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.

The place of (explicit) grammar within the methods and approaches of foreign language teaching: in the north, in the centre or on the margins?

This article analyzes the role that has been attributed to grammar throughout the history of foreign language teaching, with special emphasis on methods and approaches of the twentieth century. In order to support our argument, we discuss the notion of grammar by proposing a conceptual continuum that includes the main meanings of the term which are relevant to our research. We address as well the issue of "pedagogical grammar" and consider the position of grammar in the different approaches of the "era of the methods" and the current "post-method condition" in the field of language teaching and learning. The findings presented at the end of the text consist of recognizing the central role that grammar has played throughout the history of the methods and approaches, where grammar has always been present by the definition of the contents' progression. The rationale that we propose for this is the recognition of the fact that the dissociation between what is said and how it is said can not be more than theoretical and, thus, artificial.

Effect of Nintendo Wii (TM)-based motor and cognitive training on activities of daily living in patients with Parkinson's disease: A randomised clinical trial

Objectives To investigate the effect of Nintendo Wii (TM)-based motor cognitive training versus balance exercise therapy on activities of daily living in patients with Parkinson's disease. Design Parallel, prospective, single-blind, randomised clinical trial. Setting Brazilian Parkinson Association. Participants Thirty-two patients with Parkinson's disease (Hoehn and Yahr stages 1 and 2). Interventions Fourteen training sessions consisting of 30 minutes of stretching, strengthening and axial mobility exercises, plus 30 minutes of balance training. The control group performed balance exercises without feedback or cognitive stimulation, and the experimental group performed 10 Wii Fit (TM) games. Main outcome measure Section II of the Unified Parkinson's Disease Rating Scale (UPDRS-II). Randomisation Participants were randomised into a control group (n = 16) and an experimental group (n = 16) through blinded drawing of names. Statistical analysis Repeated-measures analysis of variance (RM-ANOVA). Results Both groups showed improvement in the UPDRS-II with assessment effect (RM-ANOVA P < 0.001, observed power = 0.999). There was no difference between the control group and the experimental group before training {8.9 [standard deviation (SD) 2.9] vs 10.1 (SD 3.8)}, after training [7.6 (SD 2.9) vs 8.1 (SD 3.5)] or 60 days after training [8.1 (SD 3.2) vs 8.3 (SD 3.6)]. The mean difference of the whole group between before training and after training was -0.9 (SD 2.3, 95% confidence interval -1.7 to -0.6). Conclusion Patients with Parkinson's disease showed improved performance in activities of daily living after 14 sessions of balance training, with no additional advantages associated with the Wii-based motor and cognitive training. Registered on http://www.clinicaltrials.gov (identifier: NCT01580787). (C) 2012 Chartered Society of Physiotherapy. Published by Elsevier Ltd. All rights reserved.

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.

Non-Dominated Sorting Genetic Algorithm Based on Reinforcement Learning to Optimization of Broad-Band Reflector Antennas Satellite

This paper aims to provide an improved NSGA-II (Non-Dominated Sorting Genetic Algorithm-version II) which incorporates a parameter-free self-tuning approach by reinforcement learning technique, called Non-Dominated Sorting Genetic Algorithm Based on Reinforcement Learning (NSGA-RL). The proposed method is particularly compared with the classical NSGA-II when applied to a satellite coverage problem. Furthermore, not only the optimization results are compared with results obtained by other multiobjective optimization methods, but also guarantee the advantage of no time-spending and complex parameter tuning.

Evolving decision trees with beam search-based initialization and lexicographic multi-objective evaluation

Decision tree induction algorithms represent one of the most popular techniques for dealing with classification problems. However, traditional decision-tree induction algorithms implement a greedy approach for node splitting that is inherently susceptible to local optima convergence. Evolutionary algorithms can avoid the problems associated with a greedy search and have been successfully employed to the induction of decision trees. Previously, we proposed a lexicographic multi-objective genetic algorithm for decision-tree induction, named LEGAL-Tree. In this work, we propose extending this approach substantially, particularly w.r.t. two important evolutionary aspects: the initialization of the population and the fitness function. We carry out a comprehensive set of experiments to validate our extended algorithm. The experimental results suggest that it is able to outperform both traditional algorithms for decision-tree induction and another evolutionary algorithm in a variety of application domains.

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.