Exploiting Geometries of Learning for Fast, Adaptive and Robust AI. This project aims to uniquely exploit geometric manifolds in deep learning to advance the frontier of Artificial Intelligence (AI) research and applications in cybersecurity and general cognitive tasks. It expects to develop new theories, algorithms, tools, and technologies for machine learning systems that are fast, adaptive, lifelong and robust, even with limited supervision. Expected outcomes will enhance Australia's capabili ....Exploiting Geometries of Learning for Fast, Adaptive and Robust AI. This project aims to uniquely exploit geometric manifolds in deep learning to advance the frontier of Artificial Intelligence (AI) research and applications in cybersecurity and general cognitive tasks. It expects to develop new theories, algorithms, tools, and technologies for machine learning systems that are fast, adaptive, lifelong and robust, even with limited supervision. Expected outcomes will enhance Australia's capability and competitiveness in AI, and deliver robust and trustworthy learning technology. The project should provide significant benefits not only in advancing scientific and translational knowledge but also in accelerating AI innovations, safeguarding cyberspace, and reducing the burden on defence expenses in Australia.Read moreRead less
The Benefits of Utilising Visual-Spatial Representations of Numbers . The aim of this project is to investigate how visual-spatial representations of numbers enhance practice to promote the use of retrieval-based over counting-based strategies for children learning early arithmetic. About one-third of Australian children stay reliant on counting strategies for basic arithmetic, despite these being associated with lower achievement in mathematics in later years. Expected outcomes of this project ....The Benefits of Utilising Visual-Spatial Representations of Numbers . The aim of this project is to investigate how visual-spatial representations of numbers enhance practice to promote the use of retrieval-based over counting-based strategies for children learning early arithmetic. About one-third of Australian children stay reliant on counting strategies for basic arithmetic, despite these being associated with lower achievement in mathematics in later years. Expected outcomes of this project are new understandings of how problem-answer associations can be strengthened in memory and the development of tools to promote retrieval-based strategies. Potential benefits include children who are better prepared to take on higher-level mathematics in secondary school and, subsequently, more numerate citizens. Read moreRead less
Modelling complex learning spaces. The growing use of digital tools and resources means that students' learning activities are no longer tied to unique physical places. Their work is distributed across increasingly complex mixtures of physical and digital spaces, which both shape and are shaped by students' activity. This project aims to identify productive ways of modelling the characteristics and uses of complex learning spaces in higher education. Evidence and models generated by the project ....Modelling complex learning spaces. The growing use of digital tools and resources means that students' learning activities are no longer tied to unique physical places. Their work is distributed across increasingly complex mixtures of physical and digital spaces, which both shape and are shaped by students' activity. This project aims to identify productive ways of modelling the characteristics and uses of complex learning spaces in higher education. Evidence and models generated by the project aim to strengthen the logic connecting the use, management and design of learning spaces. A better understanding of the relations between pedagogy, activity and space will improve the work of architects and other designers, campus managers, university teachers and students themselves.Read moreRead less
Does a teacher-led mindfulness intervention improve student outcomes? This project aims to determine if improving teacher knowledge and practice of mindfulness in the classroom, can lead to better child attention and school functioning outcomes during the early primary school years. Mindfulness is an approach that aims to improve attention, self-regulation, mental health, and cognitive functioning. Expected outcomes include new knowledge as to whether mindfulness can be integrated into classroom ....Does a teacher-led mindfulness intervention improve student outcomes? This project aims to determine if improving teacher knowledge and practice of mindfulness in the classroom, can lead to better child attention and school functioning outcomes during the early primary school years. Mindfulness is an approach that aims to improve attention, self-regulation, mental health, and cognitive functioning. Expected outcomes include new knowledge as to whether mindfulness can be integrated into classroom practice, how to best implement it, student benefits and cost-effectiveness. Findings will inform schools as to whether this approach can support students in making a positive transition to primary school that can place them on positive academic and well-being pathways and lead to benefits in their adulthood.Read moreRead less
Assessments for writing with generative artificial intelligence . This project aims to develop a novel assessment framework for writing with generative artificial intelligence—a new technology capable of producing text with humanlike fluency. This project endeavours to produce new knowledge at the intersection of learning analytics, the learning sciences, and educational technology using innovative methods for data capture and analysis. Expected outcomes of this project include the first valid, ....Assessments for writing with generative artificial intelligence . This project aims to develop a novel assessment framework for writing with generative artificial intelligence—a new technology capable of producing text with humanlike fluency. This project endeavours to produce new knowledge at the intersection of learning analytics, the learning sciences, and educational technology using innovative methods for data capture and analysis. Expected outcomes of this project include the first valid, feasible, and reliable framework for assessing writing composed with the help of artificial intelligence. This should provide significant benefits to (a) writing assessment in higher education, (b) student learning, and (c) our understanding of collaborations between humans and artificial intelligence.Read moreRead less
Data analytics-based tools and methods to enhance self-regulated learning. This project aims to develop student self-regulated learning skills by harnessing the potential of Big Data analytics. The project expects to generate new knowledge at the intersection of learning analytics, educational technology, learning sciences and teaching practice resulting from novel data collection and analysis tools and methods. The outputs are expected to include insights into metacognitive, motivational, and t ....Data analytics-based tools and methods to enhance self-regulated learning. This project aims to develop student self-regulated learning skills by harnessing the potential of Big Data analytics. The project expects to generate new knowledge at the intersection of learning analytics, educational technology, learning sciences and teaching practice resulting from novel data collection and analysis tools and methods. The outputs are expected to include insights into metacognitive, motivational, and technical issues facing analytics-based personalised feedback. The outcomes are intended to offer benefits for developing pedagogical and the design of educational technology. The outcomes can result in improved student learning outcomes in higher education to ensure graduates are prepared for the digital economy.Read moreRead less
Causal Knowledge-Empowered Adaptive Federated Learning. Federated learning tools are a promising framework for collaborative machine learning (ML) that also maintain data privacy; however, their ability to model heterogeneous data remains a key challenge. This project aims to develop a new learning scheme for coordinated training of ML models that successfully bridges variable data distributions. The framework proposed will be the first globally that can use causal knowledge to 1) handle data he ....Causal Knowledge-Empowered Adaptive Federated Learning. Federated learning tools are a promising framework for collaborative machine learning (ML) that also maintain data privacy; however, their ability to model heterogeneous data remains a key challenge. This project aims to develop a new learning scheme for coordinated training of ML models that successfully bridges variable data distributions. The framework proposed will be the first globally that can use causal knowledge to 1) handle data heterogeneity across devices and 2) address the real-world challenges when only a subset of devices have labelled data. Expected outcomes and benefits include the theoretical underpinnings and algorithms of causality-based collaborative training of ML models while better preserving the users’ data privacy.Read moreRead less
Teaching how to learn: promoting self-regulated learning in STEM classes. This project aims to investigate key factors that influence change in teacher practices and student achievement in Science, Technology, Engineering and Mathematics (STEM). It will involve the development and evaluation of interventions designed to help teachers create learning environments that promote student engagement and the development of the cognitive and metacognitive skills needed for success in STEM. The project w ....Teaching how to learn: promoting self-regulated learning in STEM classes. This project aims to investigate key factors that influence change in teacher practices and student achievement in Science, Technology, Engineering and Mathematics (STEM). It will involve the development and evaluation of interventions designed to help teachers create learning environments that promote student engagement and the development of the cognitive and metacognitive skills needed for success in STEM. The project will advance our understanding of how to increase the quality of teaching and learning in STEM subjects. Improving teacher capacity and student performance in STEM is a national priority with significant social and economic benefits to Australia.Read moreRead less
Advanced Machine Learning with Bilevel Optimization. There is an urgent need to develop a new machine learning (ML) paradigm that can overcome data-privacy and model-size constraints in real-world applications. This project aims to develop an advanced paradigm of ML with bilevel optimisation, called bilevel ML. A theoretically-guaranteed fast approximate solver and a new fuzzy bilevel learning framework will be developed to achieve the aim in complex situations; a methodology to transfer knowled ....Advanced Machine Learning with Bilevel Optimization. There is an urgent need to develop a new machine learning (ML) paradigm that can overcome data-privacy and model-size constraints in real-world applications. This project aims to develop an advanced paradigm of ML with bilevel optimisation, called bilevel ML. A theoretically-guaranteed fast approximate solver and a new fuzzy bilevel learning framework will be developed to achieve the aim in complex situations; a methodology to transfer knowledge and an approach to fast-adapt bilevel optimization solutions when required computing resources change. The anticipated outcomes should significantly improve the reliability of ML with benefits for safety learning and computing resource optimisation in ML-based data analytics.Read moreRead less
Understanding Growth in Emotion Regulatory Flexibility in Emerging Adults. Emerging adults (ages 18-25) are now facing unparalleled social and technological change and the on-going effects of the COVID-19 pandemic. Such demands can be overwhelming and undermine engagement with education and employment, with serious impacts for the individual and society. At the same time, our novel model proposes that the diverse daily adult-like stressors that characterise emerging adulthood can also drive grow ....Understanding Growth in Emotion Regulatory Flexibility in Emerging Adults. Emerging adults (ages 18-25) are now facing unparalleled social and technological change and the on-going effects of the COVID-19 pandemic. Such demands can be overwhelming and undermine engagement with education and employment, with serious impacts for the individual and society. At the same time, our novel model proposes that the diverse daily adult-like stressors that characterise emerging adulthood can also drive growth in flexible emotion regulation when combined with reflection on, and insight into, their own coping processes. Our research expands scientific knowledge by taking the first steps to uncover why some emerging adults increase their ability to flexibly regulate their emotions over this period, whereas others fail to do so.Read moreRead less