Early career teacher induction: Supporting precarious teachers. This project aims to investigate the ways in which Australian induction policies support precariously employed early career teachers to effectively manage student classroom behaviour. This project expects to generate new knowledge of workforce development and induction experiences of early career teachers employed on casual and short-term contracts. Expected outcomes of this project include alternative policy and practice recommenda ....Early career teacher induction: Supporting precarious teachers. This project aims to investigate the ways in which Australian induction policies support precariously employed early career teachers to effectively manage student classroom behaviour. This project expects to generate new knowledge of workforce development and induction experiences of early career teachers employed on casual and short-term contracts. Expected outcomes of this project include alternative policy and practice recommendations to support the transition of insecure replacement teachers within the profession. The benefits of this research include, improving teachers’ classroom management practices; the retention of new teachers; improving teacher workforce development; and building a healthier education system. Read moreRead less
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE160100090
Funder
Australian Research Council
Funding Amount
$250,000.00
Summary
Computational infrastructure for developing deep machine learning models. Computational infrastructure for developing deep machine learning models:
The computational infrastructure for developing deep machine learning models aims to enable new developments in machine learning of deep neural network models by providing the specialised computing necessary to train and evaluate the networks. In the last three years, deep networks have smashed previous performance ceilings for tasks such as object ....Computational infrastructure for developing deep machine learning models. Computational infrastructure for developing deep machine learning models:
The computational infrastructure for developing deep machine learning models aims to enable new developments in machine learning of deep neural network models by providing the specialised computing necessary to train and evaluate the networks. In the last three years, deep networks have smashed previous performance ceilings for tasks such as object recognition in images, speech recognition and automatic translation, bringing the prospect of machine intelligence closer than ever. Modern machine learning techniques have had huge impact in the last decade in fields such as robotics, computer vision and data analytics. The facility would enable Australian researchers to develop, learn and apply deep networks to problems of national importance in robotic vision and big data analytics. Read moreRead less
Pursuing equity in high poverty rural schools: improving learning through rich accountabilities. Poor performance of students in schools located in high poverty communities is a pressing educational problem for Australia, with educational disadvantage in poor rural communities in particular demanding amelioration. The evidence suggests the equity and quality of schooling outcomes are centrally important to the nation's economic future, the strength of Australian democracy, social inclusion and a ....Pursuing equity in high poverty rural schools: improving learning through rich accountabilities. Poor performance of students in schools located in high poverty communities is a pressing educational problem for Australia, with educational disadvantage in poor rural communities in particular demanding amelioration. The evidence suggests the equity and quality of schooling outcomes are centrally important to the nation's economic future, the strength of Australian democracy, social inclusion and a unified nation. In strengthening policy and practice knowledge about educative usage of performance data and the development of rich forms of accountability, the research will advance the academic literature and provide an evidence base for success of the national partnership on low socio-economic status schools.Read moreRead less
ARC Centre of Excellence for Robotic Vision. Robots are vital to Australia's future prosperity in the face of high relative wages, low or decreasing productivity, and impending labour shortages. However the work and workplaces of our most important industries are unstructured and changeable and current robots are challenged by their inability to quickly, safely and reliably "see" and "understand" what is around them. The Centre's research will create the fundamental science and technologies th ....ARC Centre of Excellence for Robotic Vision. Robots are vital to Australia's future prosperity in the face of high relative wages, low or decreasing productivity, and impending labour shortages. However the work and workplaces of our most important industries are unstructured and changeable and current robots are challenged by their inability to quickly, safely and reliably "see" and "understand" what is around them. The Centre's research will create the fundamental science and technologies that will allow robots to see as we do, and overcome the last barrier to the ubiquitous deployment of robots into society for the benefit of all.Read moreRead less
Industrial Transformation Research Hubs - Grant ID: IH180100002
Funder
Australian Research Council
Funding Amount
$5,000,000.00
Summary
ARC Research Hub for Driving Farming Productivity and Disease Prevention. The ARC Research Hub for Driving Farming Productivity and Disease Prevention aims to increase farm production and disease prevention through advancing and transferring new artificial intelligence technologies into industrial deployment. The Hub will combine machine vision, machine learning, software quality control, engineering, biology, and farming industries to develop technologies to build more intelligent systems. Thes ....ARC Research Hub for Driving Farming Productivity and Disease Prevention. The ARC Research Hub for Driving Farming Productivity and Disease Prevention aims to increase farm production and disease prevention through advancing and transferring new artificial intelligence technologies into industrial deployment. The Hub will combine machine vision, machine learning, software quality control, engineering, biology, and farming industries to develop technologies to build more intelligent systems. These dynamic systems will help determine what goal to achieve and the most efficient plan to achieve it. This Hub is expected to contribute to higher farming efficiency, lower production costs and fewer disease risks, giving the Australian industry new business opportunities and an international competitive advantage.Read moreRead less
Improving student outcomes: coaching teachers in the power of feedback. This project aims to investigate how student outcomes can be augmented through coaching teachers in effective feedback practice. The project addresses a critical problem of stagnating levels of student achievement in Australian schools with the innovative research design combining evidence-based, pedagogies of feedback, formative assessment and instructional coaching to improve teacher practice and ultimately raise student a ....Improving student outcomes: coaching teachers in the power of feedback. This project aims to investigate how student outcomes can be augmented through coaching teachers in effective feedback practice. The project addresses a critical problem of stagnating levels of student achievement in Australian schools with the innovative research design combining evidence-based, pedagogies of feedback, formative assessment and instructional coaching to improve teacher practice and ultimately raise student achievement levels. The project aims to guide policy implementation in pedagogy to raise the quality of teaching standards and to improve learning outcomes for Australian students. Ultimately, outcomes from the research will help close the gap for low achieving students, and challenge and extend those who may already be meeting required benchmarks. Read moreRead less
ARC/NHMRC Research Network in Genes and Environment in Development. Interactions between the early environment and the genetic regulatory program of the developing organism have major consequences for the lifetime health of individuals. The primary objective of the Network in Genes and Environment in Development is to harness the resources of leading researchers from the currently distinct disciplines of developmental biology and developmental physiology to define key developmental regulatory ne ....ARC/NHMRC Research Network in Genes and Environment in Development. Interactions between the early environment and the genetic regulatory program of the developing organism have major consequences for the lifetime health of individuals. The primary objective of the Network in Genes and Environment in Development is to harness the resources of leading researchers from the currently distinct disciplines of developmental biology and developmental physiology to define key developmental regulatory networks and to address how environmental factors impinge on these regulatory networks. The formation of this National Research Network is unique, timely and strategic. It will generate new insights into the mechanisms by which events in early life determine the risk of adverse outcomes in perinatal and adult life.Read moreRead less
Artificial intelligence in education: Democratising policy. The rapid introduction of artificial intelligence into education is occurring with inadequate policy support. Additionally, there is a lack of stakeholder input into decisions about the use of AI in education. Utilising social science and data science approaches, this project aims to democratise policy about AI in education by building tools to monitor policies, and developing collaborative policy making methods. The expected outcomes i ....Artificial intelligence in education: Democratising policy. The rapid introduction of artificial intelligence into education is occurring with inadequate policy support. Additionally, there is a lack of stakeholder input into decisions about the use of AI in education. Utilising social science and data science approaches, this project aims to democratise policy about AI in education by building tools to monitor policies, and developing collaborative policy making methods. The expected outcomes include publicly available policy resources to anticipate, and respond to, the role of AI in education, and participatory frameworks for policy making. The benefits include informed stakeholder engagement, and concrete policy recommendations that are globally relevant and adaptable to the Australian context.Read moreRead less
Strengthening Relationships for Young People in Residential Care. Young people in residential care face major challenges in forming positive relationships, many having experienced adults as a source of threat rather than safety. This project aims to investigate practices within therapeutic residential care that enable or limit young people’s identity formation, positive social connections, safety and wellbeing. This research will generate nuanced knowledge informing interpersonal and institution ....Strengthening Relationships for Young People in Residential Care. Young people in residential care face major challenges in forming positive relationships, many having experienced adults as a source of threat rather than safety. This project aims to investigate practices within therapeutic residential care that enable or limit young people’s identity formation, positive social connections, safety and wellbeing. This research will generate nuanced knowledge informing interpersonal and institutional change. Expected outcomes include improved approaches to therapeutic care and to methods for enabling the participation of young people in care in matters that may change their life trajectory on exiting care. Expected benefits include more responsive policies and frameworks for practice.Read moreRead less
What Cost-effective Built Environment Interventions Would Create Healthy, Liveable And Equitable Communities In Australia, And What Would Facilitate These Being Translated Into Policy And Practice?
Funder
National Health and Medical Research Council
Funding Amount
$2,658,832.00
Summary
This CRE involves collaboration between a multi-disciplinary research team across Australia working with policy-makers covering planning, urban design, transport planning and health. It will identify the most cost-effective built environment interventions required to create healthy, liveable, and equitable communities. Factors that influence research findings being translated into urban planning policy and practice will be examined and tools to assist changes to policy and practice developed.