Optimising students’ academic trajectories: The role of growth (‘personal best’) goals. Too many students fail to reach their academic potential and, as a result, they risk being systematically denied a sense of academic ‘success’ and progress. Through a focus on academic growth (and ‘personal bests’), this research project traverses complex terrain to identify the role of growth goals and growth goal setting in students’ academic trajectories. It also tackles methodological challenges that have ....Optimising students’ academic trajectories: The role of growth (‘personal best’) goals. Too many students fail to reach their academic potential and, as a result, they risk being systematically denied a sense of academic ‘success’ and progress. Through a focus on academic growth (and ‘personal bests’), this research project traverses complex terrain to identify the role of growth goals and growth goal setting in students’ academic trajectories. It also tackles methodological challenges that have impeded research progress in this compelling area. Through strategic international and institutional links, the research program will identify innovative approaches to academic growth and growth goals that will significantly assist pedagogy and psychology aimed at optimising students’ academic potential.Read moreRead less
Transforming primary teachers' representational practices: effects on students' scientific reasoning and discourse within contemporary sciences. Training teachers to appropriately represent and communicate scientific information is critically important for promoting scientific thinking and learning in students. This research is critical to securing Australia's future interests in developing new and emerging frontier science and technologies through the engagement and retention of students.
Solving the inert knowledge problem. A central goal of education is for students to transfer what they learn to new contexts or problems. Indeed, expert reasoning is often characterised by seeing the deep structural commonalities across seemingly disparate situations. However, the knowledge students acquire is notoriously inert, tied to the specifics of the learning examples. This project aims to move towards solving 'the inert knowledge problem' by investigating how humans learn concepts define ....Solving the inert knowledge problem. A central goal of education is for students to transfer what they learn to new contexts or problems. Indeed, expert reasoning is often characterised by seeing the deep structural commonalities across seemingly disparate situations. However, the knowledge students acquire is notoriously inert, tied to the specifics of the learning examples. This project aims to move towards solving 'the inert knowledge problem' by investigating how humans learn concepts defined by abstract relational structure, and by designing educational applications that enhance the use of relational learning mechanisms in students with a wide range of cognitive abilities.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE150100731
Funder
Australian Research Council
Funding Amount
$361,744.00
Summary
Contextual supports for the early development of self-regulated learning. How do young children develop critical learning behaviours that are the key for their future academic success? What kinds of environments support this development? This project aims to answer these questions by investigating the development of regulatory behaviours (with a specific focus on self-regulated learning) during the first two years of schooling, and identifying critical contextual variables at home and at school ....Contextual supports for the early development of self-regulated learning. How do young children develop critical learning behaviours that are the key for their future academic success? What kinds of environments support this development? This project aims to answer these questions by investigating the development of regulatory behaviours (with a specific focus on self-regulated learning) during the first two years of schooling, and identifying critical contextual variables at home and at school impacting on this development. Findings from this research will provide crucial information for the design of family and practitioner-based interventions helping to improve the educational outcomes of young Australians.Read moreRead less
Discrimination learning in humans: Associative and attentional mechanisms. This project offers three major benefits: (1) Australian researchers excel in cognitive neuroscience, learning and psychopharmacology, areas based largely on animal models of human cognition. This project contributes to these areas by specifying the relationship between animal learning and human cognition; (2) the project enhances Australia's international reputation in these areas via its collaboration with a scientist ....Discrimination learning in humans: Associative and attentional mechanisms. This project offers three major benefits: (1) Australian researchers excel in cognitive neuroscience, learning and psychopharmacology, areas based largely on animal models of human cognition. This project contributes to these areas by specifying the relationship between animal learning and human cognition; (2) the project enhances Australia's international reputation in these areas via its collaboration with a scientist of Geoff Hall's stature; it also offers students outstanding research training and international exposure; (3) given Chris Mitchell's industry experience and the relevance of this work to advertising/marketing, this project will generate knowledge relevant to, and possible future collaborations with, Australian industries.Read moreRead less
Evaluating models of category learning that use general feature-based representations. Three competing models of human category learning will be evaluated by comparing their behaviour to human performance on an experimental task where each model makes qualitatively different predictions. A series of theoretical and algorithmic advances will be undertaken to ensure each of the category learning models uses the same feature-based representation. Because the three models propose very different lear ....Evaluating models of category learning that use general feature-based representations. Three competing models of human category learning will be evaluated by comparing their behaviour to human performance on an experimental task where each model makes qualitatively different predictions. A series of theoretical and algorithmic advances will be undertaken to ensure each of the category learning models uses the same feature-based representation. Because the three models propose very different learning processes, their comparison will give insight into the basic cognitive process of categorisation. The algorithms for generating feature representations and modelling human category learning will also have potential for application in data visualisation and information handling systems.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
Deep Adder Networks on Edge Devices. This project aims to empower edge devices with intelligence by developing advanced deep neural networks that address the conflict between the high resource requirements of deep learning and the generally inadequate performance of the edge. Multiplication has been the dominant type of operation in deep learning, though the addition is known to be much cheaper. This project expects to yield theories and algorithms that allow deep neural networks consisting of n ....Deep Adder Networks on Edge Devices. This project aims to empower edge devices with intelligence by developing advanced deep neural networks that address the conflict between the high resource requirements of deep learning and the generally inadequate performance of the edge. Multiplication has been the dominant type of operation in deep learning, though the addition is known to be much cheaper. This project expects to yield theories and algorithms that allow deep neural networks consisting of nearly pure additions to fulfil the requisites of accuracy, robustness, calibration and generalisation in real-world computer vision tasks. The success of this project will benefit deep learning-based products on smartphones or robots in health and cybersecurity.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE230101591
Funder
Australian Research Council
Funding Amount
$419,154.00
Summary
Towards Real-world Continual Learning on Unrestricted Task Steams. This project aims to enable machines to continually learn without forgetting and accumulate knowledge from the sequential data streams containing diverse tasks. This project expects to advance the continual learning to unrestricted real-world task steams that are long-term and complex and promote artificial intelligence toward the human-level intelligence that can automatically evolve during interaction with the world. Expected o ....Towards Real-world Continual Learning on Unrestricted Task Steams. This project aims to enable machines to continually learn without forgetting and accumulate knowledge from the sequential data streams containing diverse tasks. This project expects to advance the continual learning to unrestricted real-world task steams that are long-term and complex and promote artificial intelligence toward the human-level intelligence that can automatically evolve during interaction with the world. Expected outcomes of this project include the paradigm-shifting continual learning framework and techniques for handling unrestricted task steams in real-world scenarios. They will benefit society and the economy nationally and internationally by enhancing the applicability of artificial intelligence.Read 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