Australia¡¯s ¡°rewriting¡± of its research assessment exercise is an?¡°amazing opportunity¡± to?reward desirable practices that have long been overlooked, Times Higher Education¡¯s World Academic Summit has heard.

Emma Johnston, deputy vice-chancellor of?research at?the University of Sydney, said national evaluation was at a?¡°very interesting experimental stage¡± following the federal government¡¯s decision to?retire the Excellence in?Research for Australia (ERA) and Engagement and Impact (EI) assessments.

She said the process to replace ERA ¨C which had been ¡°gameable¡± and ¡°very time-consuming for institutions¡± ¨C was in its early stages and ¡°a?bit of a black box¡±. But she was ¡°optimistic¡± about the outcome.

¡°When you look at¡­the sophistication that¡¯s arising through new metrics-based approaches, we have opportunities to reward teamwork,¡± Professor Johnston told the summit.

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¡°Is someone publishing always by themselves as the first author, or are they clearly playing a team game? Are they contributing to multiple different projects? Are they interdisciplinary in their research? Are they reading widely and citing a broad range of diverse literature from multiple countries? We can reward these things. We can assess them through metrics-based approaches,¡± she said.

While Canberra has accepted a?review recommendation to scrap ERA and EI, education minister Jason Clare has asked the Universities Accord panel to propose a new research assessment model in its final report in December.

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¡°Research remains a key element of all Australian universities, and reform of the performance measurement and management of university research is critical to ensure that the future contribution of universities is driven effectively,¡± the government¡¯s to the review explains.

Professor Johnston said that whatever model was chosen, it should have a ¡°multiplicity of approaches¡± to evaluation. ¡°As soon as you start evaluating, people start moving towards that goal,¡± she said. ¡°You inevitably shape activity, culture and behaviour. If you only have a single goal, everyone¡¯s going to focus on that at the expense of the others.¡±

The new model should also be able to ¡°evolve¡±, she added. ¡°We have to critique it, because the longer a metric stays around, the more people are able to game it ¨C and the more it drives potentially perverse behaviours as a consequence.¡±

Professor Johnston said the new assessment should also harness new artificial intelligence and machine learning capabilities. She said her ¡°on-the-side research project¡± was analysing a 10-year sample of student evaluations to test whether they could be used in the university¡¯s academic excellence framework.

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The team¡¯s ¡°sentiment analysis¡± had detected student biases against female academics in STEM disciplines and almost all academics from non English-speaking backgrounds. Students¡¯ gender and nationality also affected their appraisals. ¡°The students are choosing, for example, to assess female academics on some aspects of education and male academics on other aspects of education.

¡°It is this wonderful power of machine learning and artificial intelligence that¡¯s enabling us to get at it. It means that we don¡¯t have to throw the baby out with the bathwater. We want our students to be evaluating our teaching, informing us and allowing us to progress and improve, so we don¡¯t want to throw out those evaluations. But we also want to eliminate or minimise the potential for bias within those systems.¡±

john.ross@timeshighereducation.com