Automating mistrust

Ben Williamson

Turnitin can now analyse students’ individual writing styles to tackle ‘contract cheating’. Image by Xavi

The acquisition of plagiarism detection company Turnitin for US$1.75 billion, due to be completed later this year, demonstrates how higher education has become a profitable market for education technology companies. As concern grows about student plagiarism and ‘contract cheating’, Turnitin is making ‘academic fraud’ into a market opportunity to extend its automated detection software further. It is monetizing students’ writing while manufacturing mistrust between universities and students, and is generating some perverse side effects.

Cheating software
Turnitin’s acquisition is one of the biggest deals ever signed in the edtech field. Its new owner, Advance Publications, is a global media conglomerate with a portfolio including the Conde Nast company. With traditional media forms losing audiences, the deal indicates how technology and media businesses have begun to view education as a potentially valuable investment market.

The profitability of Turnitin, and attraction to Advance, derives from the assignments that students provide for free to its platform. Its plagiarism detection algorithm is constantly fine-tuned as millions of essays are added, analysed and cross-checked against each other and other sources. The ‘world’s largest comparison database’ of student writing, it consists of 600+ million student papers, 155,000+ published works and 60+ billion web pages. Similar to social media companies profiting from user-generated content, value for Turnitin comes from analysing students’ uploaded essays against that database, and securing purchases from universities based on the analysis.

Students can even pay to upload their essays prior to submission to Turnitin’s WriteCheck service, in order to check for similar sentences and phrases, missing or inaccurate citations, and spelling or grammatical inaccuracies. WriteCheck uses the same techniques as common standardized English language tests, and offers an online Professional Tutor Service through a partnership with Pearson.

The company has had years to grow and finesse its services and its ‘Similarity Score’ algorithm. In the UK, the original version of Turnitin, then known as iParadigms, was first paid for on behalf of the HE sector by Jisc (the digital learning agency) from 2002 to 2005, giving it an inbuilt cost advantage over competitors. It also gave it an inbuilt data advantage to train its plagiarism detection algorithm on a very large population of students’ assignments. Nonetheless, studies have repeatedly shown its plagiarism detection software is inaccurate. It both mistakenly brands some students as cheats while completely missing other clear instances of plagiarism, with an error rate that suggests its automated plagiarism reports should be trusted  less than its commercial valuation and market penetration indicates.

With the announcement of its acquisition by Advance, critics say the $1.75bn deal also amounts to the exploitation of students’ intellectual property. ‘This is a pretty common end game for tech companies, especially ones that traffic in human data’, commented Jesse Stommel of the University of Mary Washington. Turnitin’s business model, he added, is to ‘create a large base of users, collect their data, monetize that data in ways that help assess its value, [and] leverage that valuation in an acquisition deal’.

The tension between students’ intellectual property and Turnitin’s profit-making is not new. In many universities, it is compulsory for all student assignments to be submitted to Turnitin, with their intellectual effort then contributing to its growing commercial valuation without their informed knowledge. Ten years ago, four US college students tried to sue Turnitin for taking their assignments against their will and then profiting from them.

Manufacturing mistrust
Beyond its monetization strategy, Turnitin is also reshaping relationships between universities and students. Students are treated by default as potential essay cheats by its plagiarism detection algorithm. This is not a new concern. Ten years ago Sean Zwagerman argued that  plagiarism detection software is a ‘surveillance technology’ that ‘treats writing as a product, grounds the student-teacher relationship in mistrust, and requires students to actively comply with a system that marks them as untrustworthy’. Turnitin’s continued profitability depends on manufacturing and maintaining mistrust between students and academic staff, while also foregrounding its automated algorithm over teachers’ professional expertise.

In the book Why They Can’t Write, John Warner argues that students’ writing abilities have been eroded by decades of standardized curriculum and assessment reforms. Turnitin is yet another technology that treats writing as a rule-based game. ‘It signals to students that the writing is a game meant to please an algorithm rather than an attempt to convey an idea to an interested audience’, Warner has noted. ‘It incentivizes assignments which can be checked by the algorithm, which harms motivation’.

Turnitin also changes how students practice academic writing. One of the leading critical researchers of Turnitin, Lucas Introna, argues it results in the ‘algorithmic governance’ of students’ academic writing practices. Moreover, he suggests that ‘what the algorithms often detect is the difference between skilful copiers and unskilful copiers’, and as a result that it privileges students ‘who conceive of “good” writing practice as the composition of undetectable texts’.

The new deal will open opportunities for Turnitin to develop and promote new features that will further intervene in students’ writing. One is its new service to scan essays to detect an individual’s unique writing style, launched to the HE market in March just a week after announcing its acquisition. This could then be used to identify ‘ghostwriting’—when students hire someone else to write their essays or purchase made-to-order assignments.

Turnitin has published expert guidance for universities to identify and combat contract cheating

The new Authorship Investigate service extends Turnitin from the analysis of plagiarism to students’ writing ability, using students’ past assignments, document metadata, forensic linguistic analysis, machine learning algorithms and Natural Language Processing to identify if a student has submitted work written by someone else. It reinforces the idea that the originality, value and quality of student writing should first be assessed according to the criteria of the detection algorithm, and treats all student writing as potential academic piracy. It is also likely to require students to submit extensive writing samples to train the algorithm to make reliable assessments of their writing style, thereby further enhancing the monopoly hold of Turnitin over data about student writing.

Turnitin has bred suspicion and mistrust between students and academics, while affecting how students value and practice academic writing. Yet this mistrust is itself a market opportunity, as the company seeks to offer more solutions services to the perceived problem of increased student plagiarism and contract cheating. As suspicions about student cheating have continued to grow since it was launched nearly 20 years ago, Turnitin has been able to capitalize to dramatically profitable results. Its ghostwriter detection service, of course, is a solution to one of the very problems Turnitin created–because plagiarism has become so detectable, the huge essay mills industry has emerged to produce original on-demand content for students to order. As a result, Turnitin is automating mistrust as it erodes relationships between students and universities, devalues teacher judgment, and reduces student motivation.

Plagiarism police
However damaging and inaccurate it may be, the Advance acquisition will enable Turnitin to further expand its market share and product portfolio. For Turnitin, the timing is ideal, as universities and HE policymakers are collectively beginning to address the rise of online ‘essay mills’ and their erosion of ‘academic integrity’. Government education departments in the UK and Australia have begun to tackle contract cheating more seriously, including through advocating increased use of innovative plagiarism detection software.

In a speech to the Universities UK International higher education forum in March, universities minister Chris Skidmore identified essay mills as one of the issues that needed to be tackled to protect and improve the quality of higher education in England and ensure that it retained its reputation for excellence.

UK academic leaders, HE agencies and ministers have already asked PayPal to stop processing payments to essay mills, and Google and YouTube to block online ads, in an effort to close down the $1 billion annual market in made-to-order assignments. These moves to prevent contract cheating also affect university students and graduates in Kenya, a ‘hotspot‘ for essay mill companies and writers, who rely on contract academic writing as a major source of income. So while Turnitin is set to profit from the detection of contract cheating in Global North contexts, it is disrupting a significant source of employment in specific Global South contexts. In Kenya, for example, where unemployment is high, ‘participants think of their jobs as providing a service of value, not as helping people to cheat. They see themselves as working as academic writers.’

Turnitin’s website now prominently markets its ghostwriter detection service along with a series of free-to-download ebooks to help universities identify contract cheating and develop strategies and tactics to combat it. It’s positioning itself not just as a technical solutions vendor, but as an expert source of insight and authority on ‘upholding academic integrity’. At the same time, Authorship Investigate will allow Turnitin to become the market leader in the fight against essay mills.

The launch of Authorship Investigate has coincided with a Times Higher Education report on the ‘surprising level of support’ among academics for contract cheating services to be made illegal and for ‘the criminalising of student use of these services’. This would appear to raise the prospect of algorithmic identification of students for criminal prosecution. Though there’s nothing to indicate quite such a hard punitive line being taken, the UK Department for Education has urged universities to address the problem, commenting to the THE, ‘universities should also be taking steps to tackle this issue, by investing in detection software and educating students on the severe consequences they face if caught cheating’.

Turnitin is the clear market-leader to solve the essay mills problem that the department has now called on universities to tackle. Its technical solution, however, does not address the wider reasons—social, institutional, psychological, financial or pedagogic—for student cheating, or encourage universities to work proactively with students to resolve them. Instead, it acts as a kind of automated ‘plagiarism police force’ to enforce academic integrity, which at the same time is also set to further disadvantage young people in countries such as Kenya where preparing academic texts for UK and US students is seen as a legitimate and lucrative service by students and graduates.

Robotizing higher education
Like many other technology organizations in education, Turnitin is increasing automation in the sector. Despite huge financial pressures, universities are investing in Turnitin to automate plagiarism and ghostwriting detection as a way of combating academic fraud. The problem of essay mills that politicians are now fixated upon is the ideal market opportunity for Turnitin to grow its business and its authority over student writing even further. In so doing, it also risks standardizing students’ writing practices to conform to the rules of the algorithm–ultimately contributing to the algorithmic governance, and even ‘robotization’, of academic writing.

The real problem is that universities are being motivated to invest in these robotized, data-crunching edtech products for multiple complex reasons. As universities have to seek larger student enrolments for their financial security, algorithmic services become efficient ways of handling huge numbers of student assignments. They satisfy government demands for action to be taken to raise standards, boost student performance, and preserve academic integrity. But automated software is a weak, robotic, and error-prone substitute for the long-term development of trusting pedagogic relationships between teachers and students.

A version of this post was previously published on Research Professional with the title ‘Manufacturing mistrust‘ on 12 June 2019.