Tracking towards a complete model of skilled reading comprehension. This project aims to promote the development of the first complete computational model of reading comprehension. Many computational models of sub-components of reading have been developed, but none fully explain the complex co-ordination of perceptual, attentional and cognitive processes required for successful comprehension. The project intends to use eye tracking studies to test and refine Über-Reader, a new computational mode ....Tracking towards a complete model of skilled reading comprehension. This project aims to promote the development of the first complete computational model of reading comprehension. Many computational models of sub-components of reading have been developed, but none fully explain the complex co-ordination of perceptual, attentional and cognitive processes required for successful comprehension. The project intends to use eye tracking studies to test and refine Über-Reader, a new computational model that aims to provide a complete account of the memory systems and cognitive processes involved in reading comprehension and how they differ with reading skill. The outcomes will advance understanding of the causes of success and failure in reading and contribute to diagnosing and remediating reading difficulties.Read moreRead less
Using cognitive models to understand memorability of real world images. This proposal aims to understand and make predictions about which real world images -- specifically living things, objects, and human faces -- that people will remember remember via an integration of cognitive models of memory and machine learning techniques. Computer vision models and similarity scaling techniques will be used to produce psychological representations of the images. These representations will then be integra ....Using cognitive models to understand memorability of real world images. This proposal aims to understand and make predictions about which real world images -- specifically living things, objects, and human faces -- that people will remember remember via an integration of cognitive models of memory and machine learning techniques. Computer vision models and similarity scaling techniques will be used to produce psychological representations of the images. These representations will then be integrated with cognitive models of memory, which predict that images are more likely to be recognized if they are similar to each of the representations in memory. Large scale memory and similarity rating datasets will be used to develop and test the model.Read moreRead less
Excellent researchers: Using learner profiles to enhance research learning. Recent evidence concerning metacognitive learning and affect reveals that research degree candidates are a diverse group of learners, and little is known about explaining wasteful attrition, stress and delays in progress. Such a study is essential, especially given the growth in research degrees, new transitional pathways, diversity in candidate backgrounds and chronic high attrition. This longitudinal study applies new ....Excellent researchers: Using learner profiles to enhance research learning. Recent evidence concerning metacognitive learning and affect reveals that research degree candidates are a diverse group of learners, and little is known about explaining wasteful attrition, stress and delays in progress. Such a study is essential, especially given the growth in research degrees, new transitional pathways, diversity in candidate backgrounds and chronic high attrition. This longitudinal study applies new findings about doctoral learning profiles in a direct intervention (DOCLearnPro) that targets individual differences across students in doctoral and master’s degrees to improve learning outcomes significantly and contribute theoretically, methodologically and substantively in order to advance understanding of researcher development.Read moreRead less
Toward Human-guided Safe Reinforcement Learning in the Real World. This project aims to investigate human-guided safe reinforcement learning (RL). Safe RL is an important topic that could enable real applications of RL systems by addressing safety constraints. Existing safe RL assumes the availability of specified safety constraints in mathematical or logical forms. This project proposes to study learning safety objectives from information provided directly by humans or indirectly via language m ....Toward Human-guided Safe Reinforcement Learning in the Real World. This project aims to investigate human-guided safe reinforcement learning (RL). Safe RL is an important topic that could enable real applications of RL systems by addressing safety constraints. Existing safe RL assumes the availability of specified safety constraints in mathematical or logical forms. This project proposes to study learning safety objectives from information provided directly by humans or indirectly via language models, and human-guided continuous correction for safety improvements. The established theories and developed algorithms will advance frontier technologies in AI and contribute to a wide range of real applications of safe RL, such as robotics and autonomous driving, bringing enormous social and economic benefits. Read moreRead less
Developing interdisciplinary expertise in universities. This project aims to create a strong integrative research foundation to explain how university researchers and students develop the expertise needed to work in interdisciplinary teams and how this development can be enhanced. It combines three perspectives investigating: how research and innovation communities create interdisciplinary knowledge, how interdisciplinary teams learn to function effectively and the personal resourcefulness that ....Developing interdisciplinary expertise in universities. This project aims to create a strong integrative research foundation to explain how university researchers and students develop the expertise needed to work in interdisciplinary teams and how this development can be enhanced. It combines three perspectives investigating: how research and innovation communities create interdisciplinary knowledge, how interdisciplinary teams learn to function effectively and the personal resourcefulness that enables individuals to participate in interdisciplinary work. The outcomes will provide a much better understanding of the qualities that help individuals and groups to work productively across disciplinary boundaries. They will be used to create better strategies for supporting interdisciplinary learningRead moreRead less
Generative Visual Pre-training on Unlabelled Big Data. This project aims to develop a generative visual pre-training of large-scale deep neural networks on unlabelled big data. Developing pre-trained visual models that are accurate, robust, and efficient for downstream tasks is a keystone of modern computer vision, but it poses challenges and knowledge gaps to existing unsupervised representation learning. Expected outcomes include new theories and algorithms for unsupervised visual pre-training ....Generative Visual Pre-training on Unlabelled Big Data. This project aims to develop a generative visual pre-training of large-scale deep neural networks on unlabelled big data. Developing pre-trained visual models that are accurate, robust, and efficient for downstream tasks is a keystone of modern computer vision, but it poses challenges and knowledge gaps to existing unsupervised representation learning. Expected outcomes include new theories and algorithms for unsupervised visual pre-training, which are anticipated to deepen our understanding of visual representation and make it easier to build and deploy computer vision applications and services. Examples of benefits include modernising machines in manufacturing and farming with visual intelligence. Read moreRead less
Raising the literacy bar for economically-disadvantaged students. This project aims to promote higher order literacy skills among economically-disadvantaged students. Higher order literacy is critical for productive engagement in academic, economic and personal spheres of life in literacy-rich knowledge economies. Opportunities for disadvantaged students to develop advanced literacy skills are limited if schools serving these students focus predominantly on basic skills training. This project ....Raising the literacy bar for economically-disadvantaged students. This project aims to promote higher order literacy skills among economically-disadvantaged students. Higher order literacy is critical for productive engagement in academic, economic and personal spheres of life in literacy-rich knowledge economies. Opportunities for disadvantaged students to develop advanced literacy skills are limited if schools serving these students focus predominantly on basic skills training. This project will investigate contradictions in policies and practices in Australia and Hong Kong to understand why and how disadvantaged students are supported or unsupported to learn higher-order literacy skills. It also explores successful practices that promote such learning, alongside basic skills, for disadvantaged students. This will provide significant benefits such as providing new conceptual understandings of the policy-practice interface and empirical evidence to inform the design of effective practices that promote higher-order literacy skills, alongside basic skills, for economically-disadvantaged students in Australia and Hong Kong.Read moreRead less
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
Language for learning: Developing learning-oriented talk in long-day-care. This study aims to identify, for the first time, key features of infant-toddler long day care (LDC) environments that support or constrain the development and use of language as a critical tool for early learning. This project expects to generate new knowledge by investigating early LDC predictors of preschool language skills, and will deliver much-needed new evidence to inform LDC pedagogy and curriculum development and ....Language for learning: Developing learning-oriented talk in long-day-care. This study aims to identify, for the first time, key features of infant-toddler long day care (LDC) environments that support or constrain the development and use of language as a critical tool for early learning. This project expects to generate new knowledge by investigating early LDC predictors of preschool language skills, and will deliver much-needed new evidence to inform LDC pedagogy and curriculum development and practice and, ultimately, to improve long term educational outcomes. This will provide significant benefits, such as improving the quality of infant-toddler LDC programs, which stands to enhance children’s learning and life-long outcomes.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