Cold-start Contract Cheating Identification from Side Information Using Latent Linear Writing Style Representation
The Team: Dr Mohamed Reda Bouadjenek, Phillip Dawson, Sunil Aryal, Imran Razzak
Contract cheating is the practice of students paying a third-party to complete assignments, a phenomenon that is growing in breadth and severity in higher education. Indeed, it has been reported that 15% of University students are engaged in contract cheating, a number which suggests that it represents a significant threat to the integrity and reputation of the Australian higher education. To address this challenge, we propose to analyse historical assignment data and develop novel NLP techniques to further advance the domain of AI-empowered contract cheating, in particular, to deal with the cold-start problem in which students have no historical data.
The Team: Dr Mohamed Reda Bouadjenek is joined by Phillip Dawson, Sunil Aryal and Imran Razzak.