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Australian State/Territory : QLD
Status : Active
Australian State/Territory : ACT
Research Topic : learning difficulty
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  • Active Funded Activity

    Linkage Projects - Grant ID: LP210301171

    Funder
    Australian Research Council
    Funding Amount
    $544,236.00
    Summary
    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.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP220101336

    Funder
    Australian Research Council
    Funding Amount
    $488,142.00
    Summary
    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.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP200103760

    Funder
    Australian Research Council
    Funding Amount
    $405,000.00
    Summary
    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.
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