de las credenciales de alumnos para los laboratorios remotos; iii) disponer de una visión actualizada del ciclo de vida de los alumnos (alumnos concurrentes, tiempo restante, etc.). Se describe el uso de la herramienta en el contexto de un curso online asíncrono que opera a escala mundial así como su extensión a asignaturas con dispensa de asistencia. El panel web, creado con Google Spreadsheets y liberado bajo licencia Creative Commons, ha permitido la gestión de más de 350 alumnos, automatizar el envío de más de 1000 mensajes personalizados y facilitar las labores de gestión de credenciales de dichas actividades educativas, pudiendo ser fácilmente adaptado a otras actividades educativas afines.This contributions introduces a teaching resource to automate online educational activities that involve remote computational labs. Student-professor interaction is specially necessary in such activities, as is the case of asynchronous online courses and subjects with non-mandatory attendance. The web panel allows: i) to periodically send personalised e-mail messages; ii) the centralised management of student credentials for the remote labs; iii) a dashboard with the lifecycle of students (concurrent students, time left, etc.). The tool has been employed on a worldwide asynchronous online course together with a non-mandatory attendance subject. The web panel, created with Google Spreadsheets and released under a Creative Commons License has enabled to manage more than 350 students, automate more than 1000 personalised messages and ease the credential management. It can be adapted to other educational similar activities.Los autores quieren agradecer al Vicerrectorado de Estudios, Calidad y Acreditación de la UPV por la financiación del proyecto PIME “Análisis y Evaluación de Impacto del Cloud Computing en la Gestión de entornos Virtuales Computacionales en la Enseñanza", con referencia (A014). GM quiere agradecer a l’Escola Tècnica Superior d’Enginyeria Informàtica de la Universitat Politècnica de València el soporte económico para la presentación de este trabajo

Grid as a Service: Herramienta para el despliegue y gestión de un Grid en la nube para actividades educativas

Un Grid se compone de un conjunto de recursos de cómputo y datos ubicados en dominios administrativos diferentes que se gestionan bajo el marco de una Organización Virtual (OV) con el objeto de resolver problemas científicos. Existen OV específicas tanto a nivel nacional (e.g. tut.vo.ibergrid.eu) como a nivel internacional (Testbed Gilda) que proporcionan infraestructuras Grid específicas para uso docente. Sin embargo, estas OVs tienen un uso limitado para la realización de actividades educativas, debido a que estas solo se pueden utilizar desde un punto de vista de un usuario Grid y no como administradores de recursos, dado que son recursos prefijados, imposibilitando agregar o borrar nuevos de forma elástica y dinámica. En este trabajo se presenta un recurso docente que despliega dinámica y elásticamente un Grid as a Service (GaaS) en la nube, utilizando tanto proveedores públicos (Amazon Web Services) como privados (Open-Nebula), en la que se virtualizan los dominios administrativos del Grid y se integran en una OV. Estos GaaS se crean con fines educativos y se pueden emplear tanto para usuarios del Grid como para administradores.A Grid is composed by a set of computational and data resources located in different administrative domain that are managed under the framework of a Virtual Organisation (VO) and aim to resolve scientific problems. There are specific VOs both national (VO tut.vo.ibergrid.eu) and international (Gilda testbed that provide specific Grid infrastructures for educational purposes. However, these VOs have limited interest for educational activities since they can only be used from the Grid user point of view and not as administrators of the resources and services. This is because they use preallocated resources and it is impossible add or remove resources in the existing infrastructure in a dynamic and elastic way. In this work we present an educational resource that dynamically deploys a Grid as a Service (GaaS) on the Cloud, using both public providers (Amazon Web Services) and on-premises (OpenNebula), virtualising the administrative domains of the Grid that are integrated in a VO. These GaaS are used for educational purposes and can be employed both by Grid users and system administrators.Los autores agradecen este trabajo por la financiación recibida por el Vicerrectorado de Estudios, Calidad y Acreditación de la Universitat Politècnica de València (UPV) para desarrollar el Proyecto de Innovación y Mejora Educativa (PIME) “Entornos Virtuales Computacionales para la Evaluación de Competencias Transversales en la Nube”, con referencia A04. GM quiere agradecer a l’Escola Tècnica Superior d’Enginyeria Informàtica de la Universitat Politècnica de València el soporte económico para la presentación de este trabajo

Increasing the Efficiency on Producing Radiology Reports for Breast Cancer Diagnosis by Means of Structured Reports

Background: Radiology reports are commonly written on free-text using voice recognition devices. Structured reports (SR) have a high potential but they are usually considered more difficult to fill-in so their adoption in clinical practice leads to a lower efficiency. However, some studies have demonstrated that in some cases, producing SRs may require shorter time than plain-text ones. This work focuses on the definition and demonstration of a methodology to evaluate the productivity of software tools for producing radiology reports. A set of SRs for breast cancer diagnosis based on BI-RADS have been developed using this method. An analysis of their efficiency with respect to free-text reports has been performed. Material and Methods: The methodology proposed compares the Elapsed Time (ET) on a set of radiological reports. Free-text reports are produced with the speech recognition devices used in the clinical practice. Structured reports are generated using a web application generated with TRENCADIS framework. A team of six radiologists with three different levels of experience in the breast cancer diagnosis was recruited. These radiologists performed the evaluation, each one introducing 50 reports for mammography, 50 for ultrasound scan and 50 for MRI using both approaches. Also, the Relative Efficiency (REF) was computed for each report, dividing the ET of both methods. We applied the T-Student (T-S) test to compare the ETs and the ANOVA test to compare the REFs. Both tests were computed using the SPSS software. Results: The study produced three DICOM-SR templates for Breast Cancer Diagnosis on mammography, ultrasound and MRI, using RADLEX terms based on BIRADs 5th edition. The T-S test on radiologists with high or intermediate profile, showed that the difference between the ET was only statistically significant for mammography and ultrasound. The ANOVA test performed grouping the REF by modalities, indicated that there were no significant differences between mammograms and ultrasound scans, but both have significant statistical differences with MRI. The ANOVA test of the REF for each modality, indicated that there were only significant differences in Mammography (ANOVA p = 0.024) and Ultrasound (ANOVA p = 0.008). The ANOVA test for each radiologist profile, indicated that there were significant differences on the high profile (ANOVA p = 0.028) and medium (ANOVA p = 0.045). Conclusions: In this work, we have defined and demonstrated a methodology to evaluate the productivity of software tools for producing radiology reports in Breast Cancer. We have evaluated that adopting Structured Reporting in mammography and ultrasound studies in breast cancer diagnosis improves the performance in producing reports.INDIGO - DataCloud receives funding from the European Union's Horizon 2020 research and innovation programme under grant agreement RIA 653549.Segrelles Quilis, JD.; Medina, R.; Blanquer Espert, I.; Marti Bonmati, L. (2017). Increasing the Efficiency on Producing Radiology Reports for Breast Cancer Diagnosis by Means of Structured Reports. Methods of Information in Medicine. 56:1-13. https://doi.org/10.3414/ME16-01-0091S1135

Gestión elástica en la nube de recursos computacionales para actividades docentes: caso de uso en el Diseño de Sistemas Digitales

Hoy en día, los entornos computaciones son clave en las titulaciones STEM, dado que mejoran significativamente sus procesos de enseñanza-aprendizaje. Por ello, las universidades buscan la racionalización de las infraestructuras de cómputo que dan soporte a dichos entornos, con el objetivo de abaratar su coste (adquisición y mantenimiento). En este sentido, la nube está empujando a las universidades a moverse hacia un modelo de “pago por uso” de las infraestructuras, por lo que se requieren nuevos e innovadores servicios que gestionen dicho modelo de forma eficiente. En este artículo se presenta el recurso docente Cluster Elasticity Manager (CEM) cuyo fin es gestionar eficientemente en la nube las infraestructuras que dan soporte a entornos computacionales en actividades educativas que necesitan de elasticidad. Para el diseño de dicho recurso, se ha tomado como punto de partida un escenario de referencia donde se desarrollan actividades educativas diseñadas para formar alumnos en el Diseño de Sistemas Digitales. Finalmente, se presenta un análisis de tiempos en el despliegue de los entornos computacionales requeridos en el escenario de referencia planteado a través de CEM.Nowadays, computing environments are key in STEM degrees, since they significantly improve their teaching-learning processes. For that, universities seek to rationalize the computing infrastructures that support these environments, with the aim of lowering their cost (acquisition and maintenance). In this sense, the cloud is pushing universities towards a “pay-per-us” model of infrastructures, which is why new and innovative services are required to manage this model efficiently. This article presents the teaching resource Cluster Elasticity Manager (CEM), which objective is to efficiently manage in the cloud the elastic infrastructures that support computer environments in educational activities. For the design of this resource, a reference scenario has been analysed as a starting point, where educational activities designed to train students in the Design of Digital Systems are performed. Finally, an analysis of time is presented in the deployment of the computational environments required in the reference scenario proposed through CEM.Los autores agradecen este trabajo por la financiación recibida por el Vicerrectorado de Estudios, Calidad y Acreditación de la Universitat Politècnica de València (UPV) para desarrollar el Proyecto de Innovación y Mejora Educativa (PIME) “Comunidades de Aprendizaje como servicios en la nube para el desarrollo y evaluación automática de Competencias Transversales y Objetivos Formativos específicos”, con referencia B29

Improving knowledge management through the support of image examination and data annotation using DICOM structured reporting

[EN] An important effort has been invested on improving the image diagnosis process in different medical areas using information technologies. The field of medical imaging involves two main data types: medical imaging and reports. Developments based on the DICOM standard have demonstrated to be a convenient and widespread solution among the medical community. The main objective of this work is to design a Web application prototype that will be able to improve diagnosis and follow-on of breast cancer patients. It is based on TRENCADIS middleware, which provides a knowledge-oriented storage model composed by federated repositories of DICOM image studies and DICOM-SR medical reports. The full structure and contents of the diagnosis reports are used as metadata for indexing images. The TRENCADIS infrastructure takes full advantage of Grid technologies by deploying multi-resource grid services that enable multiple views (reports schemes) of the knowledge database. The paper presents a real deployment of such Web application prototype in the Dr. Peset Hospital providing radiologists with a tool to create, store and search diagnostic reports based on breast cancer explorations (mammography, magnetic resonance, ultrasound, pre-surgery biopsy and post-surgery biopsy), improving support for diagnostics decisions. A technical details for use cases (outlining enhanced multi-resource grid services communication and processing steps) and interactions between actors and the deployed prototype are described. As a result, information is more structured, the logic is clearer, network messages have been reduced and, in general, the system is more resistant to failures.The authors wish to thank the financial support received from The Spanish Ministry of Education and Science to develop the project "CodeCloud", with reference TIN2010-17804.Salavert Torres, J.; Segrelles Quilis, JD.; Blanquer Espert, I.; Hernández García, V. (2012). Improving knowledge management through the support of image examination and data annotation using DICOM structured reporting. Journal of Biomedical Informatics. 45(6):1066-1074. https://doi.org/10.1016/j.jbi.2012.07.004S1066107445

An energy-efficient internet of things (IoT) architecture for preventive conservation of cultural heritage

[EN] Internet of Things (IoT) technologies can facilitate the preventive conservation of cultural heritage (CH) by enabling the management of data collected from electronic sensors. This work presents an IoT architecture for this purpose. Firstly, we discuss the requirements from the artwork standpoint, data acquisition, cloud processing and data visualization to the end user. The results presented in this work focuses on the most critical aspect of the architecture, which are the sensor nodes. We designed a solution based on LoRa and Sigfox technologies to produce the minimum impact in the artwork, achieving a lifespan of more than 10 years. The solution will be capable of scaling the processing and storage resources, deployed either in a public or on-premise cloud, embedding complex predictive models. This combination of technologies can cope with different types of cultural heritage environments.This work was partially funded by the Generalitat Valenciana project AICO/2016/058 and by the Plan Nacional de I+D, Comision Interministerial de Ciencia y TecnologiA (FEDER-CICYT) under the project HAR2013-47895-C2-1-P.Perles Ivars, A.; Pérez Marín, E.; Mercado Romero, R.; Segrelles Quilis, JD.; Blanquer Espert, I.; Zarzo Castelló, M.; García Diego, FJ. (2018). An energy-efficient internet of things (IoT) architecture for preventive conservation of cultural heritage. Future Generation Computer Systems. 81:566-581. https://doi.org/10.1016/j.future.2017.06.030S5665818

Recopilación automatizada de evidencias de la realización de actividades educativas en el cloud

Resumen: En este artículo se describe una innovación educativa a través de dos experiencias docentes, para automatizar la recopilación de evidencias generadas por los alumnos durante la realización actividades educativas sobre laboratorios computacionales. Estas han sido implementadas en asignaturas del Máster de Computación Paralela y Distribuida (MCPD) de la Universitat Politècnica de València (UPV). Para ello se utiliza la plataforma ODISEA, que permite desplegar y configurar de forma dinámica Entornos Virtuales Computacionales (EVCs) (laboratorios virtuales, simuladores, herramientas software, etc.) necesarios para la puesta en marcha y ejecución de las actividades educativas sobre infraestructuras virtualizadas en un cloud, tanto privado (on-premise) como público. Como resultado, se presentan los mecanismos utilizados para la recopilación automática de evidencias con el fin de realizar un control de la gestión de abuso (uso excesivo) de recursos por parte de los alumnos, y para la creación de un portafolio digital. Además se detalla cómo éstos han sido implementados en los EVCs requeridos en cada actividad, siendo desplegados y configurados automáticamente a través de la plataforma ODISEA.Abstract: This paper describes an educational innovation driven by two teaching experiences, to automatically gather evidences of the activities of the students on computational laboratories. These have been implemented in various courses of the Master’s Degree in Parallel and Distributed Computing (MCPD) at the Universitat Politècnica de València (UPV). For that, we rely on the ODISEA platform, which allows to dynamically deploy and configure Computational Virtual Environments (CVE) (such as virtual laboratories, simulators, software tools, etc.) required to perform educational activities on top of virtual infrastructures both in public and private (on-premise) clouds. We present the approaches carried out to automatically collect the evidences, focusing on detecting abuse and misuse of computational resources and the creation of a digital portfolio. Furthermore, it is outlined how these mechanism have been implemented in the CVEs required in the activities using the capabilities provided by ODISEA platform