Algorithms are increasingly being used to make decisions that have a lasting impact on our current and future lives. There is a growing public awareness that algorithms, especially those used in forms of artificial intelligence, need to be understood as raising issues of fairness. But while everyone may have a vernacular understanding of what is fair or unfair, when algorithms are used numerous trade-offs are involved.
Studying science helps you make sense of the world and opens the door to a wide range of careers. If you’ve decided to be a doctor or engineer then you will already know you need to do a science. But if you’re in the 45% of students who don’t know what career they’ll end up in, you may want to study a range of different subject types to keep your options open. Science could be one of them
This question is central to work being undertaken by colleagues at UTS and internationally. Evidence and data are increasingly emphasised in educational contexts, with the spread of What Works centres such as the Educational Endowment Foundation (UK), Evidence for Learning (Australia), the What Works Clearinghouse (USA), international (PISA), national (SATS, NAPLAN, etc.
At the end of 2020, Centre researchers Simon Buckingham Shum, Kirsty Kitto, Simon Knight, and Jane Hunter took part in an Australian conference Empowering Learners for the Age of AI, organised by a consortium of universities to engage key stakeholders with the learning challenges in an age of AI, empowering learners through AI, empowering learners to use AI, and empowering learners to question and critique AI and the systems it embeds.
Our congratulations to Mary Coupland who led a successful UTS Team Teaching award for her work with a team (including Simon Knight) on developing quantitative literacy and critical thinking.
The award reflects five years of development of an undergraduate cross-faculty elective subject ‘Arguments, Evidence, and Intuition’.
Technology and learning are fundamentally entwined across our professional and civic lives, and formal education. The Centre exists to further our understanding of the dynamic relationship between technology and learning, across formal, informal, and professional education contexts throughout the lifespan.
As researchers, we care that our educational systems improve, support all learners, and are grounded solidly in research evidence. But how do we work with stakeholders like educational technology startups to support effective use of that evidence?
Whether they’re driven by commercial interests or not, most developers and companies care about positive impact. Of course, impact helps in selling products, but it’s also a key motivation in why people develop and refine technologies: they care about supporting learning.