The school environment as a teacher. The role of space and place in enhancing ecological literacy and social competence development in children and young people

Can space and place foster child development, and in particular social competence and ecological literacy? If yes, how can space and place do that? This study shows that the answer to the first question is positive and then tries to explain the way space and place can make a difference. The thesis begins with the review of literature from different disciplines ��� child development and child psychology, education, environmental psychology, architecture and landscape architecture. Some bridges among such disciplines are created and in some cases the ideas from the different areas of research merge: thus, this is an interdisciplinary study. The interdisciplinary knowledge from these disciplines is translated into a range of design suggestions that can foster the development of social competence and ecological literacy. Using scientific knowledge from different disciplines is a way of introducing forms of evidence into the development of design criteria. However, the definition of design criteria also has to pass through the study of a series of school buildings and un-built projects: case studies can give a positive contribution to the criteria because examples and good practices can help ���translating��� the theoretical knowledge into design ideas and illustrations. To do that, the different case studies have to be assessed in relation to the various themes that emerged in the literature review. Finally, research by design can be used to help define the illustrated design criteria: based on all the background knowledge that has been built, the role of the architect is to provide a series of different design solutions that can give answers to the different ���questions��� emerged in the literature review.

Competence: intelligence in sheep's clothing? Culture, representations, and cognitive performance

The first aim of this thesis was to contribute to the understanding of how cultural capital (Bourdieu, 1983/1986) affects students achievements and performances. We specifically claimed that the effect of cultural capital is at least partly explained by the positioning students take towards the principles they use to attribute competence and intelligence. The testing of these hypothesis have been framed within the social representations theory, specifically in the formulation of the Lemanic school approach (Doise, 1986).

Model selection and the vectorial misspecification-resistant information criterion in multivariate time series

The thesis deals with the problem of Model Selection (MS) motivated by information and prediction theory, focusing on parametric time series (TS) models. The main contribution of the thesis is the extension to the multivariate case of the Misspecification-Resistant Information Criterion (MRIC), a criterion introduced recently that solves Akaike���s original research problem posed 50 years ago, which led to the definition of the AIC. The importance of MS is witnessed by the huge amount of literature devoted to it and published in scientific journals of many different disciplines. Despite such a widespread treatment, the contributions that adopt a mathematically rigorous approach are not so numerous and one of the aims of this project is to review and assess them. Chapter 2 discusses methodological aspects of MS from information theory. Information criteria (IC) for the i.i.d. setting are surveyed along with their asymptotic properties; and the cases of small samples, misspecification, further estimators. Chapter 3 surveys criteria for TS. IC and prediction criteria are considered for: univariate models (AR, ARMA) in the time and frequency domain, parametric multivariate (VARMA, VAR); nonparametric nonlinear (NAR); and high-dimensional models. The MRIC answers Akaike���s original question on efficient criteria, for possibly-misspecified (PM) univariate TS models in multi-step prediction with high-dimensional data and nonlinear models. Chapter 4 extends the MRIC to PM multivariate TS models for multi-step prediction introducing the Vectorial MRIC (VMRIC). We show that the VMRIC is asymptotically efficient by proving the decomposition of the MSPE matrix and the consistency of its Method-of-Moments Estimator (MoME), for Least Squares multi-step prediction with univariate regressor. Chapter 5 extends the VMRIC to the general multiple regressor case, by showing that the MSPE matrix decomposition holds, obtaining consistency for its MoME, and proving its efficiency. The chapter concludes with a digression on the conditions for PM VARX models.