Improving the provision of learning assistance services in higher education

This study is motivated by the need to look continually for ways to improve Griffith University's learning assistance services so that they meet the changed needs of stakeholders and are at the same time cost-effective and efficient. This study uses the conceptual tools of cultural-historical activity theory and expansive visibilisation to investigaate the developmenet and transformation of learning assistance services at Griffith University, one of Australia's largest mult-campus universities.

Application of higher order spectra to identify epileptic EEG

Epilepsy is characterized by the spontaneous and seemingly unforeseeable occurrence of seizures, during which the perception or behavior of patients is disturbed. An automatic system that detects seizure onsets would allow patients or the people near them to take appropriate precautions, and could provide more insight into this phenomenon. Various methods have been proposed to predict the onset of seizures based on EEG recordings. The use of nonlinear features motivated by the higher order spectra (HOS) has been reported to be a promising approach to differentiate between normal, background (pre-ictal) and epileptic EEG signals. In this work, we made a comparative study of the performance of Gaussian mixture model (GMM) and Support Vector Machine (SVM) classifiers using the features derived from HOS and from the power spectrum. Results show that the selected HOS based features achieve 93.11% classification accuracy compared to 88.78% with features derived from the power spectrum for a GMM classifier. The SVM classifier achieves an improvement from 86.89% with features based on the power spectrum to 92.56% with features based on the bispectrum.

Pattern recognition using invariants defined from higher order spectra : 2-D image inputs

A new algorithm for extracting features from images for object recognition is described. The algorithm uses higher order spectra to provide desirable invariance properties, to provide noise immunity, and to incorporate nonlinearity into the feature extraction procedure thereby allowing the use of simple classifiers. An image can be reduced to a set of 1D functions via the Radon transform, or alternatively, the Fourier transform of each 1D projection can be obtained from a radial slice of the 2D Fourier transform of the image according to the Fourier slice theorem. A triple product of Fourier coefficients, referred to as the deterministic bispectrum, is computed for each 1D function and is integrated along radial lines in bifrequency space. Phases of the integrated bispectra are shown to be translation- and scale-invariant. Rotation invariance is achieved by a regrouping of these invariants at a constant radius followed by a second stage of invariant extraction. Rotation invariance is thus converted to translation invariance in the second step. Results using synthetic and actual images show that isolated, compact clusters are formed in feature space. These clusters are linearly separable, indicating that the nonlinearity required in the mapping from the input space to the classification space is incorporated well into the feature extraction stage. The use of higher order spectra results in good noise immunity, as verified with synthetic and real images. Classification of images using the higher order spectra-based algorithm compares favorably to classification using the method of moment invariants

Shape discrimination using invariants defined from higher-order spectra

An approach to pattern recognition using invariant parameters based on higher-order spectra is presented. In particular, bispectral invariants are used to classify one-dimensional shapes. The bispectrum, which is translation invariant, is integrated along straight lines passing through the origin in bifrequency space. The phase of the integrated bispectrum is shown to be scale- and amplification-invariant. A minimal set of these invariants is selected as the feature vector for pattern classification. Pattern recognition using higher-order spectral invariants is fast, suited for parallel implementation, and works for signals corrupted by Gaussian noise. The classification technique is shown to distinguish two similar but different bolts given their one-dimensional profiles

A general procedure for the derivation of principal domains of higher-order spectra

A general procedure to determine the principal domain (i.e., nonredundant region of computation) of any higher-order spectrum is presented, using the bispectrum as an example. The procedure is then applied to derive the principal domain of the trispectrum of a real-valued, stationary time series. These results are easily extended to compute the principal domains of other higher-order spectra

Position, rotation, and scale invariant recognition of images using higher-order spectra

A new approach to recognition of images using invariant features based on higher-order spectra is presented. Higher-order spectra are translation invariant because translation produces linear phase shifts which cancel. Scale and amplification invariance are satisfied by the phase of the integral of a higher-order spectrum along a radial line in higher-order frequency space because the contour of integration maps onto itself and both the real and imaginary parts are affected equally by the transformation. Rotation invariance is introduced by deriving invariants from the Radon transform of the image and using the cyclic-shift invariance property of the discrete Fourier transform magnitude. Results on synthetic and actual images show isolated, compact clusters in feature space and high classification accuracies

Theorising a model for teaching and assessing reflective learning in higher education

The importance of reflection in higher education, and across disciplinary fields is widely recognised; it is generally included in university graduate attributes, professional standards and program objectives. Furthermore, reflection is commonly embedded into assessment requirements in higher education subjects, often without necessary scaffolding or clear expectations for students. Despite the rhetoric around the importance of reflection for ongoing learning, there is scant literature on any systematic, developmental approach to teaching reflective learning across higher education programs/courses. Given that professional or academic reflection is not intuitive, and requires specific pedagogic intervention to do well, a program/course-wide approach is essential. This paper draws on current literature to theorise a new, transferable and customisable model for teaching and assessing reflective learning across higher education, which foregrounds and explains the pedagogic field of higher education as a multi-dimensional space. We argue that explicit and strategic pedagogic intervention, supported by dynamic resources, is necessary for successful, broad-scale approaches to reflection in higher education.

Conceptualising and teaching discursive and performative reflection in higher education

The importance of reflection in higher education, and across disciplinary fields is widely recognised. It is generally embedded in university graduate attributes, professional standards and course objectives. Furthermore, reflection is commonly included in assessment requirements in higher education subjects, often without necessary scaffolding or clear expectations for students. It is essential that academic staff have substantive knowledge and clear expectations about the aims of reflective activities, the most effective mode of representation, and appropriate teaching strategies to support students in deep, critical reflection. The paper argues the case for reflection to be represented in different modes, using discursive (language) or performative (symbolic practice) forms of expression according to disciplinary context and individual communicative strengths. It introduces key discursive and expressive elements that constitute different modes of representation in reflective tasks. This functional analysis of textual elements provides explicit knowledge for teaching and assessing multiple modes of reflection in higher education.

Successes and stresses : A case study on relations between international higher degree students in Australia and their universities.

The paper examines the situation of postgraduate international students studying in Australia, mostly at doctoral level; a group widely seen as sought-after by Australian universities and employers, though also exposed to difficulties in aspects like learning culture, language and temporary employment. The investigation follows a novel path, as an exercise in practice-led research on issues involved in Higher Degree supervision. It is in fact an exercise within an advanced program of professional development for HD research supervisors. It begins by deploying a journalistic method, to obtain and present information. This has entailed the publishing of two feature articles about the lives of scholars for Subtropic, a campus based online magazine in Brisbane, www.subtropic.com.au. The next step is a review of a set of supervisions, citing issues raised in individual cases. Parallels can be seen between the two information-getting and analytical processes, with scope for contradictions. An exegetical statement deals with supervisory issues that have been exposed, and implications for learning, with recommendations for developing the quality of the experience of these students.

The risks and returns of new public management : political business

Purpose – This article aims to consider success in terms of the financial returns and risks of new public management (NPM) in state-owned enterprises (SOEs). Design/methodology/approach – Financial returns of New Zealand SOEs were examined through a review of their annual reports over a five-year period. Dimensions of risk were examined through interviews conducted in two phases over a two-year period with senior executives from 12 of the (then) 17 SOEs operating in New Zealand. Findings – Findings indicate the potential for SOEs to operate as profitable government investments, with clear support for positive financial returns under NPM. However, variations noted within individual SOEs also indicate that profitable and commercial operations may not be possible in all cases. An examination of the risks associated with SOEs’ operations reveals a number of dimensions of risk, encompassing financial, political (including regulatory), reputational, and public accountability aspects. Practical implications – There is a need for an enhanced awareness on the part of internal and external stakeholders (such as the government and general public) of the risks SOEs face in pursuing higher levels of profitability. Also required, is a more acute understanding on the part of internal and external stakeholders (e.g. government and the public) of the need for SOEs to manage the range of risks identified, given the potentially delicate balance between risk and return. Originality/value – While previous studies have considered the financial returns of SOEs, or the risks faced by the public sector in terms of accountability, few have addressed the two issues collectively in a single context.

Novel numerical methods for time-space fractional reaction diffusion equations in two dimensions

We consider time-space fractional reaction diffusion equations in two dimensions. This equation is obtained from the standard reaction diffusion equation by replacing the first order time derivative with the Caputo fractional derivative, and the second order space derivatives with the fractional Laplacian. Using the matrix transfer technique proposed by Ilic, Liu, Turner and Anh [Fract. Calc. Appl. Anal., 9:333--349, 2006] and the numerical solution strategy used by Yang, Turner, Liu, and Ilic [SIAM J. Scientific Computing, 33:1159--1180, 2011], the solution of the time-space fractional reaction diffusion equations in two dimensions can be written in terms of a matrix function vector product $f(A)b$ at each time step, where $A$ is an approximate matrix representation of the standard Laplacian. We use the finite volume method over unstructured triangular meshes to generate the matrix $A$, which is therefore non-symmetric. However, the standard Lanczos method for approximating $f(A)b$ requires that $A$ is symmetric. We propose a simple and novel transformation in which the standard Lanczos method is still applicable to find $f(A)b$, despite the loss of symmetry. Numerical results are presented to verify the accuracy and efficiency of our newly proposed numerical solution strategy.

Information literacy research : dimensions of the emerging collective consciousness

Information literacy researchers are beginning to develop a collective consciousness, a consciousness that represents the newly appearing territory of information literacy research. This paper analyses the information literacy research territory as it is represented by the emerging collective consciousness of information literacy researchers. Five dimensions of the collective consciousness are proposed: 1) the sectoral location of the research, 2) ways of seeing information literacy, 3) ‘what’ is being investigated; that is the research object, 4) ‘how’ the object is being investigated; that is the research approaches and paradigms, and 5) disciplinary influences. These dimensions are used to: 1) reveal the character of the information literacy research territory which is in early stages of construction; 2) show how different kinds of research approaches can shed different kinds of light on the object of research; and 3) demonstrate how the five dimensions work together in the development of new studies.

Subjective and Objective Indicators of Recovery in Severe Mental Illness: a Cross-Sectional Study

Background: This study aimed to determine whether subjective dimensions of recovery such as empowerment are associated with self-report of more objective indicators such as level of participation in the community and income from employment. A secondary aim was to investigate the extent to which diagnosis or other consumer characteristics mediated any relationship between these variables. Methods: The Community Integration Measure, the Empowerment Scale, the Recovery Assessment Scale, and the Camberwell Assessment of Needs Short Appraisal Schedule were administered to a convenience sample of 161 consumers with severe mental illness. Results: The majority of participants had a primary diagnosis of schizophreniform, anxiety/depression or bipolar affective disorder. The Empowerment Scale was quite strongly correlated with the Recovery Assessment Scale and the Community Integration Measure. Participants with a diagnosis of bipolar affective disorder had signifi cantly higher recovery and empowerment scores than participants with schizophrenia or depression. Both empowerment and recovery scores were significantly higher for people engaged in paid employment than for those receiving social security benefits. Conclusions: The measurement of subjective dimensions of recovery such as empowerment has validity in evaluation of global recovery for people with severe mental illness. A diagnosis of bipolar disorder is associated with higher scores on subjective and objective indicators of recovery.

Pattern recognition using invariants defined from higher order spectra - one-dimensional inputs

A new approach to pattern recognition using invariant parameters based on higher order spectra is presented. In particular, invariant parameters derived from the bispectrum are used to classify one-dimensional shapes. The bispectrum, which is translation invariant, is integrated along straight lines passing through the origin in bifrequency space. The phase of the integrated bispectrum is shown to be scale and amplification invariant, as well. A minimal set of these invariants is selected as the feature vector for pattern classification, and a minimum distance classifier using a statistical distance measure is used to classify test patterns. The classification technique is shown to distinguish two similar, but different bolts given their one-dimensional profiles. Pattern recognition using higher order spectral invariants is fast, suited for parallel implementation, and has high immunity to additive Gaussian noise. Simulation results show very high classification accuracy, even for low signal-to-noise ratios.