Análise de vizinhança: uma nova abordagem para avaliar a rede proposicional de mapas conceituais*

A análise detalhada de Mapas Conceituais (MCs) pode revelar informações latentes que não são percebidas a partir da mera leitura do seu conjunto de proposições. O presente trabalho tem como objetivo propor a Análise de Vizinhança (AViz) como uma forma inovadora de avaliar os MCs obtidos em sala de aula. A seleção de um Conceito Obrigatório (CO) permite ao professor verificar como os alunos o relacionam com outros conceitos, os quais são classificados como Conceitos Vizinhos (CVs). As proposições estabelecidas entre o CO e os CVs são suficientes para indicar o nível de compreensão do aluno sobre o tema mapeado. MCs (n = 69) sobre mudanças climáticas formam o primeiro conjunto de dados empíricos que ratifica o potencial da AViz. O CO selecionado foi dispersão, a fim de avaliar se os alunos conseguem relacionar esse fenômeno físico com o caráter global desse problema ambiental. Os padrães identificados a partir da AViz sugerem que, apesar de serem submetidos a uma mesma sequência didática, nem todos os alunos conseguiram utilizar o CO de forma adequada. Isso pode ser explicado a partir da Teoria da Aprendizagem Significativa de David Ausubel, que destaca o papel fundamental dos conhecimentos prévios no processo de assimilação de novas informações.

A critical analysis on "Active Learning" in the light of Cultural-Historical Activity Theory (CHAT): Implications to Physics Education and to experimental activities as a particular case.

This is a research paper in which we discuss “active learning” in the light of Cultural-Historical Activity Theory (CHAT), a powerful framework to analyze human activity, including teaching and learning process and the relations between education and wider human dimensions as politics, development, emancipation etc. This framework has its origin in Vygotsky's works in the psychology, supported by a Marxist perspective, but nowadays is a interdisciplinary field encompassing History, Anthropology, Psychology, Education for example.

DIGITAL LIBRARIES AND LEARNING OBJECTS REPOSITORIES

Discusses the technological changes that affects learning organizations as well as the human, technical, legal and sustainable aspects regarding learning objects repositories creation, maintenance and use. It presents concepts of information objects and learning objects, the functional requirements needed to their storage at Learning Management Systems. The role of Metadata is reviewed concerning learning objects creation and retrieval, followed by considerations about learning object repositories models, community participation/collaborative strategies and potential derived metrics/indicators. As a result of this desktop research, it can be said that not only technical competencies are critical to any learning objects repository implementation, but it urges that an engaged community of interest be establish as a key to support a learning object repository project. On that matter, researchers are applying Activity Theory (Vygostky, Luria y Leontiev) in order to seek joint perceptions and actions involving learning objects repository users, curators and managers, perceived as critical assets to a successful proposal.

Stochastic competitive learning in complex networks

Competitive learning is an important machine learning approach which is widely employed in artificial neural networks. In this paper, we present a rigorous definition of a new type of competitive learning scheme realized on large-scale networks. The model consists of several particles walking within the network and competing with each other to occupy as many nodes as possible, while attempting to reject intruder particles. The particle's walking rule is composed of a stochastic combination of random and preferential movements. The model has been applied to solve community detection and data clustering problems. Computer simulations reveal that the proposed technique presents high precision of community and cluster detections, as well as low computational complexity. Moreover, we have developed an efficient method for estimating the most likely number of clusters by using an evaluator index that monitors the information generated by the competition process itself. We hope this paper will provide an alternative way to the study of competitive learning.

Network-based stochastic semisupervised learning

Semisupervised learning is a machine learning approach that is able to employ both labeled and unlabeled samples in the training process. In this paper, we propose a semisupervised data classification model based on a combined random-preferential walk of particles in a network (graph) constructed from the input dataset. The particles of the same class cooperate among themselves, while the particles of different classes compete with each other to propagate class labels to the whole network. A rigorous model definition is provided via a nonlinear stochastic dynamical system and a mathematical analysis of its behavior is carried out. A numerical validation presented in this paper confirms the theoretical predictions. An interesting feature brought by the competitive-cooperative mechanism is that the proposed model can achieve good classification rates while exhibiting low computational complexity order in comparison to other network-based semisupervised algorithms. Computer simulations conducted on synthetic and real-world datasets reveal the effectiveness of the model.