O sistema PBL, problem-based learning, no ensino de medicina no Brasil : análise bibliográfica sobre a sua execução

Introdução: Entre as estratégias de ensino e aprendizagem utilizadas nas práticas pedagógicas, a Problem Based Learning (PBL) (Aprendizagem Baseada em Problemas) é utilizada desde 1960, em especial nos cursos de Medicina. Mesmo sendo uma estratégia valiosa, um dos seus obstáculos é a pouca prática dos alunos em atividades autodirigidas, pesquisa e construção coletiva do conhecimento. Objetivo: Rastrear elementos constitutivos da PBL através de dados colhidos em artigos pesquisados em sítios de divulgação científica; Avaliar, nos estudos selecionados, os aspectos positivos e negativos que estejam relacionados com a metodologia do Sistema PBL aplicada ao ensino médico no Brasil. Metodologia: Estudo bibliográfico de 13 textos utilizando um modelo de desconstrução, denominada Análise Textual Discursiva (ATD) que consiste em: transformação dos artigos em pedaços menores; análise textual; identificação de padrões convergentes e divergentes em relação a PBL; organização e síntese dos dados, culminando com a elaboração de estratégia adaptativa da PBL para o curso de Medicina. Resultados: Foram encontradas 116 citações que convergiam para referências positivos acerca da metodologia PBL e 40 citações que divergiam acerca dos pontos positivos. Os aspectos positivos como o desenvolvimento de atitudes e habilidades; desenvolvimento de competências anteriores ao curso; efeitos positivos depois de terminada a graduação, como autonomia de estudo e a articulação entre currículo e realidade profissional, representam pontos a serem reforçados na aula. Em contraponto, foi observado que dentre os negativos a não compreensão do papel do professor como tutor; necessidade de conteúdo formal tradicional pelos alunos e a expectativa que o professor retire as suas dúvidas são pontos a serem evitados. Conclusões: A metodologia PBL deverá servir como metodologia ativa para aproveitar ao máximo as habilidades que os alunos já apresentam, potencializando o aprendizado na educação médica em sala de aula. Palavras-Chave: PBL; curso de medicina; metodologia ativa; educação médica.

HTML5, Javascript and modern browser technologies: toward a web based framework for device fingerprinting

Mentre navighiamo siamo veramente certi che i nostri dati e la nostra privacy siano al sicuro? I browser e le tecnologie di cui fanno uso possono rivelare una miriade di informazioni. Al crescere delle informazioni reperibili, si inizia a superare una massa critica che può permettere l'identificazione. Il device fingerprinting è proprio il rilevamento di questa tipologia di dati. HTML5 e le nuove API che esso mette a disposizione aumentano a dismisura le modalità per fare fingerprinting. Durante lo sviluppo della presente tesi è stato realizzato un framework molto potente che verrà mostrato nel dettaglio. Come a seguito di un disastro aereo, l'ingegneria aeronautica si mette all'opera per scovare i punti deboli allo scopo di rendere più robusti gli aerei di nuova generazione, noi con la presente tesi vogliamo dare il nostro contributo al miglioramento del web del futuro. Affinchè la nostra privacy sia veramente nelle nostre mani e possiamo essere artefici del nostro domani.

Virtual learning communities beyond the classroom: perceptions of tertiary Chinese language and non-language learners

As class contact times are reduced as a result of fiscal restraints in the modern tertiary sector, language instructors are placed in the position of having to find new ways to provide experience and continuity in language learning. Extending 'learning communities'—sites of learner knowledge exchange, exposure to diverse learning styles and strategies, and mutual support—beyond the classroom is one solution to maintaining successful linguistic competencies amongst learners. This, however, can conflict with the diverse extra-curricular commitments faced by tertiary students. The flexibility of web-based learning platforms provides one means of overcoming these obstacles. This study investigates learner perceptions of the use of the WebCT platform's computer medicated communication (CMC) tools as a means of extending the community of learning in tertiary Chinese language and non-language courses. Learner responses to Likert and open-ended questionnaires show that flexibility and reduction of negative affect are seen as significant benefits to 'virtual' interaction and communication, although responses are notably stronger in the non-language compared with the language cohort. While both learner cohorts acknowledge positive learning outcomes, CMC is not seen to consistently further interpersonal rapport beyond that established in the classroom. Maintaining a balance between web-based and classroom learning emerges as a concern, especially amongst language learners. [Author abstract, ed]

Web-based screening and brief intervention for hazardous drinking: a double-blind randomized controlled trial

Background Strong evidence exists for the efficacy of screening and brief intervention for reducing hazardous drinking. However, problems have been highlighted with respect to its implementation in health-care systems, not least of which is a reluctance of some doctors to discuss alcohol proactively with their patients. Aims To determine the efficacy of a novel web-based screening and brief intervention (e-SBI) to reduce hazardous drinking. Design A double-blind randomized controlled trial. Setting A university student health service. Participants A total of 16 7 students (17-26 years) were recruited in the reception area and completed a 3-minute web-based screen including the Alcohol Use Disorder Identifiation Test (AUDIT) questionnaire. Of these, 112 tested positive, and 104 (52 females) who consented to follow-up were included in the trial. Measurements Drinking frequency, typical occasion quantity, total volume, heavy episode frequency (females > 80 g ethanol, males > 120 g ethanol), number of personal problems, an academic problems score. Intervention Participants were randomized to 10-15 minutes of web-based assessment and personalized feedback on their drinking (intervention, n = 5 1) or to a leaflet-only control group (n = 5 3). Findings Mean baseline AUDIT scores for control and intervention groups were 16.6 (SD = 6.0) and 16.6 (SD = 5.7). At 6 weeks, participants receiving e-SBI reported significantly lower total consumption (geometric mean ratio = 0.74; 9 5 % confidence interval: 0.56-0.96), lower heavy episode frequency (0.63; 0.42-0.92) and fewer personal problems (0.70; 0.54-0.91). At 6 months personal problems remained lower (0.76; 0.60-0.97), although consumption did not differ significantly. At 6 months, academic problems were lower in the intervention group relative to controls (0.72; 0.51-1.02). Conclusions e-SBI reduced hazardous drinking among university students, to an extent similar to that found for practitioner-delivered brief interventions in the general population. e-SBI offers promise as a strategy to reduce alcohol-related harm in a way that is non-intrusive, appealing to the target group, and capable of being incorporated into primary care. Research is required to replicate the findings, to determine the duration of intervention effects, and to investigate the mechanisms by which the intervention operates.

Improving learning in undergraduate control engineering courses using context-based learning models

Control Engineering is an essential part of university electrical engineering education. Normally, a control course requires considerable mathematical as well as engineering knowledge and is consequently regarded as a difficult course by many undergraduate students. From the academic point of view, how to help the students to improve their learning of the control engineering knowledge is therefore an important task which requires careful planning and innovative teaching methods. Traditionally, the didactic teaching approach has been used to teach the students the concepts needed to solve control problems. This approach is commonly adopted in many mathematics intensive courses; however it generally lacks reflection from the students to improve their learning. This paper addresses the practice of action learning and context-based learning models in teaching university control courses. This context-based approach has been practised in teaching several control engineering courses in a university with promising results, particularly in view of student learning performances.

An Incremental EM-based Learning Approach for On-Line Prediction of Hospital Resource Utilization

Objective: Inpatient length of stay (LOS) is an important measure of hospital activity, health care resource consumption, and patient acuity. This research work aims at developing an incremental expectation maximization (EM) based learning approach on mixture of experts (ME) system for on-line prediction of LOS. The use of a batchmode learning process in most existing artificial neural networks to predict LOS is unrealistic, as the data become available over time and their pattern change dynamically. In contrast, an on-line process is capable of providing an output whenever a new datum becomes available. This on-the-spot information is therefore more useful and practical for making decisions, especially when one deals with a tremendous amount of data. Methods and material: The proposed approach is illustrated using a real example of gastroenteritis LOS data. The data set was extracted from a retrospective cohort study on all infants born in 1995-1997 and their subsequent admissions for gastroenteritis. The total number of admissions in this data set was n = 692. Linked hospitalization records of the cohort were retrieved retrospectively to derive the outcome measure, patient demographics, and associated co-morbidities information. A comparative study of the incremental learning and the batch-mode learning algorithms is considered. The performances of the learning algorithms are compared based on the mean absolute difference (MAD) between the predictions and the actual LOS, and the proportion of predictions with MAD < 1 day (Prop(MAD < 1)). The significance of the comparison is assessed through a regression analysis. Results: The incremental learning algorithm provides better on-line prediction of LOS when the system has gained sufficient training from more examples (MAD = 1.77 days and Prop(MAD < 1) = 54.3%), compared to that using the batch-mode learning. The regression analysis indicates a significant decrease of MAD (p-value = 0.063) and a significant (p-value = 0.044) increase of Prop(MAD