Estilos de aprendizagem sob a ótica da Psicologia Evolucionista

Among a variety of learning conceptions, David Kolb´s Experiential Learning Theory proposes four different learning styles: diverging, characterized by orientation towards people and multi-perspective vision; assimilating, concerned with ideas and abstract concepts as well as theory formulation; converging, expert in dealing with technical tasks and problem solving; and accommodating, risk taker and good at getting things done. Interesting correlations have been pointed out between Kolb s learning styles, professional careers and genders. With respect to behaviors, specific cognitive skills and interests, sex differences are widely known, and explained by Evolutionary Psychology as the result of distinct selective pressures acting on each gender. The aim of this research was to assess adolescents learning styles and their relation with interests on school and career choices, analyzing possible gender differences. We distributed questionnaires to 221 senior high school students to research their preferences for school disciplines, professional activities and career choices. The Learning Style Inventory specified the learning style of each individual. Our results showed a high frequency of reflective styles, with predominance of females as diverging and males as assimilating. Concerning school and professional interests, there were correlations between styles oriented towards the abstract and technical interests. Moreover, females preferred disciplines related to languages and interpersonal activities while males preferred disciplines related to science and technical activities. There were more males in exact science and engineering careers, and more females in social science and applied social science. Correlations found between learning styles, school and professional interests corroborate Kolb´s propositions, and the findings about gender differences are supported by Evolutionary Psychology theories

AdaptMCloud: uma estratégia para adaptação dinâmica de aplicações Multi-Cloud

Multi-Cloud Applications are composed of services offered by multiple cloud platforms where the user/developer has full knowledge of the use of such platforms. The use of multiple cloud platforms avoids the following problems: (i) vendor lock-in, which is dependency on the application of a certain cloud platform, which is prejudicial in the case of degradation or failure of platform services, or even price increasing on service usage; (ii) degradation or failure of the application due to fluctuations in quality of service (QoS) provided by some cloud platform, or even due to a failure of any service. In multi-cloud scenario is possible to change a service in failure or with QoS problems for an equivalent of another cloud platform. So that an application can adopt the perspective multi-cloud is necessary to create mechanisms that are able to select which cloud services/platforms should be used in accordance with the requirements determined by the programmer/user. In this context, the major challenges in terms of development of such applications include questions such as: (i) the choice of which underlying services and cloud computing platforms should be used based on the defined user requirements in terms of functionality and quality (ii) the need to continually monitor the dynamic information (such as response time, availability, price, availability), related to cloud services, in addition to the wide variety of services, and (iii) the need to adapt the application if QoS violations affect user defined requirements. This PhD thesis proposes an approach for dynamic adaptation of multi-cloud applications to be applied when a service is unavailable or when the requirements set by the user/developer point out that other available multi-cloud configuration meets more efficiently. Thus, this work proposes a strategy composed of two phases. The first phase consists of the application modeling, exploring the similarities representation capacity and variability proposals in the context of the paradigm of Software Product Lines (SPL). In this phase it is used an extended feature model to specify the cloud service configuration to be used by the application (similarities) and the different possible providers for each service (variability). Furthermore, the non-functional requirements associated with cloud services are specified by properties in this model by describing dynamic information about these services. The second phase consists of an autonomic process based on MAPE-K control loop, which is responsible for selecting, optimally, a multicloud configuration that meets the established requirements, and perform the adaptation. The adaptation strategy proposed is independent of the used programming technique for performing the adaptation. In this work we implement the adaptation strategy using various programming techniques such as aspect-oriented programming, context-oriented programming and components and services oriented programming. Based on the proposed steps, we tried to assess the following: (i) the process of modeling and the specification of non-functional requirements can ensure effective monitoring of user satisfaction; (ii) if the optimal selection process presents significant gains compared to sequential approach; and (iii) which techniques have the best trade-off when compared efforts to development/modularity and performance.