Los enfoques de aprendizaje en estudiantes universitarios Catalanes mediante el approaches and study skills inventory for students (ASSIST) = Learning Approaches of Catalan University Students Measured with the Approaches and Study Skills Inventory for Students (ASSIST)

The aim of this study is to measure the psychometric properties of a Catalan translation of the Approaches and Study Skills Inventory for Students (ASSIST), and to analyse the different learning styles used by university students, considering the influence of gender and type of studies. The instrument was administered to 834 students at the University of Girona. The results showed that most students interviewed had a deep approach to learning, although the analysis by gender showed that females tended to use a more strategic approach, while males used a deep approach predominantly. As to whether the type of studies influenced learning styles, a prevalence of deep approach was found among Science and Technology students, while a more strategic approach was found among Humanities and Education students

Learning to be literate : parental empowerment in early literacy interventions

Considerable research has focused on the success of early intervention programs for children. However, minimal research has focused on the effect these programs have on the parents of targeted children. Many current early intervention programs champion family-focused and inclusive programming, but few have evaluated parent participation in early interventions and fewer still have evaluated the impact of these programs on beliefs and attitudes and parenting practices. Since parents will continue to play a key role in their child's developmental course long after early intervention programs end, it is vital to examine whether these programs empower parents to take action to make changes in the lives of their children. The goal of this study was to understand parental influences on the early development of literacy, and in particular how parental attitudes, beliefs and self efficacy impact parent and child engagement in early literacy intervention activities. A mixed method procedure using quantitative and qualitative strategies was employed. A quasi-experimental research design was used. The research sample, sixty parents who were part of naturally occurring community interventions in at- risk neighbourhoods in a south-western Ontario city participated in the quantitative phase. Largely individuals whose home language was other than English, these participants were divided amongst three early literacy intervention groups, a Prescriptive Interventionist type group, a Participatory Empowering type group and a drop-in parent- child neighbourhood Control group. Measures completed pre and post a six session literacy intervention, on all three literacy and evidence of change in parental empowerment. Parents in all three groups, on average, held beliefs about early literacy that were positive and that were compatible with current approaches to language development and emergent literacy. No significant change in early literacy beliefs and attitudes for pre to post intervention was found. Similarly, there was no significant difference between groups on empowerment scores, but there was a significant change post intervention in one group's empowerment score. There was a drop in the empowerment score for the Prescriptive Interventionist type group, suggesting a drop in empowerment level. The qualitative aspect of this study involved six in-depth interviews completed with a sub-set of the sixty research participants. Four similar themes emerged across the groups: learning takes place across time and place; participation is key; success is achieved by taking small steps; and learning occurs in multiple ways. The research findings have important implications for practitioners and policy makers who target at risk populations with early intervention programming and wish to sustain parental empowerment. Study results show the value parents place on early learning and point to the importance of including parents in the development and delivery of early intervention programs. groups, were analyzed for evidence of change in parental attitudes and beliefs about early literacy and evidence of change in parental empowerment. Parents in all three groups, on average, held beliefs about early literacy that were positive and that were compatible with current approaches to language development and emergent literacy. No significant change in early literacy beliefs and attitudes for pre to post intervention was found. Similarly, there was no significant difference between groups on empowerment scores, but there was a significant change post intervention in one group's empowerment score. There was a drop in the empowerment score for the Prescriptive Interventionist type group, suggesting a drop in empowerment level. The qualitative aspect of this study involved six in-depth interviews completed with a sub-set of the sixty research participants. Four similar themes emerged across the groups: learning takes place across time and place; participation is key; success is achieved by taking small steps; and learning occurs in multiple ways. The research findings have important implications for practitioners and policy makers who target at risk populations with early intervention programming and wish to sustain parental empowerment. Study results show the value parents place on early learning and point to the importance of including parents in the development and delivery of early intervention programs.

Sequential Machine learning Approaches for Portfolio Management

Cette thèse envisage un ensemble de méthodes permettant aux algorithmes d'apprentissage statistique de mieux traiter la nature séquentielle des problèmes de gestion de portefeuilles financiers. Nous débutons par une considération du problème général de la composition d'algorithmes d'apprentissage devant gérer des tâches séquentielles, en particulier celui de la mise-à-jour efficace des ensembles d'apprentissage dans un cadre de validation séquentielle. Nous énumérons les desiderata que des primitives de composition doivent satisfaire, et faisons ressortir la difficulté de les atteindre de façon rigoureuse et efficace. Nous poursuivons en présentant un ensemble d'algorithmes qui atteignent ces objectifs et présentons une étude de cas d'un système complexe de prise de décision financière utilisant ces techniques. Nous décrivons ensuite une méthode générale permettant de transformer un problème de décision séquentielle non-Markovien en un problème d'apprentissage supervisé en employant un algorithme de recherche basé sur les K meilleurs chemins. Nous traitons d'une application en gestion de portefeuille où nous entraînons un algorithme d'apprentissage à optimiser directement un ratio de Sharpe (ou autre critère non-additif incorporant une aversion au risque). Nous illustrons l'approche par une étude expérimentale approfondie, proposant une architecture de réseaux de neurones spécialisée à la gestion de portefeuille et la comparant à plusieurs alternatives. Finalement, nous introduisons une représentation fonctionnelle de séries chronologiques permettant à des prévisions d'être effectuées sur un horizon variable, tout en utilisant un ensemble informationnel révélé de manière progressive. L'approche est basée sur l'utilisation des processus Gaussiens, lesquels fournissent une matrice de covariance complète entre tous les points pour lesquels une prévision est demandée. Cette information est utilisée à bon escient par un algorithme qui transige activement des écarts de cours (price spreads) entre des contrats à terme sur commodités. L'approche proposée produit, hors échantillon, un rendement ajusté pour le risque significatif, après frais de transactions, sur un portefeuille de 30 actifs.

A case study approach to classroom experiential learning of problem structuring methods

The proposed research aims at consolidating two years of practical experience in developing a classroom experiential learning pedagogic approach for the problem structuring methods (PSMs) of operational research. The results will be prepared as papers to be submitted, respectively, to the Brazilian ISSS-sponsored system theory conference in São Paulo, and to JORS. These two papers follow the submission (in 2004) of one related paper to JORS which is about to be resubmitted following certain revisions. This first paper draws from the PSM and experiential learning literatures in order to introduce a basic foundation upon which a pedagogic framework for experiential learning of PSMs may be built. It forms, in other words, an integral part of my research in this area. By September, the area of pedagogic approaches to PSM learning will have received its first official attention - at the UK OR Society conference. My research and paper production during July-December, therefore, coincide with an important time in this area, enabling me to form part of the small cohort of published researchers creating the foundations upon which future pedagogic research will build. On the institutional level, such pioneering work also raises the national and international profile of FGVEAESP, making it a reference for future researchers in this area.

Contributions to Bayesian network learning with applications to neuroscience

Neuronal morphology is a key feature in the study of brain circuits, as it is highly related to information processing and functional identification. Neuronal morphology affects the process of integration of inputs from other neurons and determines the neurons which receive the output of the neurons. Different parts of the neurons can operate semi-independently according to the spatial location of the synaptic connections. As a result, there is considerable interest in the analysis of the microanatomy of nervous cells since it constitutes an excellent tool for better understanding cortical function. However, the morphologies, molecular features and electrophysiological properties of neuronal cells are extremely variable. Except for some special cases, this variability makes it hard to find a set of features that unambiguously define a neuronal type. In addition, there are distinct types of neurons in particular regions of the brain. This morphological variability makes the analysis and modeling of neuronal morphology a challenge. Uncertainty is a key feature in many complex real-world problems. Probability theory provides a framework for modeling and reasoning with uncertainty. Probabilistic graphical models combine statistical theory and graph theory to provide a tool for managing domains with uncertainty. In particular, we focus on Bayesian networks, the most commonly used probabilistic graphical model. In this dissertation, we design new methods for learning Bayesian networks and apply them to the problem of modeling and analyzing morphological data from neurons. The morphology of a neuron can be quantified using a number of measurements, e.g., the length of the dendrites and the axon, the number of bifurcations, the direction of the dendrites and the axon, etc. These measurements can be modeled as discrete or continuous data. The continuous data can be linear (e.g., the length or the width of a dendrite) or directional (e.g., the direction of the axon). These data may follow complex probability distributions and may not fit any known parametric distribution. Modeling this kind of problems using hybrid Bayesian networks with discrete, linear and directional variables poses a number of challenges regarding learning from data, inference, etc. In this dissertation, we propose a method for modeling and simulating basal dendritic trees from pyramidal neurons using Bayesian networks to capture the interactions between the variables in the problem domain. A complete set of variables is measured from the dendrites, and a learning algorithm is applied to find the structure and estimate the parameters of the probability distributions included in the Bayesian networks. Then, a simulation algorithm is used to build the virtual dendrites by sampling values from the Bayesian networks, and a thorough evaluation is performed to show the model’s ability to generate realistic dendrites. In this first approach, the variables are discretized so that discrete Bayesian networks can be learned and simulated. Then, we address the problem of learning hybrid Bayesian networks with different kinds of variables. Mixtures of polynomials have been proposed as a way of representing probability densities in hybrid Bayesian networks. We present a method for learning mixtures of polynomials approximations of one-dimensional, multidimensional and conditional probability densities from data. The method is based on basis spline interpolation, where a density is approximated as a linear combination of basis splines. The proposed algorithms are evaluated using artificial datasets. We also use the proposed methods as a non-parametric density estimation technique in Bayesian network classifiers. Next, we address the problem of including directional data in Bayesian networks. These data have some special properties that rule out the use of classical statistics. Therefore, different distributions and statistics, such as the univariate von Mises and the multivariate von Mises–Fisher distributions, should be used to deal with this kind of information. In particular, we extend the naive Bayes classifier to the case where the conditional probability distributions of the predictive variables given the class follow either of these distributions. We consider the simple scenario, where only directional predictive variables are used, and the hybrid case, where discrete, Gaussian and directional distributions are mixed. The classifier decision functions and their decision surfaces are studied at length. Artificial examples are used to illustrate the behavior of the classifiers. The proposed classifiers are empirically evaluated over real datasets. We also study the problem of interneuron classification. An extensive group of experts is asked to classify a set of neurons according to their most prominent anatomical features. A web application is developed to retrieve the experts’ classifications. We compute agreement measures to analyze the consensus between the experts when classifying the neurons. Using Bayesian networks and clustering algorithms on the resulting data, we investigate the suitability of the anatomical terms and neuron types commonly used in the literature. Additionally, we apply supervised learning approaches to automatically classify interneurons using the values of their morphological measurements. Then, a methodology for building a model which captures the opinions of all the experts is presented. First, one Bayesian network is learned for each expert, and we propose an algorithm for clustering Bayesian networks corresponding to experts with similar behaviors. Then, a Bayesian network which represents the opinions of each group of experts is induced. Finally, a consensus Bayesian multinet which models the opinions of the whole group of experts is built. A thorough analysis of the consensus model identifies different behaviors between the experts when classifying the interneurons in the experiment. A set of characterizing morphological traits for the neuronal types can be defined by performing inference in the Bayesian multinet. These findings are used to validate the model and to gain some insights into neuron morphology. Finally, we study a classification problem where the true class label of the training instances is not known. Instead, a set of class labels is available for each instance. This is inspired by the neuron classification problem, where a group of experts is asked to individually provide a class label for each instance. We propose a novel approach for learning Bayesian networks using count vectors which represent the number of experts who selected each class label for each instance. These Bayesian networks are evaluated using artificial datasets from supervised learning problems. Resumen La morfología neuronal es una característica clave en el estudio de los circuitos cerebrales, ya que está altamente relacionada con el procesado de información y con los roles funcionales. La morfología neuronal afecta al proceso de integración de las señales de entrada y determina las neuronas que reciben las salidas de otras neuronas. Las diferentes partes de la neurona pueden operar de forma semi-independiente de acuerdo a la localización espacial de las conexiones sinápticas. Por tanto, existe un interés considerable en el análisis de la microanatomía de las células nerviosas, ya que constituye una excelente herramienta para comprender mejor el funcionamiento de la corteza cerebral. Sin embargo, las propiedades morfológicas, moleculares y electrofisiológicas de las células neuronales son extremadamente variables. Excepto en algunos casos especiales, esta variabilidad morfológica dificulta la definición de un conjunto de características que distingan claramente un tipo neuronal. Además, existen diferentes tipos de neuronas en regiones particulares del cerebro. La variabilidad neuronal hace que el análisis y el modelado de la morfología neuronal sean un importante reto científico. La incertidumbre es una propiedad clave en muchos problemas reales. La teoría de la probabilidad proporciona un marco para modelar y razonar bajo incertidumbre. Los modelos gráficos probabilísticos combinan la teoría estadística y la teoría de grafos con el objetivo de proporcionar una herramienta con la que trabajar bajo incertidumbre. En particular, nos centraremos en las redes bayesianas, el modelo más utilizado dentro de los modelos gráficos probabilísticos. En esta tesis hemos diseñado nuevos métodos para aprender redes bayesianas, inspirados por y aplicados al problema del modelado y análisis de datos morfológicos de neuronas. La morfología de una neurona puede ser cuantificada usando una serie de medidas, por ejemplo, la longitud de las dendritas y el axón, el número de bifurcaciones, la dirección de las dendritas y el axón, etc. Estas medidas pueden ser modeladas como datos continuos o discretos. A su vez, los datos continuos pueden ser lineales (por ejemplo, la longitud o la anchura de una dendrita) o direccionales (por ejemplo, la dirección del axón). Estos datos pueden llegar a seguir distribuciones de probabilidad muy complejas y pueden no ajustarse a ninguna distribución paramétrica conocida. El modelado de este tipo de problemas con redes bayesianas híbridas incluyendo variables discretas, lineales y direccionales presenta una serie de retos en relación al aprendizaje a partir de datos, la inferencia, etc. En esta tesis se propone un método para modelar y simular árboles dendríticos basales de neuronas piramidales usando redes bayesianas para capturar las interacciones entre las variables del problema. Para ello, se mide un amplio conjunto de variables de las dendritas y se aplica un algoritmo de aprendizaje con el que se aprende la estructura y se estiman los parámetros de las distribuciones de probabilidad que constituyen las redes bayesianas. Después, se usa un algoritmo de simulación para construir dendritas virtuales mediante el muestreo de valores de las redes bayesianas. Finalmente, se lleva a cabo una profunda evaluaci ón para verificar la capacidad del modelo a la hora de generar dendritas realistas. En esta primera aproximación, las variables fueron discretizadas para poder aprender y muestrear las redes bayesianas. A continuación, se aborda el problema del aprendizaje de redes bayesianas con diferentes tipos de variables. Las mixturas de polinomios constituyen un método para representar densidades de probabilidad en redes bayesianas híbridas. Presentamos un método para aprender aproximaciones de densidades unidimensionales, multidimensionales y condicionales a partir de datos utilizando mixturas de polinomios. El método se basa en interpolación con splines, que aproxima una densidad como una combinación lineal de splines. Los algoritmos propuestos se evalúan utilizando bases de datos artificiales. Además, las mixturas de polinomios son utilizadas como un método no paramétrico de estimación de densidades para clasificadores basados en redes bayesianas. Después, se estudia el problema de incluir información direccional en redes bayesianas. Este tipo de datos presenta una serie de características especiales que impiden el uso de las técnicas estadísticas clásicas. Por ello, para manejar este tipo de información se deben usar estadísticos y distribuciones de probabilidad específicos, como la distribución univariante von Mises y la distribución multivariante von Mises–Fisher. En concreto, en esta tesis extendemos el clasificador naive Bayes al caso en el que las distribuciones de probabilidad condicionada de las variables predictoras dada la clase siguen alguna de estas distribuciones. Se estudia el caso base, en el que sólo se utilizan variables direccionales, y el caso híbrido, en el que variables discretas, lineales y direccionales aparecen mezcladas. También se estudian los clasificadores desde un punto de vista teórico, derivando sus funciones de decisión y las superficies de decisión asociadas. El comportamiento de los clasificadores se ilustra utilizando bases de datos artificiales. Además, los clasificadores son evaluados empíricamente utilizando bases de datos reales. También se estudia el problema de la clasificación de interneuronas. Desarrollamos una aplicación web que permite a un grupo de expertos clasificar un conjunto de neuronas de acuerdo a sus características morfológicas más destacadas. Se utilizan medidas de concordancia para analizar el consenso entre los expertos a la hora de clasificar las neuronas. Se investiga la idoneidad de los términos anatómicos y de los tipos neuronales utilizados frecuentemente en la literatura a través del análisis de redes bayesianas y la aplicación de algoritmos de clustering. Además, se aplican técnicas de aprendizaje supervisado con el objetivo de clasificar de forma automática las interneuronas a partir de sus valores morfológicos. A continuación, se presenta una metodología para construir un modelo que captura las opiniones de todos los expertos. Primero, se genera una red bayesiana para cada experto y se propone un algoritmo para agrupar las redes bayesianas que se corresponden con expertos con comportamientos similares. Después, se induce una red bayesiana que modela la opinión de cada grupo de expertos. Por último, se construye una multired bayesiana que modela las opiniones del conjunto completo de expertos. El análisis del modelo consensuado permite identificar diferentes comportamientos entre los expertos a la hora de clasificar las neuronas. Además, permite extraer un conjunto de características morfológicas relevantes para cada uno de los tipos neuronales mediante inferencia con la multired bayesiana. Estos descubrimientos se utilizan para validar el modelo y constituyen información relevante acerca de la morfología neuronal. Por último, se estudia un problema de clasificación en el que la etiqueta de clase de los datos de entrenamiento es incierta. En cambio, disponemos de un conjunto de etiquetas para cada instancia. Este problema está inspirado en el problema de la clasificación de neuronas, en el que un grupo de expertos proporciona una etiqueta de clase para cada instancia de manera individual. Se propone un método para aprender redes bayesianas utilizando vectores de cuentas, que representan el número de expertos que seleccionan cada etiqueta de clase para cada instancia. Estas redes bayesianas se evalúan utilizando bases de datos artificiales de problemas de aprendizaje supervisado.

Climate and causation in the Swedish Iron Age : Learning from the present to understand the past

The paper reassesses the role of climate as a factor shaping changes in settlement and landscape in the Swedish Iron Age (500 BC to AD 1050). Two reasons motivate this re-evaluation. First, high-resolution data based on climate proxies from the natural sciences are now increasingly available. Second, the climate-related social sciences have yielded conceptual and theoretical developments regarding vulnerability and adaptability in the present and recent past, creating new ways to analyse the effects of climatic versus societal factors on societies in the more distant past. Recent research in this field is evaluated and the explicitly climate deterministic standpoint of many recent natural science texts is criticized. Learning from recent approaches to climate change in the social sciences is crucial for understanding society–climate relationships in the past. The paper concludes that we are not yet in a position to fully evaluate the role of the new evidence of abrupt climate change in 850 BC, at the beginning of the Iron Age. Regarding the crisis in the mid first millennium AD, however, new climate data indicate that a dust veil in AD 536–537 might have aggravated the economic and societal crisis known from previous research.

Accountability, organisational learning and risks to patient safety in England: conflict or compromise?

This article examines the current risk regulation regime, within the English National Health Service (NHS), by investigating the two, sometimes conflicting, approaches to risk embodied within the field of policies towards patient safety. The first approach focuses on promoting accountability and is built on legal principles surrounding negligence and competence. The second approach focuses on promoting learning from previous mistakes and near-misses, and is built on the development of a ‘safety culture’. Previous work has drawn attention to problems associated with risk-based regulation when faced with the dual imperatives of accountability and organisational learning. The article develops this by considering whether the NHS patient safety regime demonstrates the coexistence of two different risk regulation regimes, or merely one regime with contradictory elements. It uses the heuristic device of ‘institutional logics’ to examine the coexistence of and interrelationship between ‘organisational learning’ and ‘accountability’ logics driving risk regulation in health care.

Learning to research - researching to learn

Learning to Research Researching to Learn explores the integration of research into teaching and learning at all levels of higher education. The chapters draw on the long and ongoing debate about the teaching research nexus in universities. Although the vast majority of academics believe that there is an important and valuable link between teaching and research, the precise nature of this relationship continues to be contested. The book includes chapters that showcase innovative ways of learning to research; how research is integrated into coursework teaching; how students learn the processes of research, and how universities are preparing students to engage with the world. The chapters also showcase innovative ways of researching to learn, exploring how students learn through doing research, how they conceptualise the knowledge of their fields of study through the processes of doing research, and how students experiment and reflect on the results produced. These are the key issues addressed by this anthology, as it brings together analyses of the ways in which university teachers are developing research skills in their students, creating enquiry-based approaches to teaching, and engaging in education research themselves. The studies here explore the links between teaching, learning and research in a range of contexts, from pre-enrolment through to academic staff development, in Australia, the UK, the US, Singapore and Denmark. Through a rich array of theoretical and methodological approaches, the collection seeks to further our understanding of how universities can play an effective role in educating graduates suited to the twenty-first century

Convergence and translation: attitudes to inter-professional learning and teaching of creative problem-solving among medical and engineering students and staff

Background: Healthcare worldwide needs translation of basic ideas from engineering into the clinic. Consequently, there is increasing demand for graduates equipped with the knowledge and skills to apply interdisciplinary medicine/engineering approaches to the development of novel solutions for healthcare. The literature provides little guidance regarding barriers to, and facilitators of, effective interdisciplinary learning for engineering and medical students in a team-based project context. Methods: A quantitative survey was distributed to engineering and medical students and staff in two universities, one in Ireland and one in Belgium, to chart knowledge and practice in interdisciplinary learning and teaching, and of the teaching of innovation. Results: We report important differences for staff and students between the disciplines regarding attitudes towards, and perceptions of, the relevance of interdisciplinary learning opportunities, and the role of creativity and innovation. There was agreement across groups concerning preferred learning, instructional styles, and module content. Medical students showed greater resistance to the use of structured creativity tools and interdisciplinary teams. Conclusions: The results of this international survey will help to define the optimal learning conditions under which undergraduate engineering and medicine students can learn to consider the diverse factors which determine the success or failure of a healthcare engineering solution.

From Kandinsky to Java (The Use of 20th Century Abstract Art in Learning Programming)

At the University of Worcester we are continually striving to find new approaches to the learning and teaching of programming, to improve the quality of learning and the student experience. Over the past three years we have used the contexts of robotics, computer games, and most recently a study of Abstract Art to this end. This paper discusses our motivation for using Abstract Art as a context, details our principles and methodology, and reports on an evaluation of the student experience. Our basic tenet is that one can view the works of artists such as Kandinsky, Klee and Malevich as Object-Oriented (OO) constructions. Discussion of these works can therefore be used to introduce OO principles, to explore the meaning of classes, methods and attributes and finally to synthesize new works of art through Java code. This research has been conducted during delivery of an “Advanced OOP (Java)” programming module at final-year Undergraduate level, and during a Masters’ OO-Programming (Java) module. This allows a comparative evaluation of novice and experienced programmers’ learning. In this paper, we identify several instructional factors which emerge from our approach, and reflect upon the associated pedagogy. A Catalogue of ArtApplets is provided at the associated web-site.

An Assessment of Professional Development for Astronomy and Physics Faculty: Expanding Our Vision of How to Support Faculty's Learning About Teaching

In this thesis, we will explore approaches to faculty instructional change in astronomy and physics. We primarily focus on professional development (PD) workshops, which are a central mechanism used within our community to help faculty improve their teaching. Although workshops serve a critical role for promoting more equitable instruction, we rarely assess them through careful consideration of how they engage faculty. To encourage a shift towards more reflective, research-informed PD, we developed the Real-Time Professional Development Observation Tool (R-PDOT), to document the form and focus of faculty's engagement during workshops. We then analyze video-recordings of faculty's interactions during the Physics and Astronomy New Faculty Workshop, focusing on instances where faculty might engage in pedagogical sense-making. Finally, we consider insights gained from our own local, team-based effort to improve a course sequence for astronomy majors. We conclude with recommendations for PD leaders and researchers.

A randomized trial of a problem-based learning approach for teaching epidemiology

Purpose. To conduct a controlled trial of traditional and problem-based learning (PBL) methods of teaching epidemiology. Method. All second-year medical students (n = 136) at The University of Western Australia Medical School were offered the chance to participate in a randomized controlled trial of teaching methods fur an epidemiology course. Students who consented to participate (n = 80) were randomly assigned to either a PBL or a traditional course. Students who did not consent or did not return the consent form (n = 56) were assigned to the traditional course, Students in both streams took identical quizzes and exams. These scores, a collection of semi-quantitative feedback from all students, and a qualitative analysis of interviews with a convenience sample of six students from each stream were compared. Results. There was no significant difference in performances on quizzes or exams between PBL and traditional students. Students using PBL reported a stronger grasp of epidemiologic principles, enjoyed working with a group, and, at the end of the course, were more enthusiastic about epidemiology and its professional relevance to them than were students in the traditional course. PBL students worked more steadily during the semester but spent only marginally more time on the epidemiology course overall. Interviews corroborated these findings. Non-consenting students were older (p < 0.02) and more likely to come from non-English-speaking backgrounds (p < 0.005). Conclusions. PBL provides an academically equivalent but personally far richer learning experience. The adoption of PBL approaches to medical education makes it important to study whether PBL presents particular challenges for students whose first language is not the language of instruction.

Communicating vegetation management science to land managers and other stakeholders

Science communication. including extension services. plays a key role in achieving sustainable native vegetation management. One of the pivotal aspects of the debate on sustainable vegetation management is the scientific information underpinning policy-making. In recent years. extension services have Shifted their focus from top-down technology transfer to bottom-up participation and empowerment. I here has also been a broadening of communication strategies to recognise the range of stakeholders involved in native vegetation management and to encompass environmental concerns. This paper examines the differences between government approaches to extension services to deliver policy and the need for effective communication to address broader science issues that underpin native vegetation management. The importance of knowing the learning styles of the stakeholders involved in native vegetation management is discussed at a time of increasing reliance on mass communication for information exchange and the importance of personal communication to achieve on-ground sustainable management. Critical factors for effective science-management communication are identified Such as: (i) undertaking scientific studies (research) with community involvement, acceptance and agreed understanding of project objectives (ii) realistic community consultation periods: (iii) matching communication channels with stakeholder needs; (iv) combining scientific with local knowledge in in holistic (biophysical and social) approach to understanding in issued and (v) regional partnerships. These communication factors are considered to be essential to implementing on-ground natural resource management strategics and actions, including those concerned with native vegetation management.

Extending Learning Opportunities through a virtual faculty

This paper presents the notion of a virtual faculty as a viable alternative to extending and maintaining learner opportunities for students in regional universities or at universities where specialisations in which they are interested may not be offered. Staff from a number of Australian Universities participated in a CUTSD project to explore the viability of establishing a virtual faculty using videoconferencing as the medium of delivery. The success of this project was the result of close collaboration at a number of levels within the participating institutions and a willingness to explore effective approaches to teaching and learning for a videoconference environment.

Immunity to asexual blood stage malaria and vaccine approaches

The development of a malaria vaccine seems to be a definite possibility despite the fact that even individuals with a life time of endemic exposure do not develop sterile immunity. An effective malaria vaccine would be invaluable in preventing malaria-associated deaths in endemic areas, especially amongst children less than 5 years of age and pregnant women. This review discusses our current understanding of immunity against the asexual blood stage of malaria - the stage that is responsible for the symptoms of the disease - and approaches to the design of an asexual blood stage vaccine.