How well are Australian children doing in the first year of school?

Background: The transition to school is a sensitive period for children in relation to school success. In the early school years, children need to develop positive attitudes to school and have experiences that promote academic, behavioural and social competence. When children begin school there are higher expectations of responsibility and independence and in the year one class, there are more explicit academic goals for literacy and numeracy and more formal instruction. Most importantly, children’s early attitudes to learning and learning styles have an impact on later educational outcomes. Method: Data were drawn from The Longitudinal Study of Australian Children (LSAC). LSAC is a cross-sequential cohort study funded by the Australian Government. In these analyses, Wave 2 (2006) data for 2499 children in the Kindergarten Cohort were used. Children, at Wave 2, were in the first year of formal school. They had a mean age of 6.9 years (SD= 0.26). Measures included a 6-item measure of Approaches to Learning (task persistence, independence) and the Academic Rating Scales for language and literacy and mathematical thinking. Teachers rated their relationships with children on the short form of the STRS. Results: Girls were rated by their teachers as doing better than boys on Language and literacy, Approaches to learning; and they had a better relationship with their teacher. Children from an Aboriginal or Torres Strait Island (ATSI) background were rated as doing less well on Language and Literacy and Mathematical thinking and on their Approaches to learning. Children from high Socio Economic Position families are doing better on teacher rated Language and Literacy, Mathematical thinking, Approaches to learning and they had a better relationship with their teacher. Conclusions: Findings highlight the importance of key demographic variables in understanding children’s early school success.

The status of Australian nurse practitioners : the first national census

A five-section questionnaire was mailed to all 234 authorised Australian nurse practitioners in late 2007. An 85% response rate was achieved (202 responses). Respondents had a mean age of 47.0 years and 84.2% were women. Only 145 nurse practitioners (72% of respondents) reported being employed in Australia at the time of the census. Emergency nurse practitioners were the most commonly employed nationally (26.9%). Nearly one third of employed nurse practitioners reported that they were still awaiting approval to prescribe medications despite this being a core legislated skill. Over 70% stated that lack of Medicare provider numbers and lack of authority to prescribe through the Pharmaceut ical Benef its Scheme was extremely limiting to their practice. These findings are consistent with the international literature describing establishment of reformative

Ethyl-eicosapentaenoic acid in first-episode psychosis : a 1H-MRS study

Ethyl-eicosapentaenoic acid (E-EPA) is an omega-3 fatty acid that has been used in a range of neuropsychiatric conditions with some benefits. However, its mechanism of action is unknown. Here, we investigate its effects on in vivo brain metabolism in first-episode psychosis (FEP). Proton magnetic resonance spectroscopy at 3 T was performed in the temporal lobes of 24 FEP patients before and after 12 weeks of treatment in the context of a larger double-blind, placebo-controlled E-EPA augmentation study. Treatment group effects for glutathione (F1,12=6.1, p=0.03), and a hemisphere-by-group interaction for glutamine/glutamate (F1,20=4.4, p=0.049) were found. Glutathione increased bilaterally and glutamate/glutamine increased in the left hemisphere following E-EPA administration. Improvement in negative symptoms correlated with metabolic brain changes, particularly glutathione (r=-0.57). These results suggest that E-EPA augmentation alters glutathione availability and modulates the glutamine/glutamate cycle in early psychosis, with some of the metabolic brain changes being correlated with negative symptom improvement. Larger confirmatory studies of these postulated metabolic brain effects of E-EPA are warranted.

A 1H-MRS investigation of the medial temporal lobe in antipsychotic-naïve and early-treated first episode psychosis

Schizophrenia is associated with significant brain abnormalities, including changes in brain metabolites as measured by proton magnetic resonance spectroscopy (MRS). What remains unclear is the extent to which these changes are a consequence of the emergence of psychotic disorders or the result of treatment with antipsychotic medication. We assessed 34 patients with first episode psychosis (15 antipsychotic naïve) and 19 age- and gender-matched controls using short-echo MRS in the medial temporal lobe bilaterally. Overall, there were no differences in any metabolite, regardless of treatment status. However, when the analysis was limited to patients with a diagnosis of schizophrenia, schizophreniform or schizoaffective disorder, significant elevations of creatine/phosphocreatine (Cr/PCr) and myo-inositol (mI) were found in the treated group. These data indicate a relative absence of temporal lobe metabolic abnormalities in first episode psychosis, but suggest that some treatment-related changes in mI might be apparent in patients with schizophrenia-spectrum diagnoses. Seemingly illness-related Cr/PCr elevations were also specific to the diagnosis of schizophrenia-spectrum disorder and seem worthy of future study.

Harnessing assessment and feedback in the first year to support learning success, engagement and retention

It has been argued that intentional first year curriculum design has a critical role to play in enhancing first year student engagement, success and retention (Kift, 2008). A fundamental first year curriculum objective should be to assist students to make the successful transition to assessment in higher education. Scott (2006) has identified that ‘relevant, consistent and integrated assessment … [with] prompt and constructive feedback’ are particularly relevant to student retention generally; while Nicol (2007) suggests that ‘lack of clarity regarding expectations in the first year, low levels of teacher feedback and poor motivation’ are key issues in the first year. At the very minimum, if we expect first year students to become independent and self-managing learners, they need to be supported in their early development and acquisition of tertiary assessment literacies (Orrell, 2005). Critical to this attainment is the necessity to alleviate early anxieties around assessment information, instructions, guidance, and performance. This includes, for example:  inducting students thoroughly into the academic languages and assessment genres they will encounter as the vehicles for evidencing learning success; and  making expectations about the quality of this evidence clear. Most importantly, students should receive regular formative feedback of their work early in their program of study to aid their learning and to provide information to both students and teachers on progress and achievement. Leveraging research conducted under an ALTC Senior Fellowship that has sought to articulate a research-based 'transition pedagogy' (Kift & Nelson, 2005) – a guiding philosophy for intentional first year curriculum design and support that carefully scaffolds and mediates the first year learning experience for contemporary heterogeneous cohorts – this paper will discuss theoretical and practical strategies and examples that should be of assistance in implementing good assessment and feedback practices across a range of disciplines in the first year.

Designing for engagement : building IT systems

Context The School of Information Technology at QUT has recently undertaken a major restructuring of their Bachelor of Information Technology (BIT) course. Some of the aims of this restructuring include a reduction in first year attrition and to provide an attractive degree course that meets both student and industry expectations. Emphasis has been placed on the first semester in the context of retaining students by introducing a set of four units that complement one another and provide introductory material on technology, programming and related skills, and generic skills that will aid the students throughout their undergraduate course and in their careers. This discussion relates to one of these four fist semester units, namely Building IT Systems. The aim of this unit is to create small Information Technology (IT) systems that use programming or scripting, databases as either standalone applications or web applications. In the prior history of teaching introductory computer programming at QUT, programming has been taught as a stand alone subject and integration of computer applications with other systems such as databases and networks was not undertaken until students had been given a thorough grounding in those topics as well. Feedback has indicated that students do not believe that working with a database requires programming skills. In fact, the teaching of the building blocks of computer applications have been compartmentalized and taught in isolation from each other. The teaching of introductory computer programming has been an industry requirement of IT degree courses as many jobs require at least some knowledge of the topic. Yet, computer programming is not a skill that all students have equal capabilities of learning (Bruce et al., 2004) and this is clearly shown by the volume of publications dedicated to this topic in the literature over a broad period of time (Eckerdal & Berglund, 2005; Mayer, 1981; Winslow, 1996). The teaching of this introductory material has been done pretty much the same way over the past thirty years. During this period of time that introductory computer programming courses have been taught at QUT, a number of different programming languages and programming paradigms have been used and different approaches to teaching and learning have been attempted in an effort to find the golden thread that would allow students to learn this complex topic. Unfortunately, computer programming is not a skill that can be learnt in one semester. Some basics can be learnt but it can take many years to master (Norvig, 2001). Faculty data typically has shown a bimodal distribution of results for students undertaking introductory programming courses with a high proportion of students receiving a high mark and a high proportion of students receiving a low or failing mark. This indicates that there are students who understand and excel with the introductory material while there is another group who struggle to understand the concepts and practices required to be able to translate a specification or problem statement into a computer program that achieves what is being requested. The consequence of a large group of students failing the introductory programming course has been a high level of attrition amongst first year students. This attrition level does not provide good continuity in student numbers in later years of the degree program and the current approach is not seen as sustainable.

Osteryoung square wave voltammetric determination of lactose in food samples by a derivative procedure

A method for determination of lactose in food samples by Osteryoung square wave voltammetry (OSWV) was developed. It was based on the nucleophilic addition reaction between lactose and aqua ammonia. The carbonyl group of lactose can be changed into imido group, and this increases the electrochemical activity in reduction and the sensitivity. The optimal condition for the nucleophilic addition reaction was investigated and it was found that in NH4Cl–NH3 buffer of pH 10.1, the linear range between the peak current and the concentration of lactose was 0.6–8.4 mg L−1, and the detection limits was 0.44 mg L−1. The proposed method was applied to the determination of lactose in food samples and satisfactory results were obtained.

Introduction of a short writing task to measure and develop academic literacy in first year health undergraduates

This paper describes an initiative in the Faculty of Health at the Queensland University of Technology, Australia, where a short writing task was introduced to first year undergraduates in four courses including Public Health, Nursing, Social Work and Human Services, and Human Movement Studies. Over 1,000 students were involved in the trial. The task was assessed using an adaptation of the MASUS Procedure (Measuring the Academic Skills of University Students) (Webb & Bonanno, 1994). Feedback to the students including MASUS scores then enabled students to be directed to developmental workshops targeting their academic literacy needs. Students who achieved below the benchmark score were required to attend academic writing workshops in order to obtain the same summative 10% that was obtained by those who had achieved above the benchmark score. The trial was very informative, in terms of determining task appropriateness and timing, student feedback, student use of support, and student perceptions of the task and follow-up workshops. What we learned from the trial will be presented with a view to further refinement of this initiative.

A review of prostate-specific antigen screening prevalence and risk perceptions for first-degree relatives of men with prostate cancer

First-degree relatives of men with prostate cancer have a higher risk of being diagnosed with prostate cancer than men without a family history. The present review examines the prevalence and predictors of testing in first-degree relatives, perceptions of risk, prostate cancer knowledge and psychological consequences of screening. Medline, PsycInfo and Cinahl databases were searched for articles examining risk perceptions or screening practices of first-degree relatives of men with prostate cancer for the period of 1990 to August 2007. Eighteen studies were eligible for inclusion. First-degree relatives participated in prostate-specific antigen (PSA) testing more and perceived their risk of prostate cancer to be higher than men without a family history. Family history factors (e.g. being an unaffected son rather than an unaffected brother) were consistent predictors of PSA testing. Studies were characterized by sampling biases and a lack of longitudinal assessments. Prospective, longitudinal assessments with well-validated and comprehensive measures are needed to identify factors that cue the uptake of screening and from this develop an evidence base for decision support. Men with a family history may benefit from targeted communication about the risks and benefits of prostate cancer testing that responds to the implications of their heightened risk.

Inducing shades of meaning by matrix methods : a first step towards thematic analysis of opinion

This article explores two matrix methods to induce the ``shades of meaning" (SoM) of a word. A matrix representation of a word is computed from a corpus of traces based on the given word. Non-negative Matrix Factorisation (NMF) and Singular Value Decomposition (SVD) compute a set of vectors corresponding to a potential shade of meaning. The two methods were evaluated based on loss of conditional entropy with respect to two sets of manually tagged data. One set reflects concepts generally appearing in text, and the second set comprises words used for investigations into word sense disambiguation. Results show that for NMF consistently outperforms SVD for inducing both SoM of general concepts as well as word senses. The problem of inducing the shades of meaning of a word is more subtle than that of word sense induction and hence relevant to thematic analysis of opinion where nuances of opinion can arise.

Numerical methods for fractional partial differential equations with Riesz space fractional derivatives

In this paper, we consider the numerical solution of a fractional partial differential equation with Riesz space fractional derivatives (FPDE-RSFD) on a finite domain. Two types of FPDE-RSFD are considered: the Riesz fractional diffusion equation (RFDE) and the Riesz fractional advection–dispersion equation (RFADE). The RFDE is obtained from the standard diffusion equation by replacing the second-order space derivative with the Riesz fractional derivative of order αset membership, variant(1,2]. The RFADE is obtained from the standard advection–dispersion equation by replacing the first-order and second-order space derivatives with the Riesz fractional derivatives of order βset membership, variant(0,1) and of order αset membership, variant(1,2], respectively. Firstly, analytic solutions of both the RFDE and RFADE are derived. Secondly, three numerical methods are provided to deal with the Riesz space fractional derivatives, namely, the L1/L2-approximation method, the standard/shifted Grünwald method, and the matrix transform method (MTM). Thirdly, the RFDE and RFADE are transformed into a system of ordinary differential equations, which is then solved by the method of lines. Finally, numerical results are given, which demonstrate the effectiveness and convergence of the three numerical methods.

Intentional first year curriculum design as a means of facilitating student engagement: some exemplars

The importance of student engagement to higher education quality, making deep learning outcomes possible for students, and achieving student retention, is increasingly being understood. The issue of student engagement in the first year of tertiary study is of particular significance. This paper takes the position that the first year curriculum, and the pedagogical principles that inform its design, are critical influencers of student engagement in the first year learning environment. We use an analysis of case studies prepared for Kift’s ALTC Senior Fellowship to demonstrate ways in which student engagement in the first year of tertiary study can be successfully supported through intentional curriculum design that motivates students to learn, provides a positive learning climate, and encourages students to be active in their learning.

Numerical investigations of linear least squares methods for derivative estimation

The results of a numerical investigation into the errors for least squares estimates of function gradients are presented. The underlying algorithm is obtained by constructing a least squares problem using a truncated Taylor expansion. An error bound associated with this method contains in its numerator terms related to the Taylor series remainder, while its denominator contains the smallest singular value of the least squares matrix. Perhaps for this reason the error bounds are often found to be pessimistic by several orders of magnitude. The circumstance under which these poor estimates arise is elucidated and an empirical correction of the theoretical error bounds is conjectured and investigated numerically. This is followed by an indication of how the conjecture is supported by a rigorous argument.

Stochastic modelling of financial time series with memory and multifractal scaling

Financial processes may possess long memory and their probability densities may display heavy tails. Many models have been developed to deal with this tail behaviour, which reflects the jumps in the sample paths. On the other hand, the presence of long memory, which contradicts the efficient market hypothesis, is still an issue for further debates. These difficulties present challenges with the problems of memory detection and modelling the co-presence of long memory and heavy tails. This PhD project aims to respond to these challenges. The first part aims to detect memory in a large number of financial time series on stock prices and exchange rates using their scaling properties. Since financial time series often exhibit stochastic trends, a common form of nonstationarity, strong trends in the data can lead to false detection of memory. We will take advantage of a technique known as multifractal detrended fluctuation analysis (MF-DFA) that can systematically eliminate trends of different orders. This method is based on the identification of scaling of the q-th-order moments and is a generalisation of the standard detrended fluctuation analysis (DFA) which uses only the second moment; that is, q = 2. We also consider the rescaled range R/S analysis and the periodogram method to detect memory in financial time series and compare their results with the MF-DFA. An interesting finding is that short memory is detected for stock prices of the American Stock Exchange (AMEX) and long memory is found present in the time series of two exchange rates, namely the French franc and the Deutsche mark. Electricity price series of the five states of Australia are also found to possess long memory. For these electricity price series, heavy tails are also pronounced in their probability densities. The second part of the thesis develops models to represent short-memory and longmemory financial processes as detected in Part I. These models take the form of continuous-time AR(∞) -type equations whose kernel is the Laplace transform of a finite Borel measure. By imposing appropriate conditions on this measure, short memory or long memory in the dynamics of the solution will result. A specific form of the models, which has a good MA(∞) -type representation, is presented for the short memory case. Parameter estimation of this type of models is performed via least squares, and the models are applied to the stock prices in the AMEX, which have been established in Part I to possess short memory. By selecting the kernel in the continuous-time AR(∞) -type equations to have the form of Riemann-Liouville fractional derivative, we obtain a fractional stochastic differential equation driven by Brownian motion. This type of equations is used to represent financial processes with long memory, whose dynamics is described by the fractional derivative in the equation. These models are estimated via quasi-likelihood, namely via a continuoustime version of the Gauss-Whittle method. The models are applied to the exchange rates and the electricity prices of Part I with the aim of confirming their possible long-range dependence established by MF-DFA. The third part of the thesis provides an application of the results established in Parts I and II to characterise and classify financial markets. We will pay attention to the New York Stock Exchange (NYSE), the American Stock Exchange (AMEX), the NASDAQ Stock Exchange (NASDAQ) and the Toronto Stock Exchange (TSX). The parameters from MF-DFA and those of the short-memory AR(∞) -type models will be employed in this classification. We propose the Fisher discriminant algorithm to find a classifier in the two and three-dimensional spaces of data sets and then provide cross-validation to verify discriminant accuracies. This classification is useful for understanding and predicting the behaviour of different processes within the same market. The fourth part of the thesis investigates the heavy-tailed behaviour of financial processes which may also possess long memory. We consider fractional stochastic differential equations driven by stable noise to model financial processes such as electricity prices. The long memory of electricity prices is represented by a fractional derivative, while the stable noise input models their non-Gaussianity via the tails of their probability density. A method using the empirical densities and MF-DFA will be provided to estimate all the parameters of the model and simulate sample paths of the equation. The method is then applied to analyse daily spot prices for five states of Australia. Comparison with the results obtained from the R/S analysis, periodogram method and MF-DFA are provided. The results from fractional SDEs agree with those from MF-DFA, which are based on multifractal scaling, while those from the periodograms, which are based on the second order, seem to underestimate the long memory dynamics of the process. This highlights the need and usefulness of fractal methods in modelling non-Gaussian financial processes with long memory.