Stories and Statistics: Describing a Mixed Method Study of Effective Classroom Practice

The Effective Classroom Practice project aimed to identify key factors that contribute to effective teaching in primary and secondary phases of schooling in different socioeconomic contexts. This article addresses the ways in which qualitative and quantitative approaches were combined within an integrated design to provide a comprehensive methodology for the research purposes. Strategies for the study are discussed, followed by the challenges of combining complex statistics with individual stories, particularly in relation to the ongoing iteration between these different data sets, and issues of validity and reliability. The findings shed new light on the meanings and measurement of teachers’ effective classroom practice and the complex nature of, and relationships with, professional life phase, teacher identities, and school context.

Every One, Every Day

Creative Statement: “There are those who see Planet Earth as a gigantic living being, one that feeds and nurtures humanity and myriad other species – an entity that must be cared for. Then there are those who see it as a rock full of riches to be pilfered heedlessly in a short-term quest for over-abundance. This ‘cradle to grave’ mentality, it would seem, is taking its toll (unless you’re a virulent disbeliever in climate change). Why not, ask artists Priscilla Bracks and Gavin Sade, take a different approach? To this end they have set out on a near impossible task; to visualise the staggering quantity of carbon produced by Australia every year. Their eerie, glowing plastic cube resembles something straight out of Dr Who or The X Files. And, like the best science fiction, it has technical realities at its heart. Every One, Every Day tangibly illustrates our greenhouse gas output – its 27m3 volume is approximately the amount of green-house gas emitted per capita, daily. Every One, Every Dayis lit by an array of LED’s displaying light patterns representing energy use generated by data from the Australian Energy Market. Every One, Every Day was formed from recycled, polyethylene – used milk bottles – ‘lent’ to the artists by a Visy recycling facility. At the end of the Vivid Festival this plastic will be returned to Visy, where it will re-enter the stream of ‘technical nutrients.’ Could we make another world? One that emulates the continuing cycles of nature? One that uses our ‘technical nutrients’ such as plastic and steel in continual cycles, just like a deciduous tree dropping leaves to compost itself and keep it’s roots warm and moist?” (Ashleigh Crawford. Melbourne – April, 2013) Artistic Research Statement: The research focus of this work is on exploring how to represent complex statistics and data at a human scale, and how produce a work where a large percentage of the materials could be recycled. The surface of Every One, Every Day is clad in tiles made from polyethylene, from primarily recycled milk bottles, ‘lent’ to the artists by the Visy recycling facility in Sydney. The tiles will be returned to Visy for recycling. As such the work can be viewed as an intervention in the industrial ecology of polyethylene, and in the process demonstrates how to sustain cycles of technical materials – by taking the output of a recycling facility back to a manufacturer to produce usable materials. In terms of data visualisation, Every One, Every Day takes the form of a cube with a volume of 27 cubic meters. The annual per capita emissions figures for Australia are cited as ranging between 18 to 25 tons. Assuming the lower figure, 18tons per capital annually, the 27 cubic meters represents approximately one day per capita of CO2 emissions – where CO2 is a gas at 15C and 1 atmosphere of pressure. The work also explores real time data visualisation by using an array of 600 controllable LEDs inside the cube. Illumination patterns are derived from a real time data from the Australian Energy Market, using the dispatch interval price and demand graph for New South Wales. The two variables of demand and price are mapped to properties of the illumination - hue, brightness, movement, frequency etc. The research underpinning the project spanned industrial ecology to data visualization and public art practices. The result is that Every One, Every Day is one of the first public artworks that successfully bring together materials, physical form, and real time data representation in a unified whole.

Coupled symplectic maps as models for subdiffusive processes in disordered Hamiltonian lattices

Acknowledgements One of us (T. B.) acknowledges many interesting discussions on coupled maps with Professor C. Tsallis. We are also grateful to the anonymous referees for their constructive feedback that helped us improve the manuscript and to the HPCS Laboratory of the TEI of Western Greece for providing the computer facilities where all our simulations were performed. C. G. A. was partially supported by the “EPSRC EP/I032606/1” grant of the University of Aberdeen. This research has been co-financed by the European Union (European Social Fund - ESF) and Greek national funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF) - Research Funding Program: THALES - Investing in knowledge society through the European Social Fund.

Coupled symplectic maps as models for subdiffusive processes in disordered Hamiltonian lattices

Acknowledgements One of us (T. B.) acknowledges many interesting discussions on coupled maps with Professor C. Tsallis. We are also grateful to the anonymous referees for their constructive feedback that helped us improve the manuscript and to the HPCS Laboratory of the TEI of Western Greece for providing the computer facilities where all our simulations were performed. C. G. A. was partially supported by the “EPSRC EP/I032606/1” grant of the University of Aberdeen. This research has been co-financed by the European Union (European Social Fund - ESF) and Greek national funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF) - Research Funding Program: THALES - Investing in knowledge society through the European Social Fund.

Statistics and kinetics of single-molecule electron transfer dynamics in complex environments: A simulation model study

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Predicting class I major histocompatibility complex (MHC) binders using multivariate statistics:comparison of discriminant analysis and multiple linear regression

The accurate in silico identification of T-cell epitopes is a critical step in the development of peptide-based vaccines, reagents, and diagnostics. It has a direct impact on the success of subsequent experimental work. Epitopes arise as a consequence of complex proteolytic processing within the cell. Prior to being recognized by T cells, an epitope is presented on the cell surface as a complex with a major histocompatibility complex (MHC) protein. A prerequisite therefore for T-cell recognition is that an epitope is also a good MHC binder. Thus, T-cell epitope prediction overlaps strongly with the prediction of MHC binding. In the present study, we compare discriminant analysis and multiple linear regression as algorithmic engines for the definition of quantitative matrices for binding affinity prediction. We apply these methods to peptides which bind the well-studied human MHC allele HLA-A*0201. A matrix which results from combining results of the two methods proved powerfully predictive under cross-validation. The new matrix was also tested on an external set of 160 binders to HLA-A*0201; it was able to recognize 135 (84%) of them.

Bayesian methodology for genetics of complex diseases

Genetic research of complex diseases is a challenging, but exciting, area of research. The early development of the research was limited, however, until the completion of the Human Genome and HapMap projects, along with the reduction in the cost of genotyping, which paves the way for understanding the genetic composition of complex diseases. In this thesis, we focus on the statistical methods for two aspects of genetic research: phenotype definition for diseases with complex etiology and methods for identifying potentially associated Single Nucleotide Polymorphisms (SNPs) and SNP-SNP interactions. With regard to phenotype definition for diseases with complex etiology, we firstly investigated the effects of different statistical phenotyping approaches on the subsequent analysis. In light of the findings, and the difficulties in validating the estimated phenotype, we proposed two different methods for reconciling phenotypes of different models using Bayesian model averaging as a coherent mechanism for accounting for model uncertainty. In the second part of the thesis, the focus is turned to the methods for identifying associated SNPs and SNP interactions. We review the use of Bayesian logistic regression with variable selection for SNP identification and extended the model for detecting the interaction effects for population based case-control studies. In this part of study, we also develop a machine learning algorithm to cope with the large scale data analysis, namely modified Logic Regression with Genetic Program (MLR-GEP), which is then compared with the Bayesian model, Random Forests and other variants of logic regression.

Bayesian mixtures for modelling complex medical data : a case study in Parkinson’s disease

Mixture models are a flexible tool for unsupervised clustering that have found popularity in a vast array of research areas. In studies of medicine, the use of mixtures holds the potential to greatly enhance our understanding of patient responses through the identification of clinically meaningful clusters that, given the complexity of many data sources, may otherwise by intangible. Furthermore, when developed in the Bayesian framework, mixture models provide a natural means for capturing and propagating uncertainty in different aspects of a clustering solution, arguably resulting in richer analyses of the population under study. This thesis aims to investigate the use of Bayesian mixture models in analysing varied and detailed sources of patient information collected in the study of complex disease. The first aim of this thesis is to showcase the flexibility of mixture models in modelling markedly different types of data. In particular, we examine three common variants on the mixture model, namely, finite mixtures, Dirichlet Process mixtures and hidden Markov models. Beyond the development and application of these models to different sources of data, this thesis also focuses on modelling different aspects relating to uncertainty in clustering. Examples of clustering uncertainty considered are uncertainty in a patient’s true cluster membership and accounting for uncertainty in the true number of clusters present. Finally, this thesis aims to address and propose solutions to the task of comparing clustering solutions, whether this be comparing patients or observations assigned to different subgroups or comparing clustering solutions over multiple datasets. To address these aims, we consider a case study in Parkinson’s disease (PD), a complex and commonly diagnosed neurodegenerative disorder. In particular, two commonly collected sources of patient information are considered. The first source of data are on symptoms associated with PD, recorded using the Unified Parkinson’s Disease Rating Scale (UPDRS) and constitutes the first half of this thesis. The second half of this thesis is dedicated to the analysis of microelectrode recordings collected during Deep Brain Stimulation (DBS), a popular palliative treatment for advanced PD. Analysis of this second source of data centers on the problems of unsupervised detection and sorting of action potentials or "spikes" in recordings of multiple cell activity, providing valuable information on real time neural activity in the brain.

Factors affecting timely completion of a PhD : a complex systems approach

Completing a PhD on time is a complex process, influenced by many interacting factors. In this paper we take a Bayesian Network approach to analyzing the factors perceived to be important in achieving this aim. Focusing on a single research group in Mathematical Sciences, we develop a conceptual model to describe the factors considered to be important to students and then quantify the network based on five individual perspectives: the students, a supervisor and a university research students centre manager. The resultant network comprised 37 factors and 40 connections, with an overall probability of timely completion of between 0.6 and 0.8. Across all participants, the four factors that were considered to most directly influence timely completion were personal aspects, the research environment, the research project, and incoming skills.

Creating a Sustainability Scorecard as a predictive tool for measuring the complex social, economic and environmental impacts of industries, a case study : assessing the viability and sustainability of the dairy industry

Sustainability is a key driver for decisions in the management and future development of industries. The World Commission on Environment and Development (WCED, 1987) outlined imperatives which need to be met for environmental, economic and social sustainability. Development of strategies for measuring and improving sustainability in and across these domains, however, has been hindered by intense debate between advocates for one approach fearing that efforts by those who advocate for another could have unintended adverse impacts. Studies attempting to compare the sustainability performance of countries and industries have also found ratings of performance quite variable depending on the sustainability indices used. Quantifying and comparing the sustainability of industries across the triple bottom line of economy, environment and social impact continues to be problematic. Using the Australian dairy industry as a case study, a Sustainability Scorecard, developed as a Bayesian network model, is proposed as an adaptable tool to enable informed assessment, dialogue and negotiation of strategies at a global level as well as being suitable for developing local solutions.

A new approach to spatial data interpolation using higher-order statistics

Interpolation techniques for spatial data have been applied frequently in various fields of geosciences. Although most conventional interpolation methods assume that it is sufficient to use first- and second-order statistics to characterize random fields, researchers have now realized that these methods cannot always provide reliable interpolation results, since geological and environmental phenomena tend to be very complex, presenting non-Gaussian distribution and/or non-linear inter-variable relationship. This paper proposes a new approach to the interpolation of spatial data, which can be applied with great flexibility. Suitable cross-variable higher-order spatial statistics are developed to measure the spatial relationship between the random variable at an unsampled location and those in its neighbourhood. Given the computed cross-variable higher-order spatial statistics, the conditional probability density function (CPDF) is approximated via polynomial expansions, which is then utilized to determine the interpolated value at the unsampled location as an expectation. In addition, the uncertainty associated with the interpolation is quantified by constructing prediction intervals of interpolated values. The proposed method is applied to a mineral deposit dataset, and the results demonstrate that it outperforms kriging methods in uncertainty quantification. The introduction of the cross-variable higher-order spatial statistics noticeably improves the quality of the interpolation since it enriches the information that can be extracted from the observed data, and this benefit is substantial when working with data that are sparse or have non-trivial dependence structures.

Bayesian networks for information synthesis in complex systems

This thesis introduces a method of applying Bayesian Networks to combine information from a range of data sources for effective decision support systems. It develops a set of techniques in development, validation, visualisation, and application of Complex Systems models, with a working demonstration in an Australian airport environment. The methods presented here have provided a modelling approach that produces highly flexible, informative and applicable interpretations of a system's behaviour under uncertain conditions. These end-to-end techniques are applied to the development of model based dashboards to support operators and decision makers in the multi-stakeholder airport environment. They provide highly flexible and informative interpretations and confidence in these interpretations of a system's behaviour under uncertain conditions.

Dynamic Bayesian network inferencing for non-homogeneous complex systems

Dynamic Bayesian Networks (DBNs) provide a versatile platform for predicting and analysing the behaviour of complex systems. As such, they are well suited to the prediction of complex ecosystem population trajectories under anthropogenic disturbances such as the dredging of marine seagrass ecosystems. However, DBNs assume a homogeneous Markov chain whereas a key characteristics of complex ecosystems is the presence of feedback loops, path dependencies and regime changes whereby the behaviour of the system can vary based on past states. This paper develops a method based on the small world structure of complex systems networks to modularise a non-homogeneous DBN and enable the computation of posterior marginal probabilities given evidence in forwards inference. It also provides an approach for an approximate solution for backwards inference as convergence is not guaranteed for a path dependent system. When applied to the seagrass dredging problem, the incorporation of path dependency can implement conditional absorption and allows release from the zero state in line with environmental and ecological observations. As dredging has a marked global impact on seagrass and other marine ecosystems of high environmental and economic value, using such a complex systems model to develop practical ways to meet the needs of conservation and industry through enhancing resistance and/or recovery is of paramount importance.

The importance of modelling heterogeneity in complex disease: application to NIMH Schizophrenia Genetics Initiative data

As for other complex diseases, linkage analyses of schizophrenia (SZ) have produced evidence for numerous chromosomal regions, with inconsistent results reported across studies. The presence of locus heterogeneity appears likely and may reduce the power of linkage analyses if homogeneity is assumed. In addition, when multiple heterogeneous datasets are pooled, inter-sample variation in the proportion of linked families (alpha) may diminish the power of the pooled sample to detect susceptibility loci, in spite of the larger sample size obtained. We compare the significance of linkage findings obtained using allele-sharing LOD scores (LOD(exp))-which assume homogeneity-and heterogeneity LOD scores (HLOD) in European American and African American NIMH SZ families. We also pool these two samples and evaluate the relative power of the LOD(exp) and two different heterogeneity statistics. One of these (HLOD-P) estimates the heterogeneity parameter alpha only in aggregate data, while the second (HLOD-S) determines alpha separately for each sample. In separate and combined data, we show consistently improved performance of HLOD scores over LOD(exp). Notably, genome-wide significant evidence for linkage is obtained at chromosome 10p in the European American sample using a recessive HLOD score. When the two samples are combined, linkage at the 10p locus also achieves genome-wide significance under HLOD-S, but not HLOD-P. Using HLOD-S, improved evidence for linkage was also obtained for a previously reported region on chromosome 15q. In linkage analyses of complex disease, power may be maximised by routinely modelling locus heterogeneity within individual datasets, even when multiple datasets are combined to form larger samples.

Using Bayesian networks to model surveillance in complex plant and animal health systems

In this chapter we consider biosecurity surveillance as part of a complex system comprising many different biological, environmental and human factors and their interactions. Modelling and analysis of surveillance strategies should take into account these complexities, and also facilitate the use and integration of the many types of different information that can provide insight into the system as a whole. After a brief discussion of a range of options, we focus on Bayesian networks for representing such complex systems. We summarize the features of Bayesian networks and describe these in the context of surveillance.