Alternative Tasks for the Teaching and the Learning of functions: analysis of an intervention in the High School

In this paper we focus on the application of two mathematical alternative tasks to the teaching and learning of functions with high school students. The tasks were elaborated according to the following methodological approach: (i) Problem Solving and/or mathematics investigation and (ii) a pedagogical proposal, which defends that mathematical knowledge is developed by means of a balance between logic and intuition. We employed a qualitative research approach (characterized as a case study) aimed at analyzing the didactic pedagogical potential of this type of methodology in high school. We found that tasks such as those presented and discussed in this paper provide a more significant learning for the students, allowing a better conceptual understanding, becoming still more powerful when one considers the social-cultural context of the students.

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.

Analysis of MLL2 gene in the first Brazilian family with Kabuki syndrome

Most patients with Kabuki syndrome (KS) are the only person in their family with the condition. However, familial cases of KS have been described showing evidence that this syndrome can be inherited as a dominant trait with variable expressivity. We report on two related individuals with facial findings characteristic of KS. The proposita had arched eyebrows, long and upward slanting palpebral fissures, cleft lip and palate, retromicrognathia, brachydactyly of hands and feet, stubby fingers, nail hypoplasia, and prominent finger pads. Her mother had eyebrows with dispersed lateral half, long and upward slanting palpebral fissures, retrognathia, abnormal and posteriorly rotated ears, prominent finger pads, brachydactyly of feet, learning difficulties, and psychomotor development delay. DNA sequencing revealed a novel missense mutation in the MLL2 gene in both the proposita and her mother. The mutation (p.R5432Q) was found in the exon 51, within the SET domain of the gene, which confers methyltransferase activity on the protein. Therefore, the epigenetic and transcriptional regulatory properties of this protein may be altered and this suggests that the mutation is the cause of phenotype observed in both the patient and her mother. The clinical signs and the molecular evidence in this family further support the notion that KS is an autosomal dominant condition with variable expressivity. To our knowledge this is the first report of a Brazilian family with recurrence of this syndrome. (C) 2012 Wiley Periodicals, Inc.

Semi-supervised learning guided by the modularity measure in complex networks

Semi-supervised learning techniques have gained increasing attention in the machine learning community, as a result of two main factors: (1) the available data is exponentially increasing; (2) the task of data labeling is cumbersome and expensive, involving human experts in the process. In this paper, we propose a network-based semi-supervised learning method inspired by the modularity greedy algorithm, which was originally applied for unsupervised learning. Changes have been made in the process of modularity maximization in a way to adapt the model to propagate labels throughout the network. Furthermore, a network reduction technique is introduced, as well as an extensive analysis of its impact on the network. Computer simulations are performed for artificial and real-world databases, providing a numerical quantitative basis for the performance of the proposed method.

VIBRATIONAL ANALYSIS OF COORDINATION COMPOUNDS OF NICKEL (II): AN APPROACH TO THE TEACHING OF POINT GROUPS

VIBRATIONAL ANALYSIS OF COORDINATION COMPOUNDS OF NICKEL (II): AN APPROACH TO THE TEACHING OF POINT GROUPS. This paper presents an IR and Raman experiment executed during the teaching of the course "Chemical Bonds" for undergraduated students of Science and Technology and Chemistry at the Federal University of ABC, in order to facilitate and encourage the teaching and learning of group theory. Some key aspects of this theory are also outlined. We believe that student learning was more significant with the introduction of this experiment, because there was an increase in the discussions level and in the performance during evaluations. This work also proposes a multidisciplinary approach to include the use of quantum chemistry tools.

Saethre-Chotzen phenotype with learning disability and hyper IgE phenotype in a patient due to complex chromosomal rearrangement involving chromosomes 3 and 7

The authors describe on a Brazilian girl with coronal synostosis, facial asymmetry, ptosis, brachydactyly, significant learning difficulties, recurrent scalp infections with marked hair loss, and elevated serum immunoglobulin E. Standard lymphocyte karyotype showed a small additional segment in 7p21[46,XX,add(7)(p21)]. Deletion of the TWIST1 gene, detected by Multiplex Ligation Probe-dependent Amplification (MPLA) and array-CGH, was consistent with phenotype of SaethreChotzen syndrome. Array CGH also showed deletion of four other genes at 7p21.1 (SNX13, PRPS1L1, HD9C9, and FERD3L) and the deletion of six genes (CACNA2D2, C3orf18, HEMK1, CISH, MAPKAPK3, and DOCK3) at 3p21.31. Our case reinforces FERD3L as candidate gene for intellectual disability and suggested that genes located in 3p21.3 can be related to hyper IgE phenotype. (C) 2012 Wiley Periodicals, Inc.

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.

Can neuroimaging be used as a support to diagnosis of borderline personality disorder? An approach based on computational neuroanatomy and machine learning

Several recent studies in literature have identified brain morphological alterations associated to Borderline Personality Disorder (BPD) patients. These findings are reported by studies based on voxel-based-morphometry analysis of structural MRI data, comparing mean gray-matter concentration between groups of BPD patients and healthy controls. On the other hand, mean differences between groups are not informative about the discriminative value of neuroimaging data to predict the group of individual subjects. In this paper, we go beyond mean differences analyses, and explore to what extent individual BPD patients can be differentiated from controls (25 subjects in each group), using a combination of automated-morphometric tools for regional cortical thickness/volumetric estimation and Support Vector Machine classifier. The approach included a feature selection step in order to identify the regions containing most discriminative information. The accuracy of this classifier was evaluated using the leave-one-subject-out procedure. The brain regions indicated as containing relevant information to discriminate groups were the orbitofrontal, rostral anterior cingulate, posterior cingulate, middle temporal cortices, among others. These areas, which are distinctively involved in emotional and affect regulation of BPD patients, were the most informative regions to achieve both sensitivity and specificity values of 80% in SVM classification. The findings suggest that this new methodology can add clinical and potential diagnostic value to neuroimaging of psychiatric disorders. (C) 2012 Elsevier Ltd. All rights reserved.

Frequency and precision of feedback and the adaptive process of learning a dual motor task

This work investigated the effects of frequency and precision of feedback on the learning of a dual-motor task. One hundred and twenty adults were randomly assigned to six groups of different knowledge of results (KR), frequency (100%, 66% or 33%) and precision (specific or general) levels. In the stabilization phase, participants performed the dual task (combination of linear positioning and manual force control) with the provision of KR. Ten non-KR adaptation trials were performed for the same task, but with the introduction of an electromagnetic opposite traction force. The analysis showed a significant main effect for frequency of KR. The participants who received KR in 66% of the stabilization trials showed superior adaptation performance than those who received 100% or 33%. This finding reinforces that there is an optimal level of information, neither too high nor too low, for motor learning to be effective.