This unit provides students with a unique opportunity to gain research experience and deep insight into the future of learning. Students will conduct research on emerging trends in this area together with experts from a wide range of disciplines. Through a series of online discussions and face-to-face seminars, students will expand their understanding of how innovations from diverse disciplinary fields-such as Information Technology, Psychology, Neuroscience, Architecture, Anthropology, and Economics-contribute to new ways of learning across formal and informal educational settings as well as organisational contexts. The work produced will contribute to the Centre for Research on Learning and Innovation's report on recent innovations and opportunities for improving teaching and learning titled 'Re-imagining the future of learning'.

This unit is designed for honours and 3rd year students to explore the use of data and data science techniques, in developing and structuring a complex research problem. Students will be part of a multi-disciplinary team that is a partnership between students who are domain specialists and those who are data scientists. Research problems will be chosen to span the space of data-driven discovery. These problems will have the following features (1) The complexity of the problem is always greater than the amount of available data (2) Data is varied in type and comes from heterogeneous sources (3) The problem has the potential for impact. Examples could include what factors predict youth disengagement, How effective was a policy in bringing about change, What lies underneath Australia and what implication does this have for resource discovery?, The obesity epidemic, Are humans more ethical than algorithms. The Centre of Translational Data Science has many such projects, students will be able to choose one of these problem, or to propose a problem of relevance and interest to them. Within these broad areas students will learn to develop a specific research problem, by building data-centric, predictive and testable models of the phenomenon. They will learn how to discover by being specific. Students will be required to outline how they might generalize the ideas in their specific problem to a larger class of problem, and so recognise that research problems in diverse domains, which differ widely in surface characteristics, can have similar structure. Participation in this unit will require students to submit an application. Where appropriate, and with the approval of the relevant faculty, this unit may be counted as a selective for a major.