The professional development, resource and support needs of rural and urban ICT teachers

This paper presents findings from the SiMERR National Survey concerning the need priorities of secondary ICT teachers for professional development, resources and student learning experiences. The findings - drawn from a survey of 237 secondary ICT teachers across Australia - provide an opportunity to compare the needs of teachers working in metropolitan, provincial and remote schools. The study found that vacant ICT positions are difficult to fill ond that the novel on dynamic nature of ICT requires teachers to have more extensive opportunities for on-the-job training, collegial collaboration and mentoring than is the case for teachers of more traditional subjects like science and mathematics. The study also found that ICT teachers are commonly required to manage and maintain ICT resources and to assist other staff to use ICT resources, while being allocated insufficient time in which to do these additional activities. The implications of these and other findings are discussed along with recommendations to help address the needs of ICT teachers in different parts of Australia.

Implementation of e-learning: A case study of three schools

As Western Australian schools move to implement technology into the classroom, there appears to be prevalence in combining e-learning with face to face traditional classroom practice. This has been accompanied by a shift toward a digital curriculum that incorporates re-usable learning objects. Essential to any teacher contemplating the use of a digital curriculum resource is not only the knowledge of learning theories but models of best practice to create online curriculum for students to use in every day classrooms. This paper explores the e-learning practices in three case study schools (n=3) in Western Australia. Data were collected by observation and interviews (n=11) conducted with the teachers and the ICT co-ordinators, to ascertain their perceptions and experiences with regard to the e-learning environment. There were challenges associated with the implementation of e-learning by teachers into their classroom such as skill development, changes in their role and the pedagogies they employ. The case study schools were pilot schools breaking new ground in order to test a new portal technology. Findings indicated that successful implementation of the e-learning environment was dependent on the four key factors of ICT infrastructure, ICT leadership, support and training initiatives and the teachers’ ICT capacity.

An online community designed to support future makers in educational reform

Australian education is undergoing national reform at many levels. The school sector, where preservice teachers will be employed, are adjusting to the demands of the National Curriculum and improving teacher quality through the National Professional Standards for Teachers. In addition, the university sector, where pre-service teachers are prepared, is undergoing its own education reform through the introduction of a demand-driven system and ensuring quality for tertiary education interns through the Higher Education Standards Framework. In moving to prepare preservice teachers for the school system; universities are grappling with the double-barreled approach to teacher quality; quality within the university course and quality within the student teachers being prepared. Through a collaborative partnership including university lecturers, Department of Education central administration staff, school principals, school coordinators, practicum supervisors, mentor teachers and pre-service teachers; the stakeholders have formed an online community of learners engaging in reflective practice who are committed to improving teacher quality. This online community not only links the key stakeholders within the project, it facilitates the nexus between theory and practice often missing in our pre-service teacher placements. This paper reports preliminary data about an initiative to ensure final year pre-service teachers are aspiring to meet the graduate professional standards through the use of an innovative online community.

Patterns of support in an online community for smoking cessation

Social support offers various benefits for health and behaviour change. However, previous work has shown that individuals are typically reluctant to ask for support on social network sites, unless they can present a changed, healthier identity. To examine the relationship between stage of change and social support we conducted a thematic analysis of messages posted in a public Facebook support group for people trying to quit smoking. Our findings show that the kind of support exchanged online is related to participants' stage of change. Contrary to our expectations, supportive responses and leadership in the support group came mainly from users who just started their change process rather than people who had already changed. We discuss contributions to theories of online participation and impression management as well as implications for practitioners who seek to establish support groups.

Expanding the research area of behavior change support systems

The First International Workshop on Behavior Change Support Systems attracted a great research interest. The selected papers focused on abstraction, implementation and evaluation of Behavior Change Support Systems. The workshop is an evidence of how researchers from around the globe have their own perspective of behavior change interventions. In this abstract, we have attempted to outline core issues that can enhance persuasiveness of such support systems. Finally, we highlight important research questions relating to the development of effective Behavior Change Support Systems.

Incorporating the interrelationship between primary and support processes in monitoring and optimising longwall coal mining efficiency

This thesis increased the researchers understanding of the relationship between operations and maintenance in underground longwall coal mines, using data from a Queensland underground coal mine. The thesis explores various relationships between recorded variables. Issues with human recorded data was uncovered, and results emphasised the significance of variables associated with conveyor operation to explain production.

Academic achievement among recently arrived Chinese adolescent migrants: The role of social support, school belonging, and acculturative stress

Factors contributing to academic achievement among recently arrived Chinese adolescents in Australia remains relatively underexplored. Previous studies focused on Asian migrants, including Chinese, but did not distinguish Chinese from other Asian migrants. The current study specifically looks at Chinese migrants who have recently arrived as opposed to Asian migrants. This study aims to explore the role of social support, school belonging, and acculturative stress on academic achievement of recently arrived Chinese adolescents (n = 55). Questionnaires were administered to this sample. The results indicated that school belonging, interestingly, was negatively associated with academic achievement. Perceived social support and acculturative stress were not significantly associated with academic achievement. The findings provide insights into risk and protective factors influencing academic achievement of Chinese migrants. Implications of the findings are discussed.

Empowerment climate, supervisory support for creativity and work role performance in new product development settings: A team level investigation

This thesis is an examination of how organisational context variables affect the performance of new product development (NPD) teams. Specifically, the extent to how team empowerment climate and supervisory support for creativity impact NPD team performance. Moreover, this thesis is a step forward in the ongoing development of work role performance theory by examining Griffin et al.'s (2007) work role performance model in the context of NPD teams. This thesis addresses the lack of research exploring work role performance dimensions in NPD teams and the extent to which a team empowerment climate and supervisory support for creativity impact NPDs performance.

Modeling finite buffer effects on TCP traffic over an IEEE 802.11 infrastructure WLAN

The network scenario is that of an infrastructure IEEE 802.11 WLAN with a single AP with which several stations (STAs) are associated. The AP has a finite size buffer for storing packets. In this scenario, we consider TCP-controlled upload and download file transfers between the STAs and a server on the wireline LAN (e.g., 100 Mbps Ethernet) to which the AP is connected. In such a situation, it is well known that because of packet losses due to finite buffers at the AP, upload file transfers obtain larger throughputs than download transfers. We provide an analytical model for estimating the upload and download throughputs as a function of the buffer size at the AP. We provide models for the undelayed and delayed ACK cases for a TCP that performs loss recovery only by timeout, and also for TCP Reno. The models are validated incomparison with NS2 simulations.

R1STM: One-class support tensor machine with randomised kernel

Identifying unusual or anomalous patterns in an underlying dataset is an important but challenging task in many applications. The focus of the unsupervised anomaly detection literature has mostly been on vectorised data. However, many applications are more naturally described using higher-order tensor representations. Approaches that vectorise tensorial data can destroy the structural information encoded in the high-dimensional space, and lead to the problem of the curse of dimensionality. In this paper we present the first unsupervised tensorial anomaly detection method, along with a randomised version of our method. Our anomaly detection method, the One-class Support Tensor Machine (1STM), is a generalisation of conventional one-class Support Vector Machines to higher-order spaces. 1STM preserves the multiway structure of tensor data, while achieving significant improvement in accuracy and efficiency over conventional vectorised methods. We then leverage the theory of nonlinear random projections to propose the Randomised 1STM (R1STM). Our empirical analysis on several real and synthetic datasets shows that our R1STM algorithm delivers comparable or better accuracy to a state-of-the-art deep learning method and traditional kernelised approaches for anomaly detection, while being approximately 100 times faster in training and testing.

Managing uncertainty to improve the cost performance of complex infrastructure projects

There has been a recent spate of high profile infrastructure cost overruns in Australia and internationally. This is just the tip of a longer-term and more deeply-seated problem with initial budget estimating practice, well recognised in both academic research and industry reviews: the problem of uncertainty. A case study of the Sydney Opera House is used to identify and illustrate the key causal factors and system dynamics of cost overruns. It is conventionally the role of risk management to deal with such uncertainty, but the type and extent of the uncertainty involved in complex projects is shown to render established risk management techniques ineffective. This paper considers a radical advance on current budget estimating practice which involves a particular approach to statistical modelling complemented by explicit training in estimating practice. The statistical modelling approach combines the probability management techniques of Savage, which operate on actual distributions of values rather than flawed representations of distributions, and the data pooling technique of Skitmore, where the size of the reference set is optimised. Estimating training employs particular calibration development methods pioneered by Hubbard, which reduce the bias of experts caused by over-confidence and improve the consistency of subjective decision-making. A new framework for initial budget estimating practice is developed based on the combined statistical and training methods, with each technique being explained and discussed.

Prediction of Candidate Primary Immunodeficiency Disease Genes Using a Support Vector Machine Learning Approach

Screening and early identification of primary immunodeficiency disease (PID) genes is a major challenge for physicians. Many resources have catalogued molecular alterations in known PID genes along with their associated clinical and immunological phenotypes. However, these resources do not assist in identifying candidate PID genes. We have recently developed a platform designated Resource of Asian PDIs, which hosts information pertaining to molecular alterations, protein-protein interaction networks, mouse studies and microarray gene expression profiling of all known PID genes. Using this resource as a discovery tool, we describe the development of an algorithm for prediction of candidate PID genes. Using a support vector machine learning approach, we have predicted 1442 candidate PID genes using 69 binary features of 148 known PID genes and 3162 non-PID genes as a training data set. The power of this approach is illustrated by the fact that six of the predicted genes have recently been experimentally confirmed to be PID genes. The remaining genes in this predicted data set represent attractive candidates for testing in patients where the etiology cannot be ascribed to any of the known PID genes.

Development and evaluation of a spatial decision support system for malaria elimination in Bhutan

Background: Bhutan has reduced its malaria incidence significantly in the last 5 years, and is aiming for malaria elimination by 2016. To assist with the management of the Bhutanese malaria elimination programme a spatial decision support system (SDSS) was developed. The current study aims to describe SDSS development and evaluate SDSS utility and acceptability through informant interviews. Methods: The SDSS was developed based on the open-source Quantum geographical information system (QGIS) and piloted to support the distribution of long-lasting insecticidal nets (LLINs) and indoor residual spraying (IRS) in the two sub-districts of Samdrup Jongkhar District. It was subsequently used to support reactive case detection (RACD) in the two sub-districts of Samdrup Jongkhar and two additional sub-districts in Sarpang District. Interviews were conducted to ascertain perceptions on utility and acceptability of 11 informants using the SDSS, including programme and district managers, and field workers. Results: A total of 1502 households with a population of 7165 were enumerated in the four sub-districts, and a total of 3491 LLINs were distributed with one LLIN per 1.7 persons. A total of 279 households representing 728 residents were involved with RACD. Informants considered that the SDSS was an improvement on previous methods for organizing LLIN distribution, IRS and RACD, and could be easily integrated into routine malaria and other vector-borne disease surveillance systems. Informants identified some challenges at the programme and field level, including the need for more skilled personnel to manage the SDSS, and more training to improve the effectiveness of SDSS implementation and use of hardware. Conclusions: The SDSS was well accepted and informants expected its use to be extended to other malaria reporting districts and other vector-borne diseases. Challenges associated with efficient SDSS use included adequate skills and knowledge, access to training and support, and availability of hardware including computers and global positioning system receivers.