Currently Funded Projects
ID14-3821:Enabling connected learning via open source analytics in the wild: learning analytics beyond the LMS
Office for Learning and Teaching, $320,000 over 2 years (2015-2017)
Lead institution: Queensland University of Technology
Partner institutions: University of South Australia; University of Technology, Sydney; University of Sydney, University of Texas, Arlington
Team: Kirsty Kitto (Lead), Mandy Lupton, John Banks, Dann Mallet, Peter Bruza (QUT)
Shane Dawson, Dragan Gasevic (UniSA); Simon Buckingham Shum (UTS); Abelardo Pardo (Uni Sydney); George Siemens (Uni Texas, Arlington).
This project aims to improve the quality of student engagement and learning in collaborative online environments by incorporating and analysing social media platforms that students already use. It will create an easy to use and open source Connected Learning Analytics (CLA) toolkit utilising the latest mathematical and computational approaches, grounded in connected learning pedagogy.
Keywords: Social Learning Analytics, Connected learning, Semantic Technologies, xAPI, mathematical modelling
DP1094974: Generalised quantum models of complexity with application to cognitive systems
Dr KJ Kitto (APD); Prof PD Bruza
ARC Discovery Projects, $270,000 over three years (2010-2012)
Primary RFCD: 2801 INFORMATION SYSTEMS
Non-separable systems surround us. Our transportation, taxation, schooling, environmental and social policies are all interrelated, and it is increasingly recognised that we cannot consider them in isolation. Such systems are generally deemed complex, and it is often impossible to separate them from one another. Despite this, many of our most advanced modelling techniques are grounded in principles of separability and non-contextuality. This project aims to develop a radically new theory for modelling complex systems. It will provide a significant advance upon current methods by developing novel methods for modelling non-separable systems. The innovation in this project derives from exploiting quantum theory to produce this new genre of modelling tools. The theory will be applied to cognitive systems to underpin frontier information technologies more in tune with humans. More generally the research will open new doors to the modelling and understanding of all complex systems.
QONTEXT: Quantum Therory of Context Representation for Information Access and Retrieval
FP7, Marie Curie Actions -International Research Staff Exchange Scheme (IRSES) EU208,800
Australian Academy of Science funded AU$15,000 for Australian team participation, with QUT supplying matching funds ($30,000 total)
The huge number and diversity of the users, the advertising products and services, the rapid growth of online resources have imposed new challenges to conventional search. Queries are becoming even more broad and complex essentially due to context of the search process (e.g. the system, user, language, the word and action meaning, socio-psychological dimensions, interface and interaction methods). Over the past three decades, research in Information Access and Retrieval (IAR) had led to various search engine models, such as vector space and probabilistic models. Unfortunately, there has been no comprehensive investigation at the theoretical level for effectively integrating elements of context to create advanced search technology. The key issue preventing such research is a lack of a unified theoretical framework to seamlessly integrate the dimensions of context into the search engine models and into the evaluation protocols.
This project is based on the belief that the dimensions of context can be naturally integrated into a generic and fundamental framework. To address the challenges of the dimensions of context in IAR this proposal shows a new vision of the IAR paradigm based on Quantum Theory (QT). This project starts from van Rijsbergen's seminal book. QT allows to measure relevance and context via projection, and probability of relevance via the trace, to logically reason through lattice of document structures and links, to change context via unitary operators, to handle correlations dependencies as density operators, to represent composite, entangled documents and features for which classical correlations cannot straightforwardly be used. The work independently done so far for a number of years by the partners suggests that more effective results can be obtained only if the expertises are exchanged through a network which would allow the partners to work together and exchange a wide range of expertises, which is currently hardly possessed by any of the single team.