Basic life support teaching in the second year pregraduate medical curriculum: quality of acquisition after 6 months

Purpose of the study: Basic life support (BLS) and automated externaldefibrillation (AED) represent important skills to be acquired duringpregraduate medical training. Since 3 years, our medical school hasintroduced a BLS-AED course (with certification) for all second yearmedical students. Few reports about quality and persistence over timeof BLS-AED learning are available to date in the medical literature.Comprehensive evaluation of students' acquired skills was performedat the end of the 2008 academic year, 6 month after certification.Materials and methods: The students (N = 142) were evaluated duringa 9 minutes «objective structured clinical examination» (OSCE) station.Out of a standardized scenario, they had to recognize a cardiac arrestsituation and start a resuscitation process. Their performance wererecorded on a PC using an Ambuman(TM) mannequin and the AmbuCPR software kit(TM) during a minimum of 8 cycles (30 compressions:2 ventilations each). BLS parameters were systematically checked. Nostudent-rater interactions were allowed during the whole evaluation.Results: Response of the victim was checked by 99% of the students(N = 140), 96% (N = 136) called for an ambulance and/or an AED. Openthe airway and check breathing were done by 96% (N = 137), 92% (N =132) gave 2 rescue breaths. Pulse was checked by 95% (N=135), 100%(N = 142) begun chest compression, 96% (N = 136) within 1 minute.Chest compression rate was 101 ± 18 per minute (mean ± SD), depthcompression 43 ± 8 mm, 97% (N = 138) respected a compressionventilationratio of 30:2.Conclusions: Quality of BLS skills acquisition is maintained during a6-month period after a BLS-AED certification. Main targets of 2005 AHAguidelines were well respected. This analysis represents one of thelargest evaluations of specific BLS teaching efficiency reported. Furtherfollow-up is needed to control the persistence of these skills during alonger time period and noteworthy at the end of the pregraduatemedical curriculum.

Uncertainty Quantification Of A Semi-Supervised Support Vector Regression Reservoir Model

Uncertainty quantification of petroleum reservoir models is one of the present challenges, which is usually approached with a wide range of geostatistical tools linked with statistical optimisation or/and inference algorithms. Recent advances in machine learning offer a novel approach to model spatial distribution of petrophysical properties in complex reservoirs alternative to geostatistics. The approach is based of semisupervised learning, which handles both ?labelled? observed data and ?unlabelled? data, which have no measured value but describe prior knowledge and other relevant data in forms of manifolds in the input space where the modelled property is continuous. Proposed semi-supervised Support Vector Regression (SVR) model has demonstrated its capability to represent realistic geological features and describe stochastic variability and non-uniqueness of spatial properties. On the other hand, it is able to capture and preserve key spatial dependencies such as connectivity of high permeability geo-bodies, which is often difficult in contemporary petroleum reservoir studies. Semi-supervised SVR as a data driven algorithm is designed to integrate various kind of conditioning information and learn dependences from it. The semi-supervised SVR model is able to balance signal/noise levels and control the prior belief in available data. In this work, stochastic semi-supervised SVR geomodel is integrated into Bayesian framework to quantify uncertainty of reservoir production with multiple models fitted to past dynamic observations (production history). Multiple history matched models are obtained using stochastic sampling and/or MCMC-based inference algorithms, which evaluate posterior probability distribution. Uncertainty of the model is described by posterior probability of the model parameters that represent key geological properties: spatial correlation size, continuity strength, smoothness/variability of spatial property distribution. The developed approach is illustrated with a fluvial reservoir case. The resulting probabilistic production forecasts are described by uncertainty envelopes. The paper compares the performance of the models with different combinations of unknown parameters and discusses sensitivity issues.

Learning spatial filters for multispectral image segmentation

We present a novel filtering method for multispectral satellite image classification. The proposed method learns a set of spatial filters that maximize class separability of binary support vector machine (SVM) through a gradient descent approach. Regularization issues are discussed in detail and a Frobenius-norm regularization is proposed to efficiently exclude uninformative filters coefficients. Experiments carried out on multiclass one-against-all classification and target detection show the capabilities of the learned spatial filters.

Discovering the campus together: a mobile and computer-based learning experience

One of the most relevant difficulties faced by first-year undergraduate students is to settle into the educational environment of universities. This paper presents a case study that proposes a computer-assisted collaborative experience designed to help students in their transition from high school to university. This is done by facilitating their first contact with the campus and its services, the university community, methodologies and activities. The experience combines individual and collaborative activities, conducted in and out of the classroom, structured following the Jigsaw Collaborative Learning Flow Pattern. A specific environment including portable technologies with network and computer applications has been developed to support and facilitate the orchestration of a flow of learning activities into a single integrated learning setting. The result is a Computer-Supported Collaborative Blended Learning scenario, which has been evaluated with first-year university students of the degrees of Software and Audiovisual Engineering within the subject Introduction to Information and Communications Technologies. The findings reveal that the scenario improves significantly students’ interest in their studies and their understanding about the campus and services provided. The environment is also an innovative approach to successfully support the heterogeneous activities conducted by both teachers and students during the scenario. This paper introduces the goals and context of the case study, describes how the technology was employed to conduct the learning scenario, the evaluation methods and the main results of the experience.

Supporting the reuse of effective CSCL learning designs through social structure representations

Distance and blended collaborative learning settings are usually characterized by different social structures defined in terms of groups' number, dimension, and composition; these structures are variable and can change within the same activity. This variability poses additional complexity to instructional designers, when they are trying to develop successful experiences from existing designs. This complexity is greatly associated with the fact that learning designs do not render explicit how social structures influenced the decisions of the original designer, and thus whether the social structures of the new setting could preclude the effectiveness of the reused design. This article proposes the usage of new representations (social structure representations, SSRs) able to support unskilled designers in reusing existing learning designs, through the explicit characterization of the social structures and constraints embedded either by the original designers or the reusing teachers, according to well-known principles of good collaborative learning practice. The article also describes an evaluation process that involved university professors, as well as the main findings derived from it. This process supported the initial assumptions about the effectiveness of SSRs, with significant evidence from both qualitative and qualitative data.

Computational Representation of Collaborative Learning Flow Patterns Using IMS Learning Design

The identification and integration of reusable and customizable CSCL (Computer Supported Collaborative Learning) may benefit from the capture of best practices in collaborative learning structuring. The authors have proposed CLFPs (Collaborative Learning Flow Patterns) as a way of collecting these best practices. To facilitate the process of CLFPs by software systems, the paper proposes to specify these patterns using IMS Learning Design (IMS-LD). Thus, teachers without technical knowledge can particularize and integrate CSCL tools. Nevertheless, the support of IMS-LD for describing collaborative learning activities has some deficiencies: the collaborative tools that can be defined in these activities are limited. Thus, this paper proposes and discusses an extension to IMS-LD that enables to specify several characteristics of the use of tools that mediate collaboration. In order to obtain a Unit of Learning based on a CLFP, a three stage process is also proposed. A CLFP-based Unit of Learning example is used to illustrate the process and the need of the proposed extension.

LdShake: learning design solutions sharing and co-edition

Two important challenges that teachers are currently facing are the sharing and the collaborative authoring of their learning design solutions, such as didactical units and learning materials. On the one hand, there are tools that can be used for the creation of design solutions and only some of them facilitate the co-edition. However, they do not incorporate mechanisms that support the sharing of the designs between teachers. On the other hand, there are tools that serve as repositories of educational resources but they do not enable the authoring of the designs. In this paper we present LdShake, a web tool whose novelty is focused on the combined support for the social sharing and co-edition of learning design solutions within communities of teachers. Teachers can create and share learning designs with other teachers using different access rights so that they can read, comment or co-edit the designs. Therefore, each design solution is associated to a group of teachers able to work on its definition, and another group that can only see the design. The tool is generic in that it allows the creation of designs based on any pedagogical approach. However, it can be particularized in instances providing pre-formatted designs structured according to a specific didactic method (such as Problem-Based Learning, PBL). A particularized LdShake instance has been used in the context of Human Biology studies where teams of teachers are required to work together in the design of PBL solutions. A controlled user study, that compares the use of a generic LdShake and a Moodle system, configured to enable the creation and sharing of designs, has been also carried out. The combined results of the real and controlled studies show that the social structure, and the commenting, co-edition and publishing features of LdShake provide a useful, effective and usable approach for facilitating teachers' teamwork.

Personalized modeling for drug concentration prediction using Support Vector Machine

Building a personalized model to describe the drug concentration inside the human body for each patient is highly important to the clinical practice and demanding to the modeling tools. Instead of using traditional explicit methods, in this paper we propose a machine learning approach to describe the relation between the drug concentration and patients' features. Machine learning has been largely applied to analyze data in various domains, but it is still new to personalized medicine, especially dose individualization. We focus mainly on the prediction of the drug concentrations as well as the analysis of different features' influence. Models are built based on Support Vector Machine and the prediction results are compared with the traditional analytical models.

CLFP intrinsic constraints-based group management of blended learning situations

When applying a Collaborative Learning Flow Pattern (CLFP) to structure sequences of activities in real contexts, one of the tasks is to organize groups of students according to the constraints imposed by the pattern. Sometimes,unexpected events occurring at runtime force this pre-defined distribution to be changed. In such situations, an adjustment of the group structures to be adapted to the new context is needed. If the collaborative pattern is complex, this group redefinitionmight be difficult and time consuming to be carried out in real time. In this context, technology can help on notifying the teacher which incompatibilitiesbetween the actual context and the constraints imposed by the pattern. This chapter presents a flexible solution for supporting teachers in the group organization profiting from the intrinsic constraints defined by a CLFPs codified in IMS Learning Design. A prototype of a web-based tool for the TAPPS and Jigsaw CLFPs and the preliminary results of a controlled user study are alsopresented as a first step towards flexible technological systems to support grouping tasks in this context.

Time Series Input Selection using Multiple Kernel Learning

In this paper we study the relevance of multiple kernel learning (MKL) for the automatic selection of time series inputs. Recently, MKL has gained great attention in the machine learning community due to its flexibility in modelling complex patterns and performing feature selection. In general, MKL constructs the kernel as a weighted linear combination of basis kernels, exploiting different sources of information. An efficient algorithm wrapping a Support Vector Regression model for optimizing the MKL weights, named SimpleMKL, is used for the analysis. In this sense, MKL performs feature selection by discarding inputs/kernels with low or null weights. The approach proposed is tested with simulated linear and nonlinear time series (AutoRegressive, Henon and Lorenz series).

Electrical neuroimaging of memory discrimination based on single-trial multisensory learning.

Multisensory experiences influence subsequent memory performance and brain responses. Studies have thus far concentrated on semantically congruent pairings, leaving unresolved the influence of stimulus pairing and memory sub-types. Here, we paired images with unique, meaningless sounds during a continuous recognition task to determine if purely episodic, single-trial multisensory experiences can incidentally impact subsequent visual object discrimination. Psychophysics and electrical neuroimaging analyses of visual evoked potentials (VEPs) compared responses to repeated images either paired or not with a meaningless sound during initial encounters. Recognition accuracy was significantly impaired for images initially presented as multisensory pairs and could not be explained in terms of differential attention or transfer of effects from encoding to retrieval. VEP modulations occurred at 100-130ms and 270-310ms and stemmed from topographic differences indicative of network configuration changes within the brain. Distributed source estimations localized the earlier effect to regions of the right posterior temporal gyrus (STG) and the later effect to regions of the middle temporal gyrus (MTG). Responses in these regions were stronger for images previously encountered as multisensory pairs. Only the later effect correlated with performance such that greater MTG activity in response to repeated visual stimuli was linked with greater performance decrements. The present findings suggest that brain networks involved in this discrimination may critically depend on whether multisensory events facilitate or impair later visual memory performance. More generally, the data support models whereby effects of multisensory interactions persist to incidentally affect subsequent behavior as well as visual processing during its initial stages.

The art and learning patterns of knowing in nursing

Objective To identify the perception of the students about the use of art as a pedagogical strategy in learning the patterns of knowing in nursing; to identify the dimensions of each pattern valued in the analysis of pieces of art. Method Descriptive mixed study. Data collection used a questionnaire applied to 31 nursing students. Results In the analysis of the students’ discourse, it was explicit that empirical knowledge includes scientific knowledge, tradition and nature of care. The aesthetic knowledge implies expressiveness, subjectivity and sensitivity. Self-knowledge, experience, reflective attitude and relationships with others are the subcategories of personal knowledge and the moral and ethics support ethical knowledge. Conclusion It is possible to learn patterns of knowledge through art, especially the aesthetic, ethical and personal. It is necessary to investigate further pedagogical strategies that contribute to the learning patterns of nursing knowledge.

Effects of web-based electrocardiography simulation on strategies and learning styles

OBJECTIVETo identify the association between the use of web simulation electrocardiography and the learning approaches, strategies and styles of nursing degree students.METHODA descriptive and correlational design with a one-group pretest-posttest measurement was used. The study sample included 246 students in a Basic and Advanced Cardiac Life Support nursing class of nursing degree.RESULTSNo significant differences between genders were found in any dimension of learning styles and approaches to learning. After the introduction of web simulation electrocardiography, significant differences were found in some item scores of learning styles: theorist (p < 0.040), pragmatic (p < 0.010) and approaches to learning.CONCLUSIONThe use of a web electrocardiogram (ECG) simulation is associated with the development of active and reflexive learning styles, improving motivation and a deep approach in nursing students.

Mise en Place d’un Dispositif de Formation a Distance a l’universite du Cap-Vert« e-learning » :Les Technologies de L’information et de la Communication dans L’enseignement du Français Langue Etrangere

Ce mémoire est l’occasion de partager le résultat de la mise en place de ce dispositif de formation à distance que nous avons mené dans l’université du Cap-Vert. Dans la première partie, nous décrirons la nature de la politique éducative au Cap-Vert. Nous contextualiserons les principes de l’installation de l’université publique dans ce pays, ainsi que les intentions d’innovation pédagogique de cette université. La deuxième partie portera un regard complémentaire sur l’utilisation des TIC et de l’internet dans l’enseignement/apprentissage d’une langue, cas du français langue étrangère, et nous nous inspirerons des théories constructiviste et socio-constructiviste. Finalement, la troisième partie détaillera toutes les étapes de la conception et de la mise en place du dispositif de formation à distance. Dans cette troisième partie, nous aborderons dans un premier temps la question des enjeux et des risques du e-Learning et nous présenterons notre mission dans le projet « e-Learning.cv » mené par l’université. Puis, dans un deuxième temps nous analyserons quelques cours que nous avons mis en ligne, en sachant qu’un cours en ligne n’est pas la simple reproduction d’un support pédagogique imprimé mais il offre à l’apprenant un environnement multimédia et interactif. Finalement dans un troisième temps nous essayerons de prendre un peu de recul pour faire une analyse critique de ce que nous avons réalisé et essayer par là même de dégager les perspectives pour améliorer le travail effectué.

A influência do e-learning no capital humano

Na Europa e nas últimas décadas do Século XX, a emergência da Sociedade de Informação veio impor às organizações a necessidade de que, para além das inovações tecnológicas, haja uma preocupação relativamente aos bens intangíveis como a informação, as novas metodologias de trabalho e o know how (Batista, 2002). Paralelamente a estas inovações, as Instituições de Ensino Superior têm contribuído para a evolução do Capital Humano, como ativo intangível intrínseco ao Homem. Em Portugal e no contexto do Ensino/Formação a Distância parecem continuar a existir, ainda, em algumas instituições, problemas de identificação, e de descriminação das vantagens no que concerne à estrutura aberta e flexível, com o estudante/formando a ter algumas dificuldades em adaptar o seu perfil e interesses profissionais ao tipo de aprendizagem que mais se lhe adequa. O e-learning surge como um método de Ensino/Formação a Distância, só possível com a especificidade dos processos pedagógicos e em complementaridade com as Tecnologias de Informação e Comunicação (TIC), uma vez que são estas que lhe dão o suporte necessário à sua concretização. O e-learning ao proporcionar novas formas de comunicação, de interação e de confronto de ideias, permite uma aprendizagem baseada na partilha de saberes, tendo em consideração as experiências e os objetivos profissionais dos formandos. Dentro destes pressupostos, achámos importante fazer uma investigação a partir de Instituições de Ensino Superior Portuguesas, de modo a percebermos qual o papel e a influência que o e-learning desempenha nos objetivos das organizações académicas em geral e no Capital Humano dos seus Estudantes/Formandos em particular. A partir da questão da investigação foram definidos os objetivos e hipóteses de investigação de modo a que ao ser enunciada uma metodologia esta englobe fatores que foquem os elementos necessários à confirmação, ou não, dos pressupostos enunciados. Foi analisada documentação diversa, criado um questionário e conduzidas entrevistas, de modo a obter e potenciar a informação necessária e suficiente para o efeito. A recolha de dados para posterior análise e os resultados depois de interpretados, permitirão responder aos propósitos expressos desde o início da investigação.