Towards a transdisciplinary approach to integrating dance in a middle school curriculum

Within Queensland middle schools the implementation of an integrated curriculum has been challenging for many practitioners. In working to enhance dance learning in a Queensland middle school this research has focused on how dance can be integrated using a transdisciplinary approach. The research has investigated and reflected on the teaching and learning strategies used to integrate dance and has identified the key issues and challenges associated with the complex nature of an integrated curriculum in this context. Action research was used to review, plan for and implement integrated curriculum approaches and give insight into the external and internal challenges within the practice. This research has identified challenges associated with sustaining the integrity of dance as a subject area when integration requires designing curricula that go across key learning area boundaries. It has also revealed working within an integrated curriculum requires using common planning principles that focus on the students’ problem solving skills, making connections with the concepts, topics or ideas from the unit of work. The discussion of ways of working highlights a set of values, strategies or attributes a dance teacher can use while working within this middle school context. These include making collaborative partnerships and showing a willingness to work outside your area of expertise. For the school community, it outlines issues for attention and recommendations to assist in implementing dance while using a transdisciplinary approach. These recommended steps include embedding opportunities for teachers to partake in common planning, and time for professional development around transdisciplinary learning and Arts education.

Towards learner autonomy : an assessment for learning approach

Using Assessment for Learning (AfL) may develop learner autonomy however, very often AfL is reduced to a set of strategies that do not always achieve the desired outcome. This research adopted a different approach that examined AfL as a cultural practice, situated within influential social relationships that shape learner identity. The study addressed the question “What are the qualities of the teacher-student relationship that support student learning autonomy in an AfL context?” Three case studies of the interactions of Queensland middle school teachers and their classes of Year 7, 8 and 9 were developed over one year. Data were collected from field notes and video recordings of classroom interactions and individual and focus group interviews with teachers and students. The analysis began with a close look at the field data. Interpretations that emerged from a sociocultural theoretical understanding were helpful in informing the process of analysis. Themes and patterns of interrelationships were identified through thematic coding using a constant comparative approach. Validation was achieved through methodological triangulation. Four findings that inform an understanding of AfL and the development of learner autonomy emerged. Firstly, autonomy is theorised as a context-specific identity mediated through the teacher-student relationship. Secondly, it was observed that learners negotiated their identities as knowers through AfL practices in various tacit, explicit, group and individual ways in a ‘generative dance’ of knowing in action (Cook & Brown, 2005). Thirdly, teachers and learners negotiated their participation by drawing from identities in multiple communities of practice. Finally it is proposed that a new participative identity or narrative for assessment is needed. This study contributes to understandings about teacher AfL practices that can help build teacher assessment capacity. Importantly, autonomy is understood as an identity that is available to all learners. This study is also significant as it affirms the importance of teacher assessment to support learners in developing autonomy, a focus that challenges the singular assessment policy focus on measuring performance. Finally this study contributes to a sociocultural theoretical understanding of AfL.

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.

A new approach to teaching signal processing at undergraduate level

This paper describes the design and implementation of a unique undergraduate program in signal processing at the Queensland University of Technology (QUT). The criteria that influenced the choice of the subjects and the laboratories developed to support them are presented. A recently established Signal Processing Research Centre (SPRC) has played an important role in the development of the signal processing teaching program. The SPRC also provides training opportunities for postgraduate studies and research.

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

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