Responding to Changing Student Demographics in Finland: A Study of Teachers’ Developing Cultural Competence

The aim of this dissertation was to examine the skills and knowledge that pre-service teachers and teachers have and need about working with multilingual and multicultural students from immigrant backgrounds. The specific goals were to identify pre-service teachers’ and practising teachers’ current knowledge and awareness of culturally and linguistically responsive teaching, identify a profile of their strengths and needs, and devise appropriate professional development support and ways to prepare teachers to become equitable culturally responsive practitioners. To investigate these issues, the dissertation reports on six original empirical studies within two groups of teachers: international pre-service teacher education students from over 25 different countries as well as pre-service and practising Finnish teachers. The international pre-service teacher sample consisted of (n = 38, study I; and n = 45, studies II-IV) and the pre-service and practising Finnish teachers sample encompassed (n = 89, study V; and n = 380, study VI). The data used were multi-source including both qualitative (students’ written work from the course including journals, final reflections, pre- and post-definition of key terms, as well as course evaluation and focus group transcripts) and quantitative (multi-item questionnaires with open-ended options), which enhanced the credibility of the findings resulting in the triangulation of data. Cluster analytic procedures, multivariate analysis of variance (MANOVA), and qualitative analyses mostly Constant Comparative Approach were used to understand pre-service teachers’ and practising teachers’ developing cultural understandings. The results revealed that the mainly white / mainstream teacher candidates in teacher education programmes bring limited background experiences, prior socialisation, and skills about diversity. Taking a multicultural education course where identity development was a focus, positively influenced teacher candidates’ knowledge and attitudes toward diversity. The results revealed approaches and strategies that matter most in preparing teachers for culturally responsive teaching, including but not exclusively, small group activities and discussions, critical reflection, and field immersion. This suggests that there are already some tools to address the need for the support needed to teach successfully a diversity of pupils and provide in-service training for those already practising the teaching profession. The results provide insight into aspects of teachers’ knowledge about both the linguistic and cultural needs of their students, as well as what constitutes a repertoire of approaches and strategies to assure students’ academic success. Teachers’ knowledge of diversity can be categorised into sound awareness, average awareness, and low awareness. Knowledge of diversity was important in teachers’ abilities to use students’ language and culture to enhance acquisition of academic content, work effectively with multilingual learners’ parents/guardians, learn about the cultural backgrounds of multilingual learners, link multilingual learners’ prior knowledge and experience to instruction, and modify classroom instruction for multilingual learners. These findings support the development of a competency based model and can be used to frame the studies of pre-service teachers, as well as the professional development of practising teachers in increasingly diverse contexts. The present set of studies take on new significance in the current context of increasing waves of migration to Europe in general and Finland in particular. They suggest that teacher education programmes can equip teachers with the necessary attitudes, skills, and knowledge to enable them work effectively with students from different ethnic and language backgrounds as they enter the teaching profession. The findings also help to refine the tools and approaches to measuring the competencies of teachers teaching in mainstream classrooms and candidates in preparation.

Social capital and health : variations, associations and challenges

Although social capital and health have been extensively studied during the last decade, there are still open issues in current empirical research. These concern for instance the measurement of the concept in different contexts, as well as the association between different types of social capital and different dimensions of health. The present thesis addressed these questions. The general aim was to promote the understanding of social capital and health by investigating the oldest old and the two major language groups in Finland, Swedish- and Finnish-speakers. Another aim was to contribute to the discussion on methodological issues in social capital and health research. The present thesis investigated two empirical data sets, Umeå 85+ and Health 2000. The Umeå 85+ study was a cross-sectional study of 163 individuals aged 85, 90, and 95 or older, living in the municipality of Umeå, Sweden, in the year of 2000. The Health 2000 survey was a national study of 8,028 persons aged 30 or above carried out in Finland in 2000-2001. Different indicators of structural (e.g. social contacts) and cognitive (e.g. trust) social capital, as well as health indicators were used as variables in the analyses. The Umeå 85+ data set was analyzed with factor analysis, as well as univariate and multivariate analysis of variance. The Health 2000 data was analyzed with logistic regression techniques. The results showed that the Swedish-speakers in the Finnish data set Health 2000 had consistently higher prevalence of social capital compared to the Finnish-speakers even after controlling for central sociodemographic variables. The results further showed that even if the language group differences in health were small, the Swedishspeakers experienced in general better self-reported health compared with the Finnish-speakers. Common sociodemographic variables could not explain these observed differences in health. The results imply that social capital is often, but not always, associated with health. This was clearly seen in the Umeå 85+ data set where only one health indicator (depressive symptoms) was associated with structural social capital among the oldest old. The results based on the analysis of the Health 2000 survey demonstrated that the cognitive component of social capital was associated with self-rated health and psychological health rather than with participation in social activities and social contacts. In addition, social capital statistically reduced the health advantage especially for Swedish-speaking men, indicating that high prevalence of social capital may promote health. Finally, the present thesis also discussed the issue of methodological challenges faced with when analyzing social capital and health. It was suggested that certain components of social capital such as bonding and bridging social capital may be more relevant than structural and cognitive components when investigating social capital among the two language groups in Finland. The results concerning the oldest old indicated that the structural aspects of social capital probably reflect current living conditions, whereas cognitive social capital reflects attitudes and traits often acquired decades earlier. This is interpreted as an indication of the fact that structural and cognitive social capital are closely related yet empirically two distinctive concepts. Taken together, some components of social capital may be more relevant to study than others depending on which population group and age group is under study. The results also implied that the choice of cut-off point of dichotomization of selfrated health has an impact on the estimated effects of the explanatory variables. When the whole age interval, 35-64 years, was analyzed with logistic regression techniques the choice of cut-off point did not matter for the estimated effects of marital status and educational level. The results changed, however, when the age interval was divided into three shorter intervals. If self-rated health is explored using wide age intervals that do not account for age-dependent covariates there is a risk of drawing misleading conclusions. In conclusion, the results presented in the thesis suggest that the uneven distribution of social capital observed between the two language groups in Finland are of importance when trying to further understand health inequalities that exist between Swedish- and Finnish-speakers in Finland. Although social capital seemed to be relevant to the understanding of health among the oldest old, the meaning of social capital is probably different compared to a less vulnerable age group. This should be noticed in future empirical research. In the present thesis, it was shown that the relationship between social capital and health is complex and multidimensional. Different aspects of social capital seem to be important for different aspects of health. This reduces the possibility to generalize the results and to recommend general policy implementations in this area. An increased methodological awareness regarding social capital as well as health are called for in order to further understand the cfomplex association between them. However, based on the present data and findings social capital is associated with health. To understand individual health one must also consider social aspects of the individuals’ environment such as social capital.

Statistical segmentation methods and color variance analysis of retinal images

In this research, the effectiveness of Naive Bayes and Gaussian Mixture Models classifiers on segmenting exudates in retinal images is studied and the results are evaluated with metrics commonly used in medical imaging. Also, a color variation analysis of retinal images is carried out to find how effectively can retinal images be segmented using only the color information of the pixels.

Reconstruction and analysis of surface variation using photometric stereo

In many industrial applications, accurate and fast surface reconstruction is essential for quality control. Variation in surface finishing parameters, such as surface roughness, can reflect defects in a manufacturing process, non-optimal product operational efficiency, and reduced life expectancy of the product. This thesis considers reconstruction and analysis of high-frequency variation, that is roughness, on planar surfaces. Standard roughness measures in industry are calculated from surface topography. A fast and non-contact method to obtain surface topography is to apply photometric stereo in the estimation of surface gradients and to reconstruct the surface by integrating the gradient fields. Alternatively, visual methods, such as statistical measures, fractal dimension and distance transforms, can be used to characterize surface roughness directly from gray-scale images. In this thesis, the accuracy of distance transforms, statistical measures, and fractal dimension are evaluated in the estimation of surface roughness from gray-scale images and topographies. The results are contrasted to standard industry roughness measures. In distance transforms, the key idea is that distance values calculated along a highly varying surface are greater than distances calculated along a smoother surface. Statistical measures and fractal dimension are common surface roughness measures. In the experiments, skewness and variance of brightness distribution, fractal dimension, and distance transforms exhibited strong linear correlations to standard industry roughness measures. One of the key strengths of photometric stereo method is the acquisition of higher frequency variation of surfaces. In this thesis, the reconstruction of planar high-frequency varying surfaces is studied in the presence of imaging noise and blur. Two Wiener filterbased methods are proposed of which one is optimal in the sense of surface power spectral density given the spectral properties of the imaging noise and blur. Experiments show that the proposed methods preserve the inherent high-frequency variation in the reconstructed surfaces, whereas traditional reconstruction methods typically handle incorrect measurements by smoothing, which dampens the high-frequency variation.

Analysis of Satellite Data for Forest Segmentation

In this work we study the classification of forest types using mathematics based image analysis on satellite data. We are interested in improving classification of forest segments when a combination of information from two or more different satellites is used. The experimental part is based on real satellite data originating from Canada. This thesis gives summary of the mathematics basics of the image analysis and supervised learning , methods that are used in the classification algorithm. Three data sets and four feature sets were investigated in this thesis. The considered feature sets were 1) histograms (quantiles) 2) variance 3) skewness and 4) kurtosis. Good overall performances were achieved when a combination of ASTERBAND and RADARSAT2 data sets was used.

Analysis of patterns in mining time series using Qlucore

Raw measurement data does not always immediately convey useful information, but applying mathematical statistical analysis tools into measurement data can improve the situation. Data analysis can offer benefits like acquiring meaningful insight from the dataset, basing critical decisions on the findings, and ruling out human bias through proper statistical treatment. In this thesis we analyze data from an industrial mineral processing plant with the aim of studying the possibility of forecasting the quality of the final product, given by one variable, with a model based on the other variables. For the study mathematical tools like Qlucore Omics Explorer (QOE) and Sparse Bayesian regression (SB) are used. Later on, linear regression is used to build a model based on a subset of variables that seem to have most significant weights in the SB model. The results obtained from QOE show that the variable representing the desired final product does not correlate with other variables. For SB and linear regression, the results show that both SB and linear regression models built on 1-day averaged data seriously underestimate the variance of true data, whereas the two models built on 1-month averaged data are reliable and able to explain a larger proportion of variability in the available data, making them suitable for prediction purposes. However, it is concluded that no single model can fit well the whole available dataset and therefore, it is proposed for future work to make piecewise non linear regression models if the same available dataset is used, or the plant to provide another dataset that should be collected in a more systematic fashion than the present data for further analysis.

Phylogenetic Analysis of Avidins and Expression of Novel Avidins from Lactrodectus hesperus and Hoeflea phototrophica

Avidins (Avds) are homotetrameric or homodimeric glycoproteins with typically less than 130 amino acid residues per monomer. They form a highly stable, non-covalent complex with biotin (vitamin H) with Kd = 10-15 M (for chicken Avd). The best-studied Avds are the chicken Avd from Gallus gallus and streptavidin from Streptomyces avidinii, although other Avd studies have also included Avds from various origins, e.g., from frogs, fishes, mushrooms and from many different bacteria. Several engineered Avds have been reported as well, e.g., dual-chain Avds (dcAvds) and single-chain Avds (scAvds), circular permutants with up to four simultaneously modifiable ligand-binding sites. These engineered Avds along with the many native Avds have potential to be used in various nanobiotechnological applications. In this study, we made a structure-based alignment representing all currently available sequences of Avds and studied the evolutionary relationship of Avds using phylogenetic analysis. First, we created an initial multiple sequence alignment of Avds using 42 closely related sequences, guided by the known Avd crystal structures. Next, we searched for non-redundant Avd sequences from various online databases, including National Centre for Biotechnology Information and the Universal Protein Resource; the identified sequences were added to the initial alignment to expand it to a final alignment of 242 Avd sequences. The MEGA software package was used to create distance matrices and a phylogenetic tree. Bootstrap reproducibility of the tree was poor at multiple nodes and may reflect on several possible issues with the data: the sequence length compared is relatively short and, whereas some positions are highly conserved and functional, others can vary without impinging on the structure or the function, so there are few informative sites; it may be that periods of rapid duplication have led to paralogs and that the differences among them are within the error limit of the data; and there may be other yet unknown reasons. Principle component analysis applied to alternative distance data did segregate the major groups, and success is likely due to the multivariate consideration of all the information. Furthermore, based on our extensive alignment and phylogenetic analysis, we expressed two novel Avds, lacavidin from Lactrodectus Hesperus, a western black widow spider, and hoefavidin from Hoeflea phototrophica, an aerobic marine bacterium, the ultimate aim being to determine their X-ray structures. These Avds were selected because of their unique sequences: lacavidin has an N-terminal Avd-like domain but a long C-terminal overhang, whereas hoefavidin was thought to be a dimeric Avd. Both these Avds could be used as novel scaffolds in biotechnological applications.

Spectral Analysis of Symmetric and Anti-Symmetric Pairwise Kernels

This work investigates theoretical properties of symmetric and anti-symmetric kernels. First chapters give an overview of the theory of kernels used in supervised machine learning. Central focus is on the regularized least squares algorithm, which is motivated as a problem of function reconstruction through an abstract inverse problem. Brief review of reproducing kernel Hilbert spaces shows how kernels define an implicit hypothesis space with multiple equivalent characterizations and how this space may be modified by incorporating prior knowledge. Mathematical results of the abstract inverse problem, in particular spectral properties, pseudoinverse and regularization are recollected and then specialized to kernels. Symmetric and anti-symmetric kernels are applied in relation learning problems which incorporate prior knowledge that the relation is symmetric or anti-symmetric, respectively. Theoretical properties of these kernels are proved in a draft this thesis is based on and comprehensively referenced here. These proofs show that these kernels can be guaranteed to learn only symmetric or anti-symmetric relations, and they can learn any relations relative to the original kernel modified to learn only symmetric or anti-symmetric parts. Further results prove spectral properties of these kernels, central result being a simple inequality for the the trace of the estimator, also called the effective dimension. This quantity is used in learning bounds to guarantee smaller variance.

PERFORMANCE ANALYSIS OF A TACTICAL ASSET ALLOCATION – Evidence from the Black–Litterman model based fixed income portfolio

Over time the demand for quantitative portfolio management has increased among financial institutions but there is still a lack of practical tools. In 2008 EDHEC Risk and Asset Management Research Centre conducted a survey of European investment practices. It revealed that the majority of asset or fund management companies, pension funds and institutional investors do not use more sophisticated models to compensate the flaws of the Markowitz mean-variance portfolio optimization. Furthermore, tactical asset allocation managers employ a variety of methods to estimate return and risk of assets, but also need sophisticated portfolio management models to outperform their benchmarks. Recent development in portfolio management suggests that new innovations are slowly gaining ground, but still need to be studied carefully. This thesis tries to provide a practical tactical asset allocation (TAA) application to the Black–Litterman (B–L) approach and unbiased evaluation of B–L models’ qualities. Mean-variance framework, issues related to asset allocation decisions and return forecasting are examined carefully to uncover issues effecting active portfolio management. European fixed income data is employed in an empirical study that tries to reveal whether a B–L model based TAA portfolio is able outperform its strategic benchmark. The tactical asset allocation utilizes Vector Autoregressive (VAR) model to create return forecasts from lagged values of asset classes as well as economic variables. Sample data (31.12.1999–31.12.2012) is divided into two. In-sample data is used for calibrating a strategic portfolio and the out-of-sample period is for testing the tactical portfolio against the strategic benchmark. Results show that B–L model based tactical asset allocation outperforms the benchmark portfolio in terms of risk-adjusted return and mean excess return. The VAR-model is able to pick up the change in investor sentiment and the B–L model adjusts portfolio weights in a controlled manner. TAA portfolio shows promise especially in moderately shifting allocation to more risky assets while market is turning bullish, but without overweighting investments with high beta. Based on findings in thesis, Black–Litterman model offers a good platform for active asset managers to quantify their views on investments and implement their strategies. B–L model shows potential and offers interesting research avenues. However, success of tactical asset allocation is still highly dependent on the quality of input estimates.