Collaborative e-learning and Learning Objects

The Learning Object (OA) is any digital resource that can be reused to support learning with specific functions and objectives. The OA specifications are commonly offered in SCORM model without considering activities in groups. This deficiency was overcome by the solution presented in this paper. This work specified OA for e-learning activities in groups based on SCORM model. This solution allows the creation of dynamic objects which include content and software resources for the collaborative learning processes. That results in a generalization of the OA definition, and in a contribution with e-learning specifications.

Improving Alzheimer`s Disease Diagnosis with Machine Learning Techniques

There is not a specific test to diagnose Alzheimer`s disease (AD). Its diagnosis should be based upon clinical history, neuropsychological and laboratory tests, neuroimaging and electroencephalography (EEG). Therefore, new approaches are necessary to enable earlier and more accurate diagnosis and to follow treatment results. In this study we used a Machine Learning (ML) technique, named Support Vector Machine (SVM), to search patterns in EEG epochs to differentiate AD patients from controls. As a result, we developed a quantitative EEG (qEEG) processing method for automatic differentiation of patients with AD from normal individuals, as a complement to the diagnosis of probable dementia. We studied EEGs from 19 normal subjects (14 females/5 males, mean age 71.6 years) and 16 probable mild to moderate symptoms AD patients (14 females/2 males, mean age 73.4 years. The results obtained from analysis of EEG epochs were accuracy 79.9% and sensitivity 83.2%. The analysis considering the diagnosis of each individual patient reached 87.0% accuracy and 91.7% sensitivity.

The impact of functional connectivity changes on support vector machines mapping of fMRI data

Functional magnetic resonance imaging (fMRI) is currently one of the most widely used methods for studying human brain function in vivo. Although many different approaches to fMRI analysis are available, the most widely used methods employ so called ""mass-univariate"" modeling of responses in a voxel-by-voxel fashion to construct activation maps. However, it is well known that many brain processes involve networks of interacting regions and for this reason multivariate analyses might seem to be attractive alternatives to univariate approaches. The current paper focuses on one multivariate application of statistical learning theory: the statistical discrimination maps (SDM) based on support vector machine, and seeks to establish some possible interpretations when the results differ from univariate `approaches. In fact, when there are changes not only on the activation level of two conditions but also on functional connectivity, SDM seems more informative. We addressed this question using both simulations and applications to real data. We have shown that the combined use of univariate approaches and SDM yields significant new insights into brain activations not available using univariate methods alone. In the application to a visual working memory fMRI data, we demonstrated that the interaction among brain regions play a role in SDM`s power to detect discriminative voxels. (C) 2008 Elsevier B.V. All rights reserved.

A decision support system to facilitate management of patients with acute gastrointestinal bleeding

Objective: To develop a model to predict the bleeding source and identify the cohort amongst patients with acute gastrointestinal bleeding (GIB) who require urgent intervention, including endoscopy. Patients with acute GIB, an unpredictable event, are most commonly evaluated and managed by non-gastroenterologists. Rapid and consistently reliable risk stratification of patients with acute GIB for urgent endoscopy may potentially improve outcomes amongst such patients by targeting scarce health-care resources to those who need it the most. Design and methods: Using ICD-9 codes for acute GIB, 189 patients with acute GIB and all. available data variables required to develop and test models were identified from a hospital medical records database. Data on 122 patients was utilized for development of the model and on 67 patients utilized to perform comparative analysis of the models. Clinical data such as presenting signs and symptoms, demographic data, presence of co-morbidities, laboratory data and corresponding endoscopic diagnosis and outcomes were collected. Clinical data and endoscopic diagnosis collected for each patient was utilized to retrospectively ascertain optimal management for each patient. Clinical presentations and corresponding treatment was utilized as training examples. Eight mathematical models including artificial neural network (ANN), support vector machine (SVM), k-nearest neighbor, linear discriminant analysis (LDA), shrunken centroid (SC), random forest (RF), logistic regression, and boosting were trained and tested. The performance of these models was compared using standard statistical analysis and ROC curves. Results: Overall the random forest model best predicted the source, need for resuscitation, and disposition with accuracies of approximately 80% or higher (accuracy for endoscopy was greater than 75%). The area under ROC curve for RF was greater than 0.85, indicating excellent performance by the random forest model Conclusion: While most mathematical models are effective as a decision support system for evaluation and management of patients with acute GIB, in our testing, the RF model consistently demonstrated the best performance. Amongst patients presenting with acute GIB, mathematical models may facilitate the identification of the source of GIB, need for intervention and allow optimization of care and healthcare resource allocation; these however require further validation. (c) 2007 Elsevier B.V. All rights reserved.

Comparing machine learning classifiers in potential distribution modelling

Species` potential distribution modelling consists of building a representation of the fundamental ecological requirements of a species from biotic and abiotic conditions where the species is known to occur. Such models can be valuable tools to understand the biogeography of species and to support the prediction of its presence/absence considering a particular environment scenario. This paper investigates the use of different supervised machine learning techniques to model the potential distribution of 35 plant species from Latin America. Each technique was able to extract a different representation of the relations between the environmental conditions and the distribution profile of the species. The experimental results highlight the good performance of random trees classifiers, indicating this particular technique as a promising candidate for modelling species` potential distribution. (C) 2010 Elsevier Ltd. All rights reserved.

Is the unfolding of the group discussion off-pattern? Improving coordination support in educational forums using mobile devices

A forum is a valuable tool to foster reflection in an in-depth discussion; however, it forces the course mediator to continually pay close attention in order to coordinate learners` activities. Moreover, monitoring a forum is time consuming given that it is impossible to know in advance when new messages are going to be posted. Additionally, a forum may be inactive for a long period and suddenly receive a burst of messages forcing forum mediators to frequently log on in order to know how the discussion is unfolding to intervene whenever it is necessary. Mediators also need to deal with a large amount of messages to identify off-pattern situations. This work presents a piece of action research that investigates how to improve coordination support in a forum using mobile devices for mitigating mediator`s difficulties in following the status of a forum. Based on summarized information extracted from message meta-data, mediators consult visual information summaries on PDAs and receive textual notifications in their mobile phone. This investigation revealed that mediators used the mobile-based coordination support to keep informed on what is taking place within the forum without the need to log on their desktop computer. (C) 2009 Elsevier Ltd. All rights reserved.

Measuring time series predictability using support vector regression

Most studies involving statistical time series analysis rely on assumptions of linearity, which by its simplicity facilitates parameter interpretation and estimation. However, the linearity assumption may be too restrictive for many practical applications. The implementation of nonlinear models in time series analysis involves the estimation of a large set of parameters, frequently leading to overfitting problems. In this article, a predictability coefficient is estimated using a combination of nonlinear autoregressive models and the use of support vector regression in this model is explored. We illustrate the usefulness and interpretability of results by using electroencephalographic records of an epileptic patient.

A expressão da modularidade

Desde Fodor (1983) a modularidade comportamental tem gerado fortes controvérsias, especialmente em torno do quanto são especializados os módulos eventuais e do quanto são encapsulados, isto é, independentes de controles externos. No presente ensaio, depois de rever formulações precursoras a respeito de instinto e de aprendizagens especializadas e de expor as características básicas da modularidade, tomo, como estudo de caso, pesquisas sobre a percepção da expressão facial da emoção, mostrando tanto o lado especializado desse processo como a sua abertura para controles externos. Termino defendendo a necessidade de, junto à estratégia de delimitar processos comportamentais especializados, colocar a questão de como eles se integram e se controlam uns aos outros e são eventualmente gerenciados por processos gerais.

Learning processes and the neural analysis of conditioning

Classical and operant conditioning principles, such as the behavioral discrepancy-derived assumption that reinforcement always selects antecedent stimulus and response relations, have been studied at the neural level, mainly by observing the strengthening of neuronal responses or synaptic connections. A review of the literature on the neural basis of behavior provided extensive scientific data that indicate a synthesis between the two conditioning processes based mainly on stimulus control in learning tasks. The resulting analysis revealed the following aspects. Dopamine acts as a behavioral discrepancy signal in the midbrain pathway of positive reinforcement, leading toward the nucleus accumbens. Dopamine modulates both types of conditioning in the Aplysia mollusk and in mammals. In vivo and in vitro mollusk preparations show convergence of both types of conditioning in the same motor neuron. Frontal cortical neurons are involved in behavioral discrimination in reversal and extinction procedures, and these neurons preferentially deliver glutamate through conditioned stimulus or discriminative stimulus pathways. Discriminative neural responses can reliably precede operant movements and can also be common to stimuli that share complex symbolic relations. The present article discusses convergent and divergent points between conditioning paradigms at the neural level of analysis to advance our knowledge on reinforcement.

Molecular phylogeny of the Myzorhynchella Section of Anopheles (Nyssorhynchus) (Diptera: Culicidae): genetic support for recently described and resurrected species

Phylogenetic relationships among species of the Myzorhynchella Section of Anopheles (Nyssorhynchus) were investigated using the nuclear ribosomal DNA second internal transcribed spacer (ITS2), the nuclear whitegene and mitochondrial cytochrome oxidase subunit I (COI) regions. The recently described Anopheles pristinus and resurrected Anopheles guarani were also included in the study. Bayesian phylogenetic analyses found Anopheles parvus to be the most distantly related species within the Section, a finding that is consistent with morphology. An. pristinus and An. guarani were clearly resolved from Anopheles antunesi and Anopheles lutzii, respectively. An. lutzii collected in the same mountain range as the type locality were found within a strongly supported clade, whereas individuals from the southern state of Rio Grande do Sul, tentatively identified as An. lutzii based on adult female external morphology, were distinct from An. lutzii, An. antunesi and from each other, and may therefore represent two new sympatric species. A more detailed examination of An. lutzii sensu latoalong its known geographic range is recommended to resolve these anomalous relationships.

Surface dental enamel lead levels and antisocial behavior in Brazilian adolescents

Lead poisoning has been reportedly linked to a high risk of learning disabilities, aggression and criminal offenses. To study the association between lead exposure and antisocial/delinquent behavior, a cross-sectional study was conducted with 173 Brazilian youths aged 14\201318 and their parents (n = 93), living in impoverished neighborhoods of Bauru-SP, with high criminality indices. Self-Reported Delinquency (SRD) and Child Behavior Checklist (CBCL) questionnaires were used to evaluate delinquent/antisocial behavior. Body lead burdens were evaluated in surface dental enamel acid microbiopsies. The dental enamel lead levels (DELL) were quantified by graphite furnace atomic absorption spectrometry (GFAAS) and phosphorus content was measured using inductively coupled plasma optical emission spectrometry (ICP-OES). Logistic regression was used to identify associations between DELL and each scale defined by CBCL and SRD scores. Odd ratios adjusted for familial and social covariates, considering a group of youths exposed to high lead levels (\2265 75 percentile), indicated that high DELL is associated with increased risk of exceeding the clinical score for somatic complaints, social problems, rule-breaking behavior and externalizing problems (CI 95 per cent). High DELL was not found to be associated with elevated SRD scores. In conclusion, our data support the hypothesis that high-level lead exposure can trigger antisocial behavior, which calls for public policies to prevent lead poisoning

Efetividade de programa de agentes comunitários na promoção da saúde bucal

OBJETIVO:Avaliar mudanças em conhecimentos, atitudes e acesso/utilização de serviços odontológicos decorrentes de um programa de promoção da saúde bucal com agentes comunitários de saúde. MÉTODOS:Um projeto de capacitação combinando ensino-aprendizagem, apoio e supervisão, foi desenvolvido entre os meses de julho de 2003 a agosto de 2004. As mudanças foram avaliadas por meio de entrevistas estruturadas em que participaram 36 agentes comunitários de saúde e uma amostra de 91 mulheres e mães, representativa de donas de casa com 25 a 39 anos de idade, alfabetizadas e residentes em domicílios de três a seis cômodos no município de Rio Grande da Serra (SP). Foram colhidos dados sobre conhecimentos de saúde-doença bucal, práticas e capacidades auto-referidas em relação ao auto-exame, higiene bucal, número de residentes e de escovas dentais individuais e coletivas em cada domicílio e acesso e uso de serviços odontológicos. Por meio do teste t de Student pareado, foram comparadas as médias dos valores obtidos antes e depois do programa para cada um dos grupos estudados. As respostas foram analisadas adotando-se um nível de significância de 5%. RESULTADOS: Foram observadas diferenças estatisticamente significativas para questões relativas ao conhecimento de saúde bucal entre os agentes e entre as mulheres antes e depois da capacitação (p<0,05). Desequilíbrio entre o número de escovas e de indivíduos em cada família diminuiu. A freqüência da escovação e do uso do fio dental se elevou depois da atuação dos agentes. Os valores de auto-avaliação da higiene bucal aumentaram. Modificação nas práticas e capacidades auto-referidas mostrou significativa elevação da auto-confiança. O acesso ao serviço foi mais fácil (p<0,000) e seu uso mais regular (p<0,000) entre mulheres. CONCLUSÕES: Houve mudanças positivas na percepção em relação a aspectos de saúde bucal, na auto-confiança e no acesso e uso de serviços odontológicos. Tais mudanças po

The Process of Simulation Authoring/Creation by Chemistry Teachers: A Service Learning Experience

Two case studies are presented to describe the process of public school teachers authoring and creating chemistry simulations. They are part of the Virtual Didactic Laboratory for Chemistry, a project developed by the School of the Future of the University of Sao Paulo. the documental analysis of the material produced by two groups of teachers reflects different selection process for both themes and problem-situations when creating simulations. The study demonstrates the potential for chemistry learning with an approach that takes students' everyday lives into account and is based on collaborative work among teachers and researches. Also, from the teachers' perspectives, the possibilities of interaction that a simulation offers for classroom activities are considered.

ALFABETIZAÇÃO DIGITAL: proposição de parâmetros metodológicos em competência informacional

Much has been discussed about Digital Literacy, but it is quite obscure the identification of the skills required to develop such process. This study was done towards an integration of the Digital Literacy process to the specific informational skills a person may dominate, search, retrieve and use information efficiently, in its professional, academic or personal life. The main objective of this work is to propose methodological parameters for training in informational skills. Otherwise, the specific objectives are associated to the supposition and identification of the desired skills of the Digital Literacy program participants. The methodological procedures applied to the research are of exploratory character, and to do so two tools are used: the literature research and case studies. Besides having the methodology in structured information competence, the study points out to the fact that the country is too far from what is desired concerning development and employment of Digital Literacy programs consistent enough to support the teaching and learning of searching, recovering and using of information by the participants. Therefore, it is essential to create programs that provide not only machinery, but motivate individuals to develop informational skills to help in the learning process.

Students' collective knowledge construction in the virtual learning environment ""ToLigado - your school interactive newspaper""

Introduction. The ToLigado Project - Your School Interactive Newspaper is an interactive virtual learning environment conceived, developed, implemented and supported by researchers at the School of the Future Research Laboratory of the University of Sao Paulo, Brazil. Method. This virtual learning environment aims to motivate trans-disciplinary research among public school students and teachers in 2,931 schools equipped with Internet-access computer rooms. Within this virtual community, students produce collective multimedia research documents that are immediately published in the portal. The project also aims to increase students' autonomy for research, collaborative work and Web authorship. Main sections of the portal are presented and described. Results. Partial results of the first two years' implementation are presented and indicate a strong motivation among students to produce knowledge despite the fragile hardware and software infrastructure at the time. Discussion. In this new environment, students should be seen as 'knowledge architects' and teachers as facilitators, or 'curiosity managers'. The ToLigado portal may constitute a repository for future studies regarding student attitudes in virtual learning environments, students' behaviour as 'authors', Web authorship involving collective knowledge production, teachers' behaviour as facilitators, and virtual learning environments as digital repositories of students' knowledge construction and social capital in virtual learning communities.