Xarxa d'innovació docent sobre aprenentatge cooperatiu (XIDAC)

Presentació de la XIDAC a la Jornada de Bones Pràctiques 2010

Nuevas metodologías docentes aplicadas en el aula. El aprendizaje cooperativo y el aprendizaje colaborativo: el trabajo en grupo

Con la implantación del Espacio Europeo de Educación Superior, tanto el alumnado como el profesorado en su totalidad, se ven ante la necesidad de modificar las técnicas tanto de aprendizaje, desde la perspectiva del alumno, como las metodologías de enseñanza, desde el punto de vista del profesor, a la hora de transmitir el conocimiento. La presente comunicación trata de estudiar la incidencia que el aprendizaje colaborativo tiene en el seno del aula, teniendo presente la formación de grupos reducidos tomando como referencia la totalidad del número de alumnos matriculados. Al mismo tiempo, se trata de analizar la adquisición de diferentes competencias transversales por parte de los alumnos participantes en la experiencia, como, por ejemplo, la obtención de habilidades en las relaciones interpersonales, la capacidad de organización y planificación y la capacidad de gestión de la información. Al mismo tiempo, se hace necesario poner de manifiesto que las conclusiones del presente estudio, se basan en las encuestas de autoevaluación que, los propios alumnos, han completado al finalizar la experiencia

Aplicació de tècniques de creativitat a assignatures d'enginyeria

'Dibuix i disseny industrial' és una assignatura optativa dins dels estudis (en procés d'extinció) d'enginyeria industrial i mecànica de la Universitat de Girona. La distribució és de 5 crèdits, repartits en 3 d'aula de teoria i 2 d'aula informàtica. L'assignatura té uns continguts i unes competències amb una forta component tècnica. Els professors, després d'estudiar iniciatives semblants a nivell internacional, hem considerat que el treball en grup, l'aprenentatge mitjançant projecte (PBL) i la utilització de tècniques creatives, són les estratègies més adequades per aconseguir desenvolupar les competències. Presentem la nostra experiència en la forma del procés adoptat i els resultats obtinguts

Propuesta de autogestión del aprendizaje basada en principios del deporte de competición

La propuesta se lleva a cabo en una asignatura de la licenciatura de CC. de la Actividad Física y del Deporte denominada Enseñanza del Voleibol. La adquisición del conocimiento se convierte en un reto colectivo. Se forman equipos y se delega el protagonismo en el proceso de la consecución de ese reto en el equipo. Se crea un sistema complejo de evaluación que genera interdependencia y la necesidad de entablar mecanismos de cooperación. Se muestra una mejora en las pruebas teóricas como en las prácticas. Además, el nivel de satisfacción de los alumnos y la valoración de la asignatura son muy elevados

Una Experiencia de trabajo cooperativo en economía de la empresa

El objeto de este trabajo se centra en el aprendizaje cooperativo. En concreto en el análisis de la introducción de una experiencia de este tipo en el segundo curso de una de las nuevas titulaciones de grado. Se pretende analizar entre otras cuestiones la puesta en marcha, la valoración del alumnado sobre este modo de trabajo, la cohabitación con otros medios de evaluación y trabajo más tradicionales y por último su efecto sobre los resultados finales del alumno en la asignatura Learning -- Evaluation Aprenentatge -- Avaluació Group work in education

Crear, colaborar y aprender con internet en lengua extranjera: el uso de la webquest

El EEES establece entre sus objetivos cambios metodológicos e innovaciones docentes. En este sentido, creemos que el uso de internet en general y de la actividad de la webquest en particular pueden constituir una metodología muy eficaz y altamente motivadora tanto para docentes como para estudiantes, aparte de favorecer un aprendizaje activo, significativo y colaborativo en clase de lengua extranjera. En nuestra comunicación mostraremos, por un lado, en qué consiste la webquest ideada, los elementos que la componen y los objetivos que persigue y, por otro, en qué medida favorece el aprendizaje significativo y colaborativo, cómo desarrolla el pensamiento crítico y creativo del estudiante y el papel que se espera del profesor y del alumno en este tipo de actividad

Content-based VLE designs improve learning efficiency in constructivist statistics education

Background: We introduced a series of computer-supported workshops in our undergraduate statistics courses, in the hope that it would help students to gain a deeper understanding of statistical concepts. This raised questions about the appropriate design of the Virtual Learning Environment (VLE) in which such an approach had to be implemented. Therefore, we investigated two competing software design models for VLEs. In the first system, all learning features were a function of the classical VLE. The second system was designed from the perspective that learning features should be a function of the course's core content (statistical analyses), which required us to develop a specific-purpose Statistical Learning Environment (SLE) based on Reproducible Computing and newly developed Peer Review (PR) technology. Objectives: The main research question is whether the second VLE design improved learning efficiency as compared to the standard type of VLE design that is commonly used in education. As a secondary objective we provide empirical evidence about the usefulness of PR as a constructivist learning activity which supports non-rote learning. Finally, this paper illustrates that it is possible to introduce a constructivist learning approach in large student populations, based on adequately designed educational technology, without subsuming educational content to technological convenience. Methods: Both VLE systems were tested within a two-year quasi-experiment based on a Reliable Nonequivalent Group Design. This approach allowed us to draw valid conclusions about the treatment effect of the changed VLE design, even though the systems were implemented in successive years. The methodological aspects about the experiment's internal validity are explained extensively. Results: The effect of the design change is shown to have substantially increased the efficiency of constructivist, computer-assisted learning activities for all cohorts of the student population under investigation. The findings demonstrate that a content-based design outperforms the traditional VLE-based design. © 2011 Wessa et al.

Automating marine mammal detection in aerial images captured during wildlife surveys: A deep learning approach

Aerial surveys conducted using manned or unmanned aircraft with customized camera payloads can generate a large number of images. Manual review of these images to extract data is prohibitive in terms of time and financial resources, thus providing strong incentive to automate this process using computer vision systems. There are potential applications for these automated systems in areas such as surveillance and monitoring, precision agriculture, law enforcement, asset inspection, and wildlife assessment. In this paper, we present an efficient machine learning system for automating the detection of marine species in aerial imagery. The effectiveness of our approach can be credited to the combination of a well-suited region proposal method and the use of Deep Convolutional Neural Networks (DCNNs). In comparison to previous algorithms designed for the same purpose, we have been able to dramatically improve recall to more than 80% and improve precision to 27% by using DCNNs as the core approach.

Joint Channel Estimation and Data Detection in MIMO-OFDM Systems: A Sparse Bayesian Learning Approach

The impulse response of wireless channels between the N-t transmit and N-r receive antennas of a MIMO-OFDM system are group approximately sparse (ga-sparse), i.e., NtNt the channels have a small number of significant paths relative to the channel delay spread and the time-lags of the significant paths between transmit and receive antenna pairs coincide. Often, wireless channels are also group approximately cluster-sparse (gac-sparse), i.e., every ga-sparse channel consists of clusters, where a few clusters have all strong components while most clusters have all weak components. In this paper, we cast the problem of estimating the ga-sparse and gac-sparse block-fading and time-varying channels in the sparse Bayesian learning (SBL) framework and propose a bouquet of novel algorithms for pilot-based channel estimation, and joint channel estimation and data detection, in MIMO-OFDM systems. The proposed algorithms are capable of estimating the sparse wireless channels even when the measurement matrix is only partially known. Further, we employ a first-order autoregressive modeling of the temporal variation of the ga-sparse and gac-sparse channels and propose a recursive Kalman filtering and smoothing (KFS) technique for joint channel estimation, tracking, and data detection. We also propose novel, parallel-implementation based, low-complexity techniques for estimating gac-sparse channels. Monte Carlo simulations illustrate the benefit of exploiting the gac-sparse structure in the wireless channel in terms of the mean square error (MSE) and coded bit error rate (BER) performance.

Building knowledge capabilities : an organisational learning approach

The communal nature of knowledge production predicts the importance of creating learning organisations where knowledge arises out of processes that are personal, social, situated and active. It follows that workplaces must provide both formal and informal learning opportunities for interaction with ideas and among individuals. This grounded theory for developing contemporary learning organisations harvests insights from the knowledge management, systems sciences, and educational learning literatures. The resultant hybrid theoretical framework informs practical application, as reported in a case study that harnesses the accelerated information exchange possibilities enabled through web 2.0 social networking and peer production technologies. Through complementary organisational processes, 'meaning making' is negotiated in formal face-to-face meetings supplemented by informal 'boundary spanning' dialogue. The organisational capacity building potential of this participatory and inclusive approach is illustrated through the example of the Dr. Martin Luther King, Jr. Library in San Jose, California, USA. As an outcome of the strategic planning process at this joint city-university library, communication, decision-making, and planning structures, processes, and systems were re-invented. An enterprise- level redesign is presented, which fosters contextualising information interactions for knowledge sharing and community building. Knowledge management within this context envisions organisations as communities where knowledge, identity, and learning are situated. This framework acknowledges the social context of learning - i.e., that knowledge is acquired and understood through action, interaction, and sharing with others. It follows that social networks provide peer-to-peer enculturation through intentional exchange of tacit information made explicit. This, in turn, enables a dynamic process experienced as a continuous spiral that perpetually elevates collective understanding and enables knowledge creation.

Prediction of Candidate Primary Immunodeficiency Disease Genes Using a Support Vector Machine Learning Approach

Screening and early identification of primary immunodeficiency disease (PID) genes is a major challenge for physicians. Many resources have catalogued molecular alterations in known PID genes along with their associated clinical and immunological phenotypes. However, these resources do not assist in identifying candidate PID genes. We have recently developed a platform designated Resource of Asian PDIs, which hosts information pertaining to molecular alterations, protein-protein interaction networks, mouse studies and microarray gene expression profiling of all known PID genes. Using this resource as a discovery tool, we describe the development of an algorithm for prediction of candidate PID genes. Using a support vector machine learning approach, we have predicted 1442 candidate PID genes using 69 binary features of 148 known PID genes and 3162 non-PID genes as a training data set. The power of this approach is illustrated by the fact that six of the predicted genes have recently been experimentally confirmed to be PID genes. The remaining genes in this predicted data set represent attractive candidates for testing in patients where the etiology cannot be ascribed to any of the known PID genes.

Assessment of crop insect damage using unmanned aerial systems: A machine learning approach

Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising technology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of the approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labeling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means. The outcome of this approach is a soft K-means algorithm similar to the EM algorithm for Gaussian mixture models. The results show the algorithm delivers decision boundaries that consistently classify the field into three clusters, one for each crop health level. The methodology presented in this paper represents a venue for further research towards automated crop damage assessments and biosecurity surveillance.