The pedagogical content knowledge of Danish geography teachers in a changing schooling context

This study examines the self-reported, topic-specific professional knowledge (TSPK) of Danish geography teachers seen as an aspect of their pedagogical content knowledge (PCK) when teaching weather formation and climate change. This topic is considered representative of geography teaching in Denmark. In the last ten years Danish primary and lower-secondary schooling has undergone several significant changes, including the introduction of a final multiple-choice exam in geography in 2007, and a fundamental reconstruction of the curriculum in 2014. These changes are expected to influence the TSPK of geography teachers in ways that potentially have an impact on their classroom practice. Teachers´ responses to specific questions relating to their choice of learning goals and the content and organisation of their lessons show that geography teachers take into account not only the knowledge aspects which point to the final multiple-choice exam, but also the ‘bildung’ perspectives of the subject equipping students to develop their own opinions when dealing with socio-scientific issues (SSI).

Strategies to Promote Research and Logical Reasoning in the Teaching of Mathematics in the Schools

The purpose of this paper is to raise a debate on the urgent need for teachers to generate innovative situations in the teaching-learning process, in the field of Mathematics, as a way for students to develop logical reasoning and research skills applicable to everyday situations. It includes some statistical data and possible reasons for the poor performance and dissatisfaction of students towards Mathematics. Since teachers are called to offer meaningful and functional learning experiences to students, in order to promote the pleasure of learning, teacher training should include experiences that can be put into practice by teachers in the education centers. This paper includes a work proposal for Mathematics Teaching to generate discussion, curiosity and logical reasoning in students, together with the Mathematical problem solving study.

Adaptive and Maladaptive Implications of Reinforcement Learning Processes: Fronto-Striatal Loops and Behavioural Correlates

That humans and animals learn from interaction with the environment is a foundational idea underlying nearly all theories of learning and intelligence. Learning that certain outcomes are associated with specific actions or stimuli (both internal and external), is at the very core of the capacity to adapt behaviour to environmental changes. In the present work, appetitive and aversive reinforcement learning paradigms have been used to investigate the fronto-striatal loops and behavioural correlates of adaptive and maladaptive reinforcement learning processes, aiming to a deeper understanding of how cortical and subcortical substrates interacts between them and with other brain systems to support learning. By combining a large variety of neuroscientific approaches, including behavioral and psychophysiological methods, EEG and neuroimaging techniques, these studies aim at clarifying and advancing the knowledge of the neural bases and computational mechanisms of reinforcement learning, both in normal and neurologically impaired population.

Development of advanced methods for the simulation of the reacting mixture formation in internal combustion engines with the use of machine learning algorithms

Besides increasing the share of electric and hybrid vehicles, in order to comply with more stringent environmental protection limitations, in the mid-term the auto industry must improve the efficiency of the internal combustion engine and the well to wheel efficiency of the employed fuel. To achieve this target, a deeper knowledge of the phenomena that influence the mixture formation and the chemical reactions involving new synthetic fuel components is mandatory, but complex and time intensive to perform purely by experimentation. Therefore, numerical simulations play an important role in this development process, but their use can be effective only if they can be considered accurate enough to capture these variations. The most relevant models necessary for the simulation of the reacting mixture formation and successive chemical reactions have been investigated in the present work, with a critical approach, in order to provide instruments to define the most suitable approaches also in the industrial context, which is limited by time constraints and budget evaluations. To overcome these limitations, new methodologies have been developed to conjugate detailed and simplified modelling techniques for the phenomena involving chemical reactions and mixture formation in non-traditional conditions (e.g. water injection, biofuels etc.). Thanks to the large use of machine learning and deep learning algorithms, several applications have been revised or implemented, with the target of reducing the computing time of some traditional tasks by orders of magnitude. Finally, a complete workflow leveraging these new models has been defined and used for evaluating the effects of different surrogate formulations of the same experimental fuel on a proof-of-concept GDI engine model.

Development of machine learning methods for multi-modal biomarkers detection and integration

In medicine, innovation depends on a better knowledge of the human body mechanism, which represents a complex system of multi-scale constituents. Unraveling the complexity underneath diseases proves to be challenging. A deep understanding of the inner workings comes with dealing with many heterogeneous information. Exploring the molecular status and the organization of genes, proteins, metabolites provides insights on what is driving a disease, from aggressiveness to curability. Molecular constituents, however, are only the building blocks of the human body and cannot currently tell the whole story of diseases. This is why nowadays attention is growing towards the contemporary exploitation of multi-scale information. Holistic methods are then drawing interest to address the problem of integrating heterogeneous data. The heterogeneity may derive from the diversity across data types and from the diversity within diseases. Here, four studies conducted data integration using customly designed workflows that implement novel methods and views to tackle the heterogeneous characterization of diseases. The first study devoted to determine shared gene regulatory signatures for onco-hematology and it showed partial co-regulation across blood-related diseases. The second study focused on Acute Myeloid Leukemia and refined the unsupervised integration of genomic alterations, which turned out to better resemble clinical practice. In the third study, network integration for artherosclerosis demonstrated, as a proof of concept, the impact of network intelligibility when it comes to model heterogeneous data, which showed to accelerate the identification of new potential pharmaceutical targets. Lastly, the fourth study introduced a new method to integrate multiple data types in a unique latent heterogeneous-representation that facilitated the selection of important data types to predict the tumour stage of invasive ductal carcinoma. The results of these four studies laid the groundwork to ease the detection of new biomarkers ultimately beneficial to medical practice and to the ever-growing field of Personalized Medicine.

Analysis of human posterior EEG alpha frequency as a function of implicit learning of probabilistic lateralized stimuli in a visual detection task

Alpha oscillatory activity has long been associated with perceptual and cognitive processes related to attention control. The aim of this study is to explore the task-dependent role of alpha frequency in a lateralized visuo-spatial detection task. Specifically, the thesis focuses on consolidating the scientific literature's knowledge about the role of alpha frequency in perceptual accuracy, and deepening the understanding of what determines trial-by-trial fluctuations of alpha parameters and how these fluctuations influence overall task performance. The hypotheses, confirmed empirically, were that different implicit strategies are put in place based on the task context, in order to maximize performance with optimal resource distribution (namely alpha frequency, associated positively with performance): “Lateralization” of the attentive resources towards one hemifield should be associated with higher alpha frequency difference between contralateral and ipsilateral hemisphere; “Distribution” of the attentive resources across hemifields should be associated with lower alpha frequency difference between hemispheres; These strategies, used by the participants according to their brain capabilities, have proven themselves adaptive or maladaptive depending on the different tasks to which they have been set: "Distribution" of the attentive resources seemed to be the best strategy when the distribution probability between hemifields was balanced: i.e. the neutral condition task. "Lateralization" of the attentive resources seemed to be more effective when the distribution probability between hemifields was biased towards one hemifield: i.e., the biased condition task.

Sensitivity and specificity of machine learning classifiers for glaucoma diagnosis using Spectral Domain OCT and standard automated perimetry

PURPOSE: To evaluate the sensitivity and specificity of machine learning classifiers (MLCs) for glaucoma diagnosis using Spectral Domain OCT (SD-OCT) and standard automated perimetry (SAP). METHODS: Observational cross-sectional study. Sixty two glaucoma patients and 48 healthy individuals were included. All patients underwent a complete ophthalmologic examination, achromatic standard automated perimetry (SAP) and retinal nerve fiber layer (RNFL) imaging with SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec Inc., Dublin, California). Receiver operating characteristic (ROC) curves were obtained for all SD-OCT parameters and global indices of SAP. Subsequently, the following MLCs were tested using parameters from the SD-OCT and SAP: Bagging (BAG), Naive-Bayes (NB), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Random Forest (RAN), Ensemble Selection (ENS), Classification Tree (CTREE), Ada Boost M1(ADA),Support Vector Machine Linear (SVML) and Support Vector Machine Gaussian (SVMG). Areas under the receiver operating characteristic curves (aROC) obtained for isolated SAP and OCT parameters were compared with MLCs using OCT+SAP data. RESULTS: Combining OCT and SAP data, MLCs' aROCs varied from 0.777(CTREE) to 0.946 (RAN).The best OCT+SAP aROC obtained with RAN (0.946) was significantly larger the best single OCT parameter (p<0.05), but was not significantly different from the aROC obtained with the best single SAP parameter (p=0.19). CONCLUSION: Machine learning classifiers trained on OCT and SAP data can successfully discriminate between healthy and glaucomatous eyes. The combination of OCT and SAP measurements improved the diagnostic accuracy compared with OCT data alone.

Avaliação do Processo de Ensino e Aprendizagem em Matemática: contribuições da teoria histórico-cultural

This article presents the results of a study that investigated the meaning of evaluation in mathematics from the historical cultural perspective, focusing on activity theory. In order to develop the investigation, a collaborative group was formed from the Oficina Pedagogica de Matematica de Ribeirao Preto - Sao Paulo (Math Pedagogic Workshop of Ribeirao Preto - OPM/RP), constituted of pre-school teachers and early elementary school teachers, who were participants in this research. The main role of the collaborative group was to offer guided development to the teachers about the teaching of mathematics from the historical-cultural perspective, aiming at collecting data on the process of appropriation of mathematical knowledge by the teachers. The syntheses about the teachers' learning process have contributed to systematize the guiding elements of evaluation in mathematics from the historical-cultural perspective.

Relative frequency of knowledge of results and task complexity in the motor skill acqusition

The aim of this study was to investigate the effects of knowledge of results (KR) frequency and task complexity on motor skill acquisition. The task consisted of throwing a bocha ball to place it as close as possible to the target ball. 120 students ages 11 to 73 years were assigned to one of eight experimental groups according to knowledge of results frequency (25, 50, 75, and 100%) and task complexity (simple and complex). Subjects performed 90 trials in the acquisition phase and 10 trials in the transfer test. The results showed that knowledge of results given at a frequency of 25% resulted in an inferior absolute error than 50% and inferior variable error than 50, 75, and 100 I frequencies, but no effect of task complexity was found.

The Learning Tourism Destination: The potential of a learning organisation approach for improving the sustainability of tourism destinations

Globalisation, increasing complexity, and the need to address triple-bottom line sustainability has seen the proliferation of Learning Organisations (LO) who, by definition, have the capacity to anticipate environmental changes and economic opportunities and adapt accordingly. Such organisations use system dynamics modelling (SDM) for both strategic planning and the promotion of organisational learning. Although SDM has been applied in the context of tourism destination management for predictive reasons, the current literature does not analyse or recognise how this could be used as a foundation for an LO. This study introduces the concept of the Learning Tourism Destinations (LTD) and discusses, on the basis of a review of 6 case studies, the potential of SDM as a tool for the implementation and enhancement of collective learning processes. The results reveal that SDM is capable of promoting communication between stakeholders and stimulating organisational learning. It is suggested that the LTD approach be further utilised and explored.

Knowledge of birds and willingness to pay for their conservation: An Australian case study

This case study concentrates on the extent of knowledge of members of the Australian public of Australia’s tropical bird species and their willingness to pay for their conservation. In order to place this issue in context, it first provides background information on the status of Australian bird species, focusing attention on species that occur in tropical Australia. Then, using survey results, this study considers the hypothesis that the public’s relative support for the conservation of different bird species depends on its comparative knowledge of their existence and status. Based on experimental results from a sample of residents of Brisbane, Queensland (Australia), it is found that their knowledge of bird species that occur exclusively in the Australian tropics (including tropical Queensland) is very poor compared to those that also occur in the Brisbane area and are relatively common. Experimental results indicate that when respondents in the sample had an option to allocate $1,000 between ten bird species listed in the survey, it resulted in a greater allocation of funds to the better known and more common species than when they were provided with balanced information about all the selected species. With balanced information the average allocation to bird species confined mostly to the Australian tropics, particularly those threatened or endangered, increased. The general consequences of this for policies for the conservation of birds are discussed.

Transfer and Reverse Transfer of Knowledge of International Acquisitions: the case of the acquisition of the Perez Companc by Petrobras in Argentina

The objective of this article was to analyze the processes of transfer and reverse trans fer of knowledge following. international acquisitions made by Brazilian multinational companies. Reverse transfer is understood,as the process of transferring knowledge from the acquired company to the acquirer. Therefore, a case study was conducted on the acquisition of the Perez Companc group by Petrobras in Argentina. The study is qualitative. Primary data were obtained and eight members of the international managing board of Petrobras were interviewed. After the first moment of integration, reported as conflictive, there was a better integration of the companies, mainly in the technical areas of, the oil and gas exploration activities. The size of Perez Companc, its aim (a company of energy, not only oil and gas company) and the length of time were critical factors for the transfer of best practices between the companies. The expatriation of the employees is seen as a key-tool, as well as the technical visits, for the transfer of knowledge.. An. additional contribution of the study was to present the results of the research on the process of transfer and reverse transfer of knowledge in Brazilian multinational companies, since most studies on the theme focus on the motivators and challenges concerning these processes.

Extinction of affective learning: No evidence for dual process accounts of human learning

The acquisition and extinction of affective valence to neutral geometrical shape conditional stimuli was investigated in three experiments. Experiment 1 employed a differential conditioning procedure with aversive shock USs. Differential electrodermal responding was evident during acquisition and lost during extinction. As indexed by verbal ratings, the CS1 acquired negative valence during acquisition,which was reduced after extinction. Affective priming, a reaction time based demand free measure of stimulus valence, failed to provide evidence for affective learning. Experiment 2 employed pictures of happy and angry faces as USs.Valence ratings after acquisitionweremore positive for theCS paired with happy faces (CS-H) and less positive for the CS paired with angry faces (CS-A) than during baseline. Extinction training reduced the extent of acquired valence significantly for both CSs, however, ratings of the CS-A remained different from baseline. Affective priming confirmed these results yielding differences between CS-A and CS-H after acquisition for pleasant and unpleasant targets, but for pleasant targets only after extinction. Experiment 3 replicated the design of Experiment 2, but presented the US pictures backwardly masked. Neither rating nor affective priming measures yielded any evidence for affective learning. The present results confirm across two different experimental procedures that, contrary to predictions from dual process accounts of human learning, affective learning is subject to extinction.

Sexual behavior and knowledge of sexually transmitted infections among university students in Sao Paulo, Brazil

Objective: To investigate the sexual behavior and knowledge about sexually transmitted infections (STIs) among undergraduate students in Sao Paulo, Brazil. Methods: Self-reported questionnaires were used. Results: Most of the 447 students in the study were single (97.3%), in their first year of university (87.7%), and the mean ages were 20.4 years (males) and 19.8 years (females). Vaginal intercourse was practiced by 69.7% of males and 48.4% of females, oral sex by 64.5% of males and 43.7% of females, and anal sex by 18.4% of males and 14.1% of females. Use of a condom during vaginal sex was practiced by 80.4% of males and 74.8% of females and during anal sex by 47.8% of males and 30.0% of females. Knowledge of transmission of STIs was greater than 90% for HIV, syphilis, genital herpes, and gonorrhea; 63%-76% for HPV and genital warts; 30%-34% for Trichomonas and only 16% for Chlamydia. Only 25%-34% knew that HIV was transmitted by breastfeeding; 56%-60% knew that HIV was transmitted by anal sex. Conclusion: Many students engage in high-risk sexual behavior with multiple partners and use condoms inconsistently. Knowledge of the acquisition and modes of sexual and vertical transmission of HIV are strikingly deficient. (C) 2010 International Federation of Gynecology and Obstetrics. Published by Elsevier Ireland Ltd. All rights reserved.