Detecting Ghostwriters in High Schools
This addresses the issue of academic dishonesty for high schools, but it is incremental as it applies existing deep learning methods to a new domain-specific dataset.
The paper tackles the problem of detecting ghostwriting in high school assignments by developing automatic techniques using deep neural networks, achieving an accuracy of 0.875 and an AUC score of 0.947 on an evenly split dataset.
Students hiring ghostwriters to write their assignments is an increasing problem in educational institutions all over the world, with companies selling these services as a product. In this work, we develop automatic techniques with special focus on detecting such ghostwriting in high school assignments. This is done by training deep neural networks on an unprecedented large amount of data supplied by the Danish company MaCom, which covers 90% of Danish high schools. We achieve an accuracy of 0.875 and a AUC score of 0.947 on an evenly split data set.