A perspectiva sóciocognitiva no tratamento temático da informação em bibliotecas universitárias: aspectos inerentes a percepção profissional

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

A natureza da aprendizagem matemática em um ambiente online de formação continuada de professores

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Aprendizado de máquina baseado em tensores e suas aplicacções para floresta de caminhos ótimos

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Teaching in the community: Changing and implementing a new curriculum

The Faculdade de Medicina de Botucatu is changing to, and implementing a new curriculum aimed at integrating teaching and learning in the community. Emphasis is on preparing the community settings for teaching, learning and providing health care. A particular task is staff development with emphasis on problem-based learning (PBL) and training medical and nursing students in the leadership to participate in this process. The new curriculum includes the gradual introduction of clinical practice during First Year, integration of the basic sciences with clinical sciences, through integrated modules studied in small groups, and maintenance of the two year clerkship. The undergraduates are introduced gradually to the community: 8% of the total curriculum during First Year, 10% during Second Year, 10% during Third Year, 20% during Fourth Year, 30% during Fifth and Sixth Years. The basic health units at primary care level, and the regional specialty outpatients and hospitals at the second level, are the main teaching sites. An Education Development Committee was established to discuss the strategies for supporting the changes and to structure the planning for promoting the gradual transformation of staff development. After 18 months of implementation of the curriculum, there followed discussions and monitoring of the objectives of changes in medical education at our school. Successful implementation of the new curriculum would fail, if the objectives were not absorbed by every member of the implementation Committee.

Fuzzy community structure detection by particle competition and cooperation

Identification and classification of overlapping nodes in networks are important topics in data mining. In this paper, a network-based (graph-based) semi-supervised learning method is proposed. It is based on competition and cooperation among walking particles in a network to uncover overlapping nodes by generating continuous-valued outputs (soft labels), corresponding to the levels of membership from the nodes to each of the communities. Moreover, the proposed method can be applied to detect overlapping data items in a data set of general form, such as a vector-based data set, once it is transformed to a network. Usually, label propagation involves risks of error amplification. In order to avoid this problem, the proposed method offers a mechanism to identify outliers among the labeled data items, and consequently prevents error propagation from such outliers. Computer simulations carried out for synthetic and real-world data sets provide a numeric quantification of the performance of the method. © 2012 Springer-Verlag.