Challenges for e-learning and adult students in higher education

This article examines Lifelong Learning, from the perspective of the adult learner in higher education, by presenting some of the results of a project, funded by the European Commission's Socrates Programme, LIHE, Learning in Higher Education. It is structured as follows: first, the background of the project is described, then the experiences of the adult student, concerning their induction and tuition, are presented. Some future trends concerning adults in higher education and lifelong learning are outlined and conclusions drawn.

Educational characteristics of adult students in portuguese technological schools

Despite a massive expansion of education in Portugal, since the 1970’s, educational attainment of the adult population in the country remains low. The numbers of working-age people in some form of continuing education are among the lowest, according to the OECD and EU-27 statistics. Technological Schools(TS), initially created in the 1990’s, under the umbrella of the Ministry of Economy in partnership with industry and industrial associations, aimed to prepare qualified staff for industries and services in the country, particularly in the engineering sector, through the provision of post secondary non-university programmes of studies, the CET (Technological Specialization Courses). Successful CET students are awarded a DET(Diploma of Technological Specialization), which corresponds to Vocational Qualification level IV of the EU, according to the latest alteration (2005) of the Education Systems Act (introduced in 1986). In this, CET’s are also clearly defined as one of the routes for access to Higher Education (HE), in Portugal. The PRILHE (Promoting Reflective and Independent Learning in Higher Education) multinational project, funded by the European Socrates Grundtvig Programme, aimed to identify the learning processes which enable adult students in higher education to become autonomous reflective learners and search best practices to support these learning processes. During this research, both quantitative and qualitative methods were used to determine how students organise their studies and develop their learning skills. The Portuguese partner in the project’ consortium used a two case studies approach, one with students of Higher Education Institutions and other with students of TS. This paper only applies to students of TS, as these have a predominant bias towards engineering. Results show that student motivation and professional teaching support contribute equally to the development of an autonomous and reflective approach to learning in adult students; this is essential for success in a knowledge economy, where lifelong learning is the key to continuous employment.

Adult learners in higher education

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Building a knowledge and learning society in Portugal: adult students in technological schools and higher education institutions

Lifelong learning (LLL) has received increasing attention in recent years. It implies that learning should take place at all stages of the “life cycle and it should be life-wide, that is embedded in all life contexts from the school to the work place, the home and the community” (Green, 2002, p.613). The ‘learning society’, is the vision of a society where there are recognized opportunities for learning for every person, wherever they are and however old they happen to be. Globalization and the rise of new information technologies are some of the driving forces that cause depreciation of specialised competences. This happens very quickly in terms of economic value; consequently, workers of all skills levels, during their working life, must have the opportunity to update “their technical skills and enhance general skills to keep pace with continuous technological change and new job requirements” (Fahr, 2005, p. 75). It is in this context that LLL tops the policy agenda of international bodies, national governments and non-governmental organizations, in the field of education and training, to justify the need for LLL opportunities for the population as they face contemporary employability challenges. It is in this context that the requirement and interest to analyse the behaviour patterns of adult learners has developed over the last few years

Lifelong learning: case study of adult students in portuguese technological schools and higher education institutions

Paper to be presented in ESREA 2007 ADC Network Seminar - Changing Relationships between the State, Civil Society and the Citizen: Implications for adult education and adult learning, 14 -16 June 2007 - University of Minho - Campus de Gualtar, Braga (Portugal).

Learning in higher education: strategies to overcome challenges faced by adult students - lessons drawn from two studies in portugal

Int’l J. of Information and Communication Technology Education, 3(2), 1-14, April-June 2007

Survival of adult AIDS patients in a reference hospital of a metropolitan area in Brazil

OBJECTIVE: To evaluate the influence of sociodemographic, clinical, and epidemiological factors in AIDS patients survival in a reference hospital. METHODS: A sample of 502 adult AIDS patients out of 1,494 AIDS cases registered in a hospital in Fortaleza, Brazil, was investigated between 1986 and 1998. Sixteen cases were excluded due to death at the moment of the AIDS diagnosis and 486 were analyzed in the study. Socioeconomic and clinical epidemiological were the variables studied. Statistical analysis was conducted using the Kaplan-Meier survival analysis and the Cox proportional hazards model. RESULTS: Three hundred and sixty two out of the 486 patients studied took at least one antiretroviral drug and their survival was ten times longer than those who did not take any drug (746 and 79 days, respectively, p <0.001). Patients who took two nucleoside reverse transcriptase inhibitors (NRTI) plus protease inhibitor were found to have higher survival rates (p <0.001). The risk of dying in the first year was significantly lower for patients who took NRTI and a protease inhibitor compared to those who took only NRTI. In addition, this risk was much lower from the second year on (0.10; 95%CI: 0.42-0.23). The risk of dying in the first year was significantly higher for less educated patients (15.58; 95%CI: 6.64-36.58) and those who had two or more systemic diseases (3.03; 95%CI: 1.74-5.25). After the first year post-diagnosis, there was no risk difference for these factors. CONCLUSIONS: Higher education revealed to exert a significant influence in the first-year survival. Antiretroviral drugs had a greater impact in the survival from the second year on. A more aggressive antiretroviral therapy started earlier could benefit those patients.

Dynamic artificial neural network for electricity market prices forecast

This paper presents an artificial neural network applied to the forecasting of electricity market prices, with the special feature of being dynamic. The dynamism is verified at two different levels. The first level is characterized as a re-training of the network in every iteration, so that the artificial neural network can able to consider the most recent data at all times, and constantly adapt itself to the most recent happenings. The second level considers the adaptation of the neural network’s execution time depending on the circumstances of its use. The execution time adaptation is performed through the automatic adjustment of the amount of data considered for training the network. This is an advantageous and indispensable feature for this neural network’s integration in ALBidS (Adaptive Learning strategic Bidding System), a multi-agent system that has the purpose of providing decision support to the market negotiating players of MASCEM (Multi-Agent Simulator of Competitive Electricity Markets).

Short-term wind power forecasting in Portugal by neural networks and wavelet transform

This paper proposes artificial neural networks in combination with wavelet transform for short-term wind power forecasting in Portugal. The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Hence, good forecasting tools play a key role in tackling these challenges. Results from a real-world case study are presented. A comparison is carried out, taking into account the results obtained with other approaches. Finally, conclusions are duly drawn. (C) 2010 Elsevier Ltd. All rights reserved.

Symbolic Knowledge Extraction from Trained Neural Networks Governed by Lukasiewicz Logics

This work describes a methodology to extract symbolic rules from trained neural networks. In our approach, patterns on the network are codified using formulas on a Lukasiewicz logic. For this we take advantage of the fact that every connective in this multi-valued logic can be evaluated by a neuron in an artificial network having, by activation function the identity truncated to zero and one. This fact simplifies symbolic rule extraction and allows the easy injection of formulas into a network architecture. We trained this type of neural network using a back-propagation algorithm based on Levenderg-Marquardt algorithm, where in each learning iteration, we restricted the knowledge dissemination in the network structure. This makes the descriptive power of produced neural networks similar to the descriptive power of Lukasiewicz logic language, minimizing the information loss on the translation between connectionist and symbolic structures. To avoid redundance on the generated network, the method simplifies them in a pruning phase, using the "Optimal Brain Surgeon" algorithm. We tested this method on the task of finding the formula used on the generation of a given truth table. For real data tests, we selected the Mushrooms data set, available on the UCI Machine Learning Repository.

Rede neural artificial para detecção de sobremortalidade atribuível à cólera no Ceará

OBJETIVO: Avaliar as redes neurais recorrentes enquanto técnica preditiva para séries temporais em saúde. MÉTODOS: O estudo foi realizado durante uma epidemia de cólera ocorrida no Estado do Ceará, em 1993 e 1994, a partir da sobremortalidade tendo como causa básica as infecções intestinais mal definidas (CID-9). O número mensal de óbitos por essa causa, referente ao período de 1979 a 1995 no Estado do Ceará, foram obtidos do Sistema de Informação de Mortalidade (SIM) do Ministério da Saúde. Estruturou-se uma rede com dois neurônios na camada de entrada, 12 na camada oculta, um neurônio na camada de saída e um na camada de memória. Todas as funções de ativação eram a função logística. O treinamento foi realizado pelo método de backpropagation, com taxa de aprendizado de 0,01 e momentum de 0,9, com dados de janeiro de 1979 a junho de 1991. O critério para fim do treinamento foi atingir 22.000 epochs. Compararam-se os resultados com os de um modelo de regressão binomial negativa. RESULTADOS: A predição da rede neural a médio prazo foi adequada, em dezembro de 1993 e novembro e dezembro de 1994. O número de óbitos registrados foi superior ao limite do intervalo de confiança. Já o modelo regressivo detectou sobremortalidade a partir de março de 1992. CONCLUSÕES: A rede neural se mostrou capaz de predição, principalmente no início do período, como também ao detectar uma alteração concomitante e posterior à ocorrência da epidemia de cólera. No entanto, foi menos precisa do que o modelo de regressão binomial, que se mostrou mais sensível para detectar aberrações concomitantes à circulação da cólera.

Scheduling distributed energy resources in an isolated grid - an artificial neural network approach

Power Systems (PS), have been affected by substantial penetration of Distributed Generation (DG) and the operation in competitive environments. The future PS will have to deal with large-scale integration of DG and other distributed energy resources (DER), such as storage means, and provide to market agents the means to ensure a flexible and secure operation. Virtual power players (VPP) can aggregate a diversity of players, namely generators and consumers, and a diversity of energy resources, including electricity generation based on several technologies, storage and demand response. This paper proposes an artificial neural network (ANN) based methodology to support VPP resource schedule. The trained network is able to achieve good schedule results requiring modest computational means. A real data test case is presented.

FMRI 3D registration based on Fourier space subsets using neural networks

In this work, we present a neural network (NN) based method designed for 3D rigid-body registration of FMRI time series, which relies on a limited number of Fourier coefficients of the images to be aligned. These coefficients, which are comprised in a small cubic neighborhood located at the first octant of a 3D Fourier space (including the DC component), are then fed into six NN during the learning stage. Each NN yields the estimates of a registration parameter. The proposed method was assessed for 3D rigid-body transformations, using DC neighborhoods of different sizes. The mean absolute registration errors are of approximately 0.030 mm in translations and 0.030 deg in rotations, for the typical motion amplitudes encountered in FMRI studies. The construction of the training set and the learning stage are fast requiring, respectively, 90 s and 1 to 12 s, depending on the number of input and hidden units of the NN. We believe that NN-based approaches to the problem of FMRI registration can be of great interest in the future. For instance, NN relying on limited K-space data (possibly in navigation echoes) can be a valid solution to the problem of prospective (in frame) FMRI registration.

Efeito da fortificação alimentar com ácido fólico na prevalência de defeitos do tubo neural

OBJETIVO:Analisar o efeito de alimentos fortificados com ácido fólico na prevalência de defeitos de fechamento do tubo neural entre nascidos vivos. MÉTODOS: Estudo longitudinal de nascidos vivos do município de Recife (PE) entre 2000 e 2006. Os dados pesquisados foram obtidos do Sistema Nacional de Informações de Nascidos Vivos. Os defeitos de fechamento do tubo neural foram definidos de acordo com o Código Internacional de Doenças-10ª Revisão: anencefalia, encefalocele e espinha bífida. Compararam-se as prevalências nos períodos anterior (2000-2004) e posterior (2005-2006) ao período mandatório à fortificação. Analisou-se a tendência temporal das prevalências trimestrais de defeitos do fechamento do tubo neural pelos testes de Mann-Kendall e Sen's Slope. RESULTADOS: Não se identificou tendência de redução na ocorrência do desfecho (Teste de Mann-Kendall; p= 0,270; Sen's Slope =-0,008) no período estudado. Não houve diferença estatisticamente significativa entre as prevalências de defeitos do fechamento do tubo neural nos períodos anterior e posterior à fortificação dos alimentos com acido fólico de acordo com as características maternas. CONCLUSÕES: Embora não tenha sido observada redução dos defeitos do fechamento do tubo neural após o período mandatório de fortificação de alimentos com ácido fólico, os resultados encontrados não permitem descartar o seu benefício na prevenção desta malformação. São necessários estudos avaliando maior período e considerando o nível de consumo dos produtos fortificados pelas mulheres em idade fértil.

Neural-network approach to modeling liquid crystals in complex confinement

Finding the structure of a confined liquid crystal is a difficult task since both the density and order parameter profiles are nonuniform. Starting from a microscopic model and density-functional theory, one has to either (i) solve a nonlinear, integral Euler-Lagrange equation, or (ii) perform a direct multidimensional free energy minimization. The traditional implementations of both approaches are computationally expensive and plagued with convergence problems. Here, as an alternative, we introduce an unsupervised variant of the multilayer perceptron (MLP) artificial neural network for minimizing the free energy of a fluid of hard nonspherical particles confined between planar substrates of variable penetrability. We then test our algorithm by comparing its results for the structure (density-orientation profiles) and equilibrium free energy with those obtained by standard iterative solution of the Euler-Lagrange equations and with Monte Carlo simulation results. Very good agreement is found and the MLP method proves competitively fast, flexible, and refinable. Furthermore, it can be readily generalized to the richer experimental patterned-substrate geometries that are now experimentally realizable but very problematic to conventional theoretical treatments.