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São várias as razões que tornam a aprendizagem de programação um processo difícil, ao qual as abordagens de ensino tradicionais não têm conseguido responder eficazmente. Muitas soluções tecnológicas têm sido desenvolvidas, porém o problema subsiste. Para além de diversas razões apontadas por muitos autores como estando na origem deste problema, destacamos a elevada dificuldade apresentada pelos alunos para resolver problemas genéricos. Esta dificuldade é acentuada quando os problemas são mais orientados à programação, traduzindo-se na incapacidade de concepção de algoritmos. Encontra-se em desenvolvimento uma nova proposta que se centra essencialmente no desenvolvimento de competências de resolução de problemas, genéricos numa fase de conhecimento inicial e orientados à programação em fases cognitivas mais avançadas. Este novo ambiente assenta em duas estruturas basilares, os estilos de aprendizagem preferenciais de cada aluno e o seu nível cognitivo. Relativamente a este último aspecto incorpora também mecanismos para treinar as funções cognitivas em défice.

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In recent years, many tools have been proposed to reduce programming learning difficulties felt by many students. Our group has contributed to this effort through the development of several tools, such as VIP, SICAS, OOP-Anim, SICAS-COL and H-SICAS. Even though we had some positive results, the utilization of these tools doesn’t seem to significantly reduce weaker student’s difficulties. These students need stronger support to motivate them to get engaged in learning activities, inside and outside classroom. Nowadays, many technologies are available to create contexts that may help to accomplish this goal. We consider that a promising path goes through the integration of solutions. In this paper we analyze the features, strengths and weaknesses of the tools developed by our group. Based on these considerations we present a new environment, integrating different types of pedagogical approaches, resources, tools and technologies for programming learning support. With this environment, currently under development, it will be possible to review contents and lessons, based on video and screen captures. The support for collaborative tasks is another key point to improve and stimulate different models of teamwork. The platform will also allow the creation of various alternative models (learning objects) for the same subject, enabling personalized learning paths adapted to each student knowledge level, needs and preferential learning styles. The learning sequences will work as a study organizer, following a suitable taxonomy, according to student’s cognitive skills. Although the main goal of this environment is to support students with more difficulties, it will provide a set of resources supporting the learning of more advanced topics. Software engineering techniques and representations, object orientation and event programming are features that will be available in order to promote the learning progress of students

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The techniques of factor analysis are used to investigate a data matrix constructed from magnetic resonance parameters for seventy-five 4-substituted styrenes, α-methylstyrenes, and α-tert-butylstyrenes and 3-substituted styrenes and α-methylstyrenes (with fifteen common substituents for each series). Two substituent factors (parameters) are clearly both necessary and sufficient to predict long-range chemical shifts within experimental uncertainty. Target testing indicates that Taft's σ1 and [Formula: see text] constants are appropriate for this purpose. One additional major factor is required for each of ipso and ortho carbons and a minor factor is apparently required for meta carbons.Conclusions do not appear to be sensitive to the weighting of individual variables and the specific variables included in the data matrix. However, the use of an extensive and well-chosen substituent set is essential.