823 resultados para Middle phase of learning
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A Guidebook for First Responders During the Initial Phase of a Dangerous Goods/Hazardous Materials Transportation Incident.
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Millennials generation is changing the way of learning, prompting educational institutions to attempt to better adapt to young needs by incorporating technologies into education. Based on this premise, we have reviewed the prominent reports of the integration of ICT into education with the aim of evidencing how education is changing, and will change, to meet the needs ofMillennials with ICT support. We conclude that most of the investments have simply resulted in an increase of computers and access to the Internet, with teachers reproducing traditional approaches to education and e-learning being seen as complementary to face-to-face education. While it would seem that the use of ICT is not revolutionizing learning, it is facilitating the personalization, collaboration and ubiquity of learning.
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The Information Society has provided the context for the development of a new generation, known as the Millennials, who are characterized by their intensive use of technologies in everyday life. These features are changing the way of learning, prompting educational institutions to attempt to better adapt to youngneeds by incorporating technologies into education. Based on this premise, wehave reviewed the prominent reports of the integration of ICT into education atdifferent levels with the aim of evidencing how education is changing, and willchange, to meet the needs of Millennials with ICT support. The results show thatmost of the investments have simply resulted in an increase of computers andaccess to the Internet, with teachers reproducing traditional approaches to education and e-learning being seen as complementary to face-to-face education.While it would seem that the use of ICT is not revolutionizing learning, it isfacilitating the personalization, collaboration and ubiquity of learning.
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Even though laboratory evolution experiments have demonstrated genetic variation for learning ability, we know little about the underlying genetic architecture and genetic relationships with other ecologically relevant traits. With a full diallel cross among twelve inbred lines of Drosophila melanogaster originating from a natural population (0.75 < F < 0.93), we investigated the genetic architecture of olfactory learning ability and compared it to that for another behavioral trait (unconditional preference for odors), as well as three traits quantifying the ability to deal with environmental challenges: egg-to-adult survival and developmental rate on a low-quality food, and resistance to a bacterial pathogen. Substantial additive genetic variation was detected for each trait, highlighting their potential to evolve. Genetic effects contributed more than nongenetic parental effects to variation in traits measured at the adult stage: learning, odorant perception, and resistance to infection. In contrast, the two traits quantifying larval tolerance to low-quality food were more strongly affected by parental effects. We found no evidence for genetic correlations between traits, suggesting that these traits could evolve at least to some degree independently of one another. Finally, inbreeding adversely affected all traits.
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In order to understand the development of non-genetically encoded actions during an animal's lifespan, it is necessary to analyze the dynamics and evolution of learning rules producing behavior. Owing to the intrinsic stochastic and frequency-dependent nature of learning dynamics, these rules are often studied in evolutionary biology via agent-based computer simulations. In this paper, we show that stochastic approximation theory can help to qualitatively understand learning dynamics and formulate analytical models for the evolution of learning rules. We consider a population of individuals repeatedly interacting during their lifespan, and where the stage game faced by the individuals fluctuates according to an environmental stochastic process. Individuals adjust their behavioral actions according to learning rules belonging to the class of experience-weighted attraction learning mechanisms, which includes standard reinforcement and Bayesian learning as special cases. We use stochastic approximation theory in order to derive differential equations governing action play probabilities, which turn out to have qualitative features of mutator-selection equations. We then perform agent-based simulations to find the conditions where the deterministic approximation is closest to the original stochastic learning process for standard 2-action 2-player fluctuating games, where interaction between learning rules and preference reversal may occur. Finally, we analyze a simplified model for the evolution of learning in a producer-scrounger game, which shows that the exploration rate can interact in a non-intuitive way with other features of co-evolving learning rules. Overall, our analyses illustrate the usefulness of applying stochastic approximation theory in the study of animal learning.
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The purpose of this meta-analysis was to examine the efficacy of maintenance treatments for bipolar disorder. Placebo-controlled or active comparator bipolar maintenance clinical trials of ≥6 months' duration with at least 15 patients/treatment group were identified using Medline, EMBASE, clinicaltrials.gov, and Cochrane databases (1993 to July 2010). The main outcome measure was relative risk for relapse for patients in remission. Twenty trials (5,364 patients) were identified. Overall, lithium and quetiapine were the most studied agents (eight and five trials, respectively). The majority of studies included patients who had previously responded to treatment for an acute episode. All interventions, with the exception of perphenazine+mood stabilizer, showed a relative risk for manic/mixed or depressive relapse below 1.0, although there was variation in the statistical significance of the findings vs. placebo. No monotherapy was associated with a significantly reduced risk for both manic/mixed and depressed relapse. Of the combination treatments, only quetiapine+lithium/divalproex, was associated with a significantly reduced risk vs. comparator (placebo+lithium/valproate) for relapse at both the manic/mixed and depressed poles of bipolar illness. Limitations for the analysis include differences in study durations and definitions of relapse. In conclusion, available maintenance therapies show considerable variation in efficacy. The efficacy of lithium and divalproex has been confirmed, but newer therapies, such as a number of atypical antipsychotics were also shown to be effective in bipolar disorder. Efficacy of all maintenance interventions needs to be balanced against the safety and tolerability profiles of individual agents.
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Learning object repositories are a basic piece of virtual learning environments used for content management. Nevertheless, learning objects have special characteristics that make traditional solutions for content management ine ective. In particular, browsing and searching for learning objects cannot be based on the typical authoritative meta-data used for describing content, such as author, title or publicationdate, among others. We propose to build a social layer on top of a learning object repository, providing nal users with additional services fordescribing, rating and curating learning objects from a teaching perspective. All these interactions among users, services and resources can be captured and further analyzed, so both browsing and searching can be personalized according to user pro le and the educational context, helping users to nd the most valuable resources for their learning process. In this paper we propose to use reputation schemes and collaborative filtering techniques for improving the user interface of a DSpace based learning object repository.
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Institutional digital repositories are a basic piece to provide preservation and reutilization of learning resources. However, their creation and maintenance is usually performed following a top-down approach, causing limitations in the search and reutilization of learning resources. In order to avoid this problem we propose to use web 2.0 functionalities. In this paper we present how tagging can be used to enhance the search and reusability functionalities of institutional learning repositories as well as promoting their usage. The paper also describes the evaluation process that was performed in a pilot experience involving open educational resources.
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To study the influence of the menstrual cycle on whole body thermal balance and on thermoregulatory mechanisms, metabolic heat production (M) was measured by indirect calorimetry and total heat losses (H) were measured by direct calorimetry in nine women during the follicular (F) and the luteal (L) phases of the menstrual cycle. The subjects were studied while exposed for 90 min to neutral environmental conditions (ambient temperature 28 degrees C, relative humidity 40%) in a direct calorimeter. The values of M and H were not modified by the phase of the menstrual cycle. Furthermore, in both phases the subjects were in thermal equilibrium because M was similar to H (69.7 +/- 1.8 and 72.1 +/- 1.8 W in F and 70.4 +/- 1.9 and 71.4 +/- 1.7 W in L phases, respectively). Tympanic temperature (Tty) was 0.24 +/- 0.07 degrees C higher in the L than in the F phase (P less than 0.05), whereas mean skin temperature (Tsk) was unchanged. Calculated skin thermal conductance (Ksk) was lower in the L (17.9 +/- 0.6 W.m-2.degrees C-1) than in the F phase (20.1 +/- 1.1 W.m-2.degrees C-1; P less than 0.05). Calculated skin blood flow (Fsk) was also lower in the L (0.101 +/- 0.008 l.min-1.m-2) than in the F phase (0.131 +/- 0.015 l.min-1.m-2; P less than 0.05). Differences in Tty, Ksk, and Fsk were not correlated with changes in plasma progesterone concentration. It is concluded that, during the L phase, a decreased thermal conductance in women exposed to a neutral environment allows the maintenance of a higher internal temperature.
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Semantic Web technology is able to provide the required computational semantics for interoperability of learning resources across different Learning Management Systems (LMS) and Learning Object Repositories (LOR). The EU research project LUISA (Learning Content Management System Using Innovative Semantic Web Services Architecture) addresses the development of a reference semantic architecture for the major challenges in the search, interchange and delivery of learning objects in a service-oriented context. One of the key issues, highlighted in this paper, is Digital Rights Management (DRM) interoperability. A Semantic Web approach to copyright management has been followed, which places a Copyright Ontology as the key component for interoperability among existing DRM systems and other licensing schemes like Creative Commons. Moreover, Semantic Web tools like reasoners, rule engines and semantic queries facilitate the implementation of an interoperable copyright management component in the LUISA architecture.
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The fact that individuals learn can change the relationship between genotype and phenotype in the population, and thus affect the evolutionary response to selection. Here we ask how male ability to learn from female response affects the evolution of a novel male behavioral courtship trait under pre-existing female preference (sensory drive). We assume a courtship trait which has both a genetic and a learned component, and a two-level female response to males. With individual-based simulations we show that, under this scenario, learning generally increases the strength of selection on the genetic component of the courtship trait, at least when the population genetic mean is still low. As a consequence, learning not only accelerates the evolution of the courtship trait, but also enables it when the trait is costly, which in the absence of learning results in an adaptive valley. Furthermore, learning can enable the evolution of the novel trait in the face of gene flow mediated by immigration of males that show superior attractiveness to females based on another, non-heritable trait. However, rather than increasing monotonically with the speed of learning, the effect of learning on evolution is maximized at intermediate learning rates. This model shows that, at least under some scenarios, the ability to learn can drive the evolution of mating behaviors through a process equivalent to Waddington's genetic assimilation.