Service: Exam control in schools: fighting fraud or profiling students?

You try to control me, I outsmart you. This was the logic of a Danish high school student who, in a post on Github, described how to disable a digital exam monitor (Schou, 2019). The move was part of massive protests by the students against The Digital Exam Monitor, developed by the Danish Ministry of Education (Den Digitale Prøvevagt), and which was set to roll out in spring 2019. Following the protests against this surveillance, which included copies of the clipboard, active websites in four browsers, a key logger, a screen dump every minute, or whenever an active window changed, detection of virtual machines in the background, a list of all active programs, various unique IDs, etc.) (Møller Kjemtrup, 2019), the ministry of education put the tool on hold (Undervisningsministeriet, 2019). Yet under the new social democrat government, students continue to ask the authorities to shelve it entirely (Mejlgaard, 2019). Meanwhile, another tool that uses machine learning to detect exam fraud has been developed at Copenhagen University. This move stirred new controversies and fears about student profiling in 2019. In 2014, high school software provider Macom supplied a tool called Lectio, which operated as an early detection system to trace students in danger of dropping out (Copenha- gen University, 2014). The tool was developed by university students and based upon information gathered through the administration and communication system used by 9 out of 10 Danish high schools, according to Macom and media reports. However, use of the algorithm was stopped about a week after it was introduced. The data used to develop it had been handed to a third party – the university and its master’s students – without permission from the high schools (Møllerhøj, 2015). More recently, in May 2019, a group at the same department of Copenhagen University published a machine learning system by the name of Ghostwriter. The developers said that it could be used to trace the writing style of students in assignments and that it could detect with some certainty, whether or not a student had submitted a text written by someone else. The aim of Ghostwriter was to fight exam fraud, but (the university wrote in a press release) that the model could also be applied elsewhere to detect texts written in an unusual style (Copenhagen University, 2019). The machine learning was based upon 130,000 student assignments held in the Lectio system by Macom. The ensuing controversy between the high schools, the university professor, and the director of Macom focused on a breach of contract related to the handling of the students’ data: “We feel unsafe when the data handling agreement is not respected,” a representative of the high schools stated to online technology news website Version2, (Bang Frederiksen, 2019-1; Bang Frederiksen, 2019-2; Bang Frederiksen, 2019-3).