In.sentar a criatividade : um estudo sobre aplicação do modelo de resolução criativa de problemas no ensino artístico

Relatório da Prática de Ensino Supervisionada, Ensino de Artes Visuais, Universidade de Lisboa, 2013

Unpacking the Link Between Entrepreneurialism and Employability: an Assessment of the Relationship Between Entrepreneurial Attitudes and Likelihood of Graduate Employment in a Professional Field

Purpose This research investigates the relationship between students’ entrepreneurial attitudes and traits and their classification of employment six months after university graduation. It aims to identify what specific attitudes and traits of entrepreneurial graduates are linked to employability in a professional or managerial field. Design/Methodology The research adopts a quantitative approach to measure the entrepreneurial drive of final-year undergraduate business school students and regresses this measurement against the employment level of the same students six months after their graduation. The employment classification of each respondent was classified as ‘professional/managerial’ or ‘non-professional/non-managerial’, in line with the Standard Occupational Classification (SOC) 2010. Findings The research found that both proactive disposition and achievement motivation were statistically linked to the likelihood of graduates being employed in a professional or managerial position six months after graduation. Originality/Value This research goes beyond existing literature linking entrepreneurship to employability to quantitatively examine what specific attitudes and traits can be linked to employability in recent graduates. By identifying the aspects of entrepreneurialism that have a relationship with employability, more information is available for educators who are designing entrepreneurial education programs and allows for greater focus on aspects that may be of greatest benefit to all students.

How Learning Styles, Chemistry Attitudes and Experiences, Confidence, and Demographics Correlate with Academic Success in First and Second Year Chemistry Courses

At Brock University, the Faculty of Mathematics and Science currently has one of the highest percentages of students on academic probation, with many students reporting the most difficulty with Introductory Chemistry in first year and Organic Chemistry in second year. To identify strategies to improve students' performance and reduce the number of students on academic probation, a multi-year research project was undertaken involving several chemistry courses. Students were asked to complete three questionnaires, and provide consent to obtain their final Chemistry grade from the Registrar's Office. Research began at the end of the 2007-08 academic year with CHEM IPOO, and in the 2008-09 academic year, students in the larger CHEM IF92 Introductory Chemistry course were invited to participate in this research near the beginning of the academic year. Students who went on to take second year Organic and Analytical Chemistry were asked to complete these questionnaires in each second year course. The three questionnaires included the Kolb Learning Styles Inventory (Kolb, 1984) modified to include specific reference to Chemistry in each question, Dalgety, ColI, and Jones' (2002) Chemistry Attitudes and Experiences Questionnaire (CAEQ), and lastly, a demographic survey. Correlations were found between learning style and academic success; concrete learners were not as successful as abstract learners. Differences were noted between females and males with respect to learning styles, academic success, and confidence. Several differences were also noted between those who are the First in the Family to attend university and those who are not First in the Family to attend university.

Estimating the Tobit Model with Serial Correlation: an Operational Approach

Several Authors Have Discussed Recently the Limited Dependent Variable Regression Model with Serial Correlation Between Residuals. the Pseudo-Maximum Likelihood Estimators Obtained by Ignoring Serial Correlation Altogether, Have Been Shown to Be Consistent. We Present Alternative Pseudo-Maximum Likelihood Estimators Which Are Obtained by Ignoring Serial Correlation Only Selectively. Monte Carlo Experiments on a Model with First Order Serial Correlation Suggest That Our Alternative Estimators Have Substantially Lower Mean-Squared Errors in Medium Size and Small Samples, Especially When the Serial Correlation Coefficient Is High. the Same Experiments Also Suggest That the True Level of the Confidence Intervals Established with Our Estimators by Assuming Asymptotic Normality, Is Somewhat Lower Than the Intended Level. Although the Paper Focuses on Models with Only First Order Serial Correlation, the Generalization of the Proposed Approach to Serial Correlation of Higher Order Is Also Discussed Briefly.

Attitudes and behaviour towards food attributes and organic food – a triangular methodological investigation of occasional organic consumers

Organic food is increasingly available in the conventional food retail, where organic products are offered alongside with various other types of products and compete mainly with conventional and the so-called conventional-plus products. The latter are conventional products displaying particular quality attributes on the product packaging, such as ‘no artificial additives’, or ‘from animal welfare husbandry’. Often, these quality attributes also apply to organic products. Occasional organic consumers might prefer such conventional-plus alternatives that are perceived to be ‘between’ organic and conventional products. The overall objective of this PhD thesis was to provide information about the segment of occasional organic consumers. In particular, the thesis focussed on consumer perceptions and attitudes towards the quality of, and preferences for, organic, conventional and conventional-plus products in two countries: Germany and Switzerland. To achieve these objectives, qualitative and quantitative consumer research was combined in order to explore occasional organic consumers’ perceptions and attitudes as well as to observe their preferences and buying behaviour regarding different types of food products: organic, conventional and conventional-plus products. The qualitative research showed that, depending on single criteria, organic production was both positively as well as negatively assessed by consumers. Consumer perception of organic food was found to be highly selective and primarily focussed on the final stage of the particular production process. A major problem is that consumers are still mostly unfamiliar with factors associated with organic production, have a lack of confidence, and often confuse organic with conventional products. Besides this, consumer expectations of organic products are different from the expectations of conventional products. The quantitative research revealed that attitudes strongly determine consumers’ preferences for organic, conventional and conventional-plus products. Consumer attitudes tended to differ more between organic and conventional choices rather than conventional-plus and conventional choices. Furthermore, occasional organic consumers are heterogeneous in their preferences. They can be grouped into two segments: the consumers in one segment were less price sensitive and preferred organic products. The consumers in the other segment were more price sensitive and rather preferred conventional-plus or conventional products. To conclude, given the selective and subjective nature of consumer perception and the strong focus of consumer perception on the final stage of the food production process, specific additional values of organic farming should be communicated in clear and catchy messages. At the same time, these messages should be particularly focussed on the final stage of organic food production. The communication of specific added values in relation with organic products to improve the perceived price-performance-ratio is important since conventional-plus products represent an interesting alternative particularly for price sensitive occasional organic consumers. Besides this, it is important to strengthen affirmative consumer attitudes towards organic production. Therefore, policy support should emphasise on long-term communication campaigns and education programmes to increase the consumer awareness and knowledge of organic food and farming. Since consumers expect that organic food is regionally or at least domestically produced while they less accept organic imports, policy support of domestic and regional producers is a crucial measure to fill the current gap between the increasing consumer demand of organic food and the stagnation of the domestic and regional organic food supply.

Analyzing the persuasion context of the persuasive systems design model with the 3D-RAB model

Research into design methodology is one of the most challenging issues in the field of persuasive technology. However, the introduction of the Persuasive Systems Design model, and the consideration of the 3-Dimensional Re-lationship between Attitude and Behavior, offer to make persuasive technolo-gies more practically viable. In this paper we demonstrate how the 3-Dimensional Relationship between Attitude and Behavior guides the analysis of the persuasion context in the Persuasive System Design model. As a result, we propose a modification of the persuasion context and assert that the technology should be analyzed as part of strategy instead of event.

Likelihood-free estimation of model evidence

Statistical methods of inference typically require the likelihood function to be computable in a reasonable amount of time. The class of “likelihood-free” methods termed Approximate Bayesian Computation (ABC) is able to eliminate this requirement, replacing the evaluation of the likelihood with simulation from it. Likelihood-free methods have gained in efficiency and popularity in the past few years, following their integration with Markov Chain Monte Carlo (MCMC) and Sequential Monte Carlo (SMC) in order to better explore the parameter space. They have been applied primarily to estimating the parameters of a given model, but can also be used to compare models. Here we present novel likelihood-free approaches to model comparison, based upon the independent estimation of the evidence of each model under study. Key advantages of these approaches over previous techniques are that they allow the exploitation of MCMC or SMC algorithms for exploring the parameter space, and that they do not require a sampler able to mix between models. We validate the proposed methods using a simple exponential family problem before providing a realistic problem from human population genetics: the comparison of different demographic models based upon genetic data from the Y chromosome.

Improved maximum likelihood estimators in a heteroskedastic errors-in-variables model

This paper develops a bias correction scheme for a multivariate heteroskedastic errors-in-variables model. The applicability of this model is justified in areas such as astrophysics, epidemiology and analytical chemistry, where the variables are subject to measurement errors and the variances vary with the observations. We conduct Monte Carlo simulations to investigate the performance of the corrected estimators. The numerical results show that the bias correction scheme yields nearly unbiased estimates. We also give an application to a real data set.

Improved maximum-likelihood estimation in a regression model with general parametrization

We analyse the finite-sample behaviour of two second-order bias-corrected alternatives to the maximum-likelihood estimator of the parameters in a multivariate normal regression model with general parametrization proposed by Patriota and Lemonte [A. G. Patriota and A. J. Lemonte, Bias correction in a multivariate regression model with genereal parameterization, Stat. Prob. Lett. 79 (2009), pp. 1655-1662]. The two finite-sample corrections we consider are the conventional second-order bias-corrected estimator and the bootstrap bias correction. We present the numerical results comparing the performance of these estimators. Our results reveal that analytical bias correction outperforms numerical bias corrections obtained from bootstrapping schemes.