Starting small : a staged approach to professional development in gifted education

One of the potentially far reaching recommendations of the Senate Inquiry of 2001 was to fund professional development for teachers of gifted children under the Australian Government Quality Teacher Program (AGQTP). This funding was made available to all sectors of schooling and led to a number of initiatives to address the shortcomings in gifted education identified in the Senate Report. This paper reports on the initiatives undertaken by one sector over an eight-year period. The initiative began with a commitment from the sector to provide professional development in gifted education and later required that sector to address gifted education in their school renewal planning. A professional development program was planned and implemented in stages drawing on the AGQTP modules. However, teachers were encouraged to pursue an active role in instigating their own professional development priorities and needs. Thus, teachers within an action research framework collaboratively designed, implemented and reflected on projects which progressively expanded over a three year period. Initial projects focussed on their own teaching or context. In the second year of the three-year-cycle projects expanded to include colleagues. Finally, in the third year teachers assumed a leadership role in their schools or district and mentored other teachers beginning the program. The paper presents both qualitative and quantitative data on the experiences of the participating teachers and the long term impact on the capacity of the jurisdiction to provide enhanced opportunities for gifted children.

A multicomponent multidisciplinary approach to obesity management

This thesis explored the potential of a multi-component and multi-disciplinary approach to obesity management using action research methodologies. A preliminary systems-based model for obesity management was developed based around four meta-components (client factors, practitioner factors, process factors and the environment) and two action theories (action research and action science).

Professional conversations : a collaborative approach to support policy implementation, professional learning and practice change in ECEC

In 2012, Australia introduced a new National Quality Framework, comprising enhanced quality expectations for early childhood education and care services, two national learning frameworks and a new Assessment and Rating System spanning child care centres, kindergartens and preschools, family day care and outside school hours care. This is the linchpin in a series of education reforms designed to support increased access to higher quality early childhood education and care (ECEC) and successful transition to school. As with any policy change, success in real terms relies upon building shared understanding and the capacity of educators to apply new knowledge and to support change and improved practice within their service. With this in mind, a collaborative research project investigated the efficacy of a new approach to professional learning in ECEC: the professional conversation. This paper reports on the trial and evaluation of a series of professional conversations to support implementation of one element of the NQF, the Early Years Learning Framework (DEEWR,2009), and their capacity to promote collaborative reflective practice, shared understanding, and improved practice in ECEC. Set against the backdrop of the NQF, this paper details the professional conversation approach, key challenges and critical success factors, and the learning outcomes for conversation participants. Findings support the efficacy of this approach to professional learning in ECEC, and its capacity to support policy reform and practice change in ECEC.

The changing motivations of students’ use of lecture podcasts across a semester: An extended theory of planned behaviour approach

We extended the previous work of Moss, O’Connor and White, to include a measure of group norms within the theory of planned behaviour (TPB), to examine the influences on students’ decisions to use lecture podcasts as part of their learning. Participants (N = 90) completed the extended TPB predictors before semester began (Time 1) and mid-semester (Time 2) and reported on their podcast use at mid-semester (Time 2) and end of semester (Time 3). We found that attitudes and perceived social pressures were important in informing intentions at both time points. At Time 1, perceptions of control over performing the behaviour and, at Time 2, perceptions of whether podcast use was normative among fellow students (group norms) also predicted intended podcast use. Intentions to use podcasting predicted self-reported use at both Time 2 and Time 3. These results provide important applied information for educators to encourage student use of novel on-line educational tools.

Arm’s length pricing and multinational banks : an old fashioned approach in a modern world

In “Arm’s Length Pricing and Multinational Banks: An Old Fashioned Approach in a Modern World”, Kerrie Sadiq, describes the high level of integration of multinational financial institutions and argues that treating each element within a given operation as a separate entity for transfer pricing purposes is not economically or legally realistic. She proposes instead formulary apportionment as a device for managing this complexity.

A Bayesian regression approach to assess uncertainty in pollutant wash-off modelling

Due to knowledge gaps in relation to urban stormwater quality processes, an in-depth understanding of model uncertainty can enhance decision making. Uncertainty in stormwater quality models can originate from a range of sources such as the complexity of urban rainfall-runoff-stormwater pollutant processes and the paucity of observed data. Unfortunately, studies relating to epistemic uncertainty, which arises from the simplification of reality are limited and often deemed mostly unquantifiable. This paper presents a statistical modelling framework for ascertaining epistemic uncertainty associated with pollutant wash-off under a regression modelling paradigm using Ordinary Least Squares Regression (OLSR) and Weighted Least Squares Regression (WLSR) methods with a Bayesian/Gibbs sampling statistical approach. The study results confirmed that WLSR assuming probability distributed data provides more realistic uncertainty estimates of the observed and predicted wash-off values compared to OLSR modelling. It was also noted that the Bayesian/Gibbs sampling approach is superior compared to the most commonly adopted classical statistical and deterministic approaches commonly used in water quality modelling. The study outcomes confirmed that the predication error associated with wash-off replication is relatively higher due to limited data availability. The uncertainty analysis also highlighted the variability of the wash-off modelling coefficient k as a function of complex physical processes, which is primarily influenced by surface characteristics and rainfall intensity.

A methodological approach to reporting corrected small field relative outputs

Purpose The goal of this work was to set out a methodology for measuring and reporting small field relative output and to assess the application of published correction factors across a population of linear accelerators. Methods and materials Measurements were made at 6 MV on five Varian iX accelerators using two PTW T60017 unshielded diodes. Relative output readings and profile measurements were made for nominal square field sizes of side 0.5 to 1.0 cm. The actual in-plane (A) and cross-plane (B) field widths were taken to be the FWHM at the 50% isodose level. An effective field size, defined as FSeff=A·B, was calculated and is presented as a field size metric. FSeffFSeff was used to linearly interpolate between published Monte Carlo (MC) calculated kQclin,Qmsrfclin,fmsr values to correct for the diode over-response in small fields. Results The relative output data reported as a function of the nominal field size were different across the accelerator population by up to nearly 10%. However, using the effective field size for reporting showed that the actual output ratios were consistent across the accelerator population to within the experimental uncertainty of ±1.0%. Correcting the measured relative output using kQclin,Qmsrfclin,fmsr at both the nominal and effective field sizes produce output factors that were not identical but differ by much less than the reported experimental and/or MC statistical uncertainties. Conclusions In general, the proposed methodology removes much of the ambiguity in reporting and interpreting small field dosimetric quantities and facilitates a clear dosimetric comparison across a population of linacs

Estimation of biodiesel properties from chemical composition – An artificial neural network (ANN) approach

Biodiesel, produced from renewable feedstock represents a more sustainable source of energy and will therefore play a significant role in providing the energy requirements for transportation in the near future. Chemically, all biodiesels are fatty acid methyl esters (FAME), produced from raw vegetable oil and animal fat. However, clear differences in chemical structure are apparent from one feedstock to the next in terms of chain length, degree of unsaturation, number of double bonds and double bond configuration-which all determine the fuel properties of biodiesel. In this study, prediction models were developed to estimate kinematic viscosity of biodiesel using an Artificial Neural Network (ANN) modelling technique. While developing the model, 27 parameters based on chemical composition commonly found in biodiesel were used as the input variables and kinematic viscosity of biodiesel was used as output variable. Necessary data to develop and simulate the network were collected from more than 120 published peer reviewed papers. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture and learning algorithm were optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the coefficient of determination (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found high predictive accuracy of the ANN in predicting fuel properties of biodiesel and has demonstrated the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties. Therefore the model developed in this study can be a useful tool to accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.