Observe, Reflect, Improve: a tool to enrich Children’s Learning (ORICL). This project aims to address long-standing concerns about the quality of education and care for children during their critical first two years. It will introduce a promising, future-focused digital tool, co-designed with practitioners and providers of early childhood services, to support infant-toddler educators’ planning and practice. Building on ground-breaking pilot work, we will undertake a national implementation and e ....Observe, Reflect, Improve: a tool to enrich Children’s Learning (ORICL). This project aims to address long-standing concerns about the quality of education and care for children during their critical first two years. It will introduce a promising, future-focused digital tool, co-designed with practitioners and providers of early childhood services, to support infant-toddler educators’ planning and practice. Building on ground-breaking pilot work, we will undertake a national implementation and evaluation of the Observe, Reflect and Improve Children’s Learning (ORICL) tool. Expected outcomes include: enhanced pedagogical practices; enriched learning experiences for children birth-two; effective communication with families; and improved resourcing for providers of early childhood education and care services. Read moreRead less
Building on rural knowledges to unlock the potential of rural students. This project aims to advance understanding of the distinctive knowledges that rural students bring to school and develop teaching practices that build on these rural knowledges to unlock the potential of this significant student population. The project involves collaborating with rural primary schools, teachers, students and communities to identify rural knowledges, study classroom practices in detail, and develop sustainabl ....Building on rural knowledges to unlock the potential of rural students. This project aims to advance understanding of the distinctive knowledges that rural students bring to school and develop teaching practices that build on these rural knowledges to unlock the potential of this significant student population. The project involves collaborating with rural primary schools, teachers, students and communities to identify rural knowledges, study classroom practices in detail, and develop sustainable teaching practices that help students connect rural knowledges and school knowledge. Expected outcomes include a framework of place-based teaching practices and resources that will benefit rural schooling, teacher education, and the education of communities crucial to the nation’s future wealth and welfare.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE200100953
Funder
Australian Research Council
Funding Amount
$413,013.00
Summary
Engaging Rural Knowledges in Education for Sustainable Community Futures. This project aims to enhance understanding of the existing and unique knowledges in rural, regional and remote communities and how these can be used to enhance achievement in contemporary, metropolitan-focused, education. The project will generate new understandings about rural knowledge, related influences upon student curriculum access and achievement, and how rural, regional and remote communities understand educational ....Engaging Rural Knowledges in Education for Sustainable Community Futures. This project aims to enhance understanding of the existing and unique knowledges in rural, regional and remote communities and how these can be used to enhance achievement in contemporary, metropolitan-focused, education. The project will generate new understandings about rural knowledge, related influences upon student curriculum access and achievement, and how rural, regional and remote communities understand educational success. Through detailed analysis of data from systems, community focus groups, and school case-studies in six rural, regional and remote communities this project expects to advance knowledge to help make education better meet the needs of rural, regional and remote communities. Read moreRead less
Historical frontier violence: drivers, legacy and the role of truth-telling. This project aims to build data to identify the historical factors that incited frontier violence; quantify the legacy on communities today and conduct fieldwork to understand how historical trauma is transmitted across generations. This project expects to develop new knowledge on the circumstances and legacy of settlement and the origins of gaps in life prospects between Indigenous and non-Indigenous Australians. Our e ....Historical frontier violence: drivers, legacy and the role of truth-telling. This project aims to build data to identify the historical factors that incited frontier violence; quantify the legacy on communities today and conduct fieldwork to understand how historical trauma is transmitted across generations. This project expects to develop new knowledge on the circumstances and legacy of settlement and the origins of gaps in life prospects between Indigenous and non-Indigenous Australians. Our expectation is that this will increase public acceptance of the circumstances of settlement and the need to make amends. This project should help increase public support for truth-telling and better relations between Indigenous and non-Indigenous Australians, a vital step towards reconciliation and healing the nation. Read moreRead less
Quantum-Inspired Machine Learning. This project aims to develop new machine learning techniques based around the close correspondence between
neural networks used in deep learning, and tensor networks used in quantum physics. Tensor networks are a form of information compression that is useful in machine learning to construct a compact representation of a large data set in a way that is more amenable to understanding the internal structure than a deep neural network. Expected outcomes of this pr ....Quantum-Inspired Machine Learning. This project aims to develop new machine learning techniques based around the close correspondence between
neural networks used in deep learning, and tensor networks used in quantum physics. Tensor networks are a form of information compression that is useful in machine learning to construct a compact representation of a large data set in a way that is more amenable to understanding the internal structure than a deep neural network. Expected outcomes of this project include more resilient algorithms for machine learning, and new ways to represent quantum states that will impact fundamental physics. The resulting benefits include enhanced capacity for cross-discipline collaboration, and improved methods for future industrial applications.
Read moreRead less