In the early 2000s, the mere idea of online schools, hybrid universities and one teacher educating hundreds – even thousands – at a time was overwhelming. But the imagined future is now a lived reality, shifting the discussion from ‘What would you do?’ to ‘How do we manage?’.

But with today’s university units often enrolling thousands of learners, and classrooms becoming more globalised, teachers are hard-pressed for time and energy – meaning students are more likely to fall behind due to a lack of personalised, timely feedback.

To commemorate the International Day of Education, we’re highlighting PhD candidate Zhiping Liang from our Centre for Learning Analytics at Monash who’s addressing this issue.

The controversy surrounding facial recognition technology manifests at the intersection of power, privacy and identity. How do we foster public trust and shift away from the dystopian tropes of surveillance societies and Big Brother? Start from the ground up – redesigning the technology itself.

If you combine a lack of accountability, transparency and local autonomy, you’ll have a recipe for distrust and disdain.

But PhD candidate Sanjeev Nahulanthran from our Department of Data Science and Artificial Intelligence is working with Dr Leimin Tian, Professor Dana Kulic and Dr Mor Vered to create a new era of facial recognition technologies through his facial expression recognition (FER) research grounded in ethics and collaboration with everyday people.

This is where Series2Vec comes in.

Developed by Navid Mouhammadi Foumani, a PhD student at Monash University’s Faculty of Information Technology, Series2Vec is a cutting-edge self-supervised learning method that tackles one of the biggest barriers to time series analysis — the need for vast amounts of labeled data.