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Anti-plagiarism strategies: How to manage it with quality in large scale thesis productions
Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.ORCID-id: 0000-0002-4308-916X
Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
2013 (engelsk)Inngår i: International Journal for Educational Integrity, ISSN 1833-2595, E-ISSN 1833-2595, Vol. 9, nr 2, s. 60-73Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

More than 400 students write their bachelor’s or master’s theses each year at the Department of Computer and Systems Sciences, Stockholm University. In order to support self-driven student thesis work and to reduce the burden on supervisors for feedback on basic skills, an IT support system called SciPro was developed. An important consideration in developing this system was to take actions to reduce plagiarism. Both prevention and detection were accomplished with the following: 1) prevention by policy guidelines, FAQ, face-to-face information, peer-reviews and transparency in the process of recurrent online thesis manuscript hand-ins; and 2) detection by automatic originality check of the final manuscript enabled by integration between SciPro and Turnitin. Explicit rules and regulations as well as frequent education about anti-plagiarism targeting both students and supervisors were also important parts of the prevention strategy. Current results include: 1) substantial improvements in policy development; 2) successful integration of anti-plagiarism software; and 3) recurrent educational activities for students and supervisors have raised the awareness of plagiarism issues at the department. Future development includes three new technical approaches in order to manage sophisticated antiplagiarism controls efficiently, with a quality standard not possible by other means, in large-scale thesis production: 1) automated and integrated (SciPro/Turnitin) recurrent anti-plagiarism controls of submitted thesis manuscripts at various stages in the thesis text production process; 2) automated anti-plagiarism controls of thesis texts submitted in SciPro by comparing consistency in style of writing between different versions of thesis manuscripts handed in by the same student during the process of producing the thesis text; and 3) an automated check of thesis manuscripts submitted to SciPro for identification of images/figures/illustrations/graphs copied from the Internet through integration of an image pattern recognition programme. These measures taken together will significantly increase thesis quality by verifying authenticity to a very high degree and systematising the anti-plagiarism procedures. They will also substantially reduce tedious, boring and immensely time consuming manual work for administrators and supervisors who need to guarantee that theses do not contain plagiarised texts or illustrations.

sted, utgiver, år, opplag, sider
2013. Vol. 9, nr 2, s. 60-73
Emneord [en]
anti-plagiarism, management, peer-review, support, supervision, thesis
HSV kategori
Forskningsprogram
data- och systemvetenskap
Identifikatorer
URN: urn:nbn:se:su:diva-97200OAI: oai:DiVA.org:su-97200DiVA, id: diva2:676244
Konferanse
5th Integrity and Plagiarism Conference, Newcastle-Upon-Tyne, UK, 16-18 July, 2012
Merknad

A previous version of the paper was presented at the 5th Integrity and Plagiarism Conference.

Tilgjengelig fra: 2013-12-05 Laget: 2013-12-05 Sist oppdatert: 2020-05-12bibliografisk kontrollert
Inngår i avhandling
1. Managing Thesis Projects in Higher Education - Through Learning Analytics
Åpne denne publikasjonen i ny fane eller vindu >>Managing Thesis Projects in Higher Education - Through Learning Analytics
2020 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

For the student, the graduation project is in most universities the final step towards graduation and increased opportunities in the professional career. It is not uncommon for students to struggle with the thesis in order to complete their graduation, resulting in disrupted plans, delays in completion and the worst case, non-completion of their degrees. This outcome is undesirable for not only the individual student or the university but also society at large.

The effects of a failed thesis are waste of resources and the supply of qualified individual in the society, for the student, the effects include lesser possibilities in the career, lower pay. Thus, the dissertation is addressing the problem of the management of resources in higher education, especially in undergraduate thesis supervision.

There are three aspects of the overall problem addressed in this dissertation; a practical problem in respect of too many uncompleted theses, or not completed on time. Another practical problem is increasing of the demand that is not accompanied by increased resources on a comparable scale. The third aspect of the problem is lack of knowledge in understanding the factors affecting the thesis process resulting in dropouts or delays. The lack of knowledge may result in a blind management process.

Two research questions investigate the problem, what factors influence the thesis completion or non-completion that can be useful for management, secondly, what management principles can be helpful to secure high quality in higher education.

The questions are answered through several studies that employ mixed-methods and learning analytics techniques which include data mining and the application of statistics and machine learning algorithms. In total, over 3000 thesis projects have been studied.

The thesis contributes with a better understanding of the factors that influence completion and non-completion of thesis projects, management guidelines for the thesis process, and a novel methodological approach using learning analytics and machine learning to support data-driven decision-making about thesis processes.

Abstract [sv]

För en student är examenprojektet vid de flesta universitet det sista steget inför examen som ger ökade möjligheter i karriären. Det är inte ovanligt att studenter kämpar med sin uppsats för att kunna slutföra examen, vilket resulterar i rubbade planer, förseningar i att avsluta utbildningen och i värsta fall, att utbildning inte fullföljs alls. Detta är ett oönskat resultat inte bara för den enskilda studenten eller universiteten utan också för samhället i stort.

Effekterna av att misslyckas med uppsatsen är ett slöseri med resurser och leder till minskad tillgång till kvalificerad individer i samhället. För studenten blir effekten sämre karriärmöjligheter och lägre lön. Avhandlingen undersöker problemet att hantera resurser inom högre utbildning, särskilt när det gäller examensarbete.

Det finns tre aspekter av det övergripande problemet som tas upp i avhandlingen. Ett praktiskt problem med för många ofullständiga uppsatser, eller uppsatserr som inte avslutas i tid. Ett annat praktiskt problem är ökande efterfrågan på högre utbildning, som inte möts av ökande tilldelning av resurser i motsvarande grad. Den tredje aspekten av problemet är bristen på kunskap för att förstå de faktorer som påverkar uppsatsprocessen vilket resulterar i bortfall eller oönskade förseningar. Bristen på denna kunskap kan leda till en blind ledningsprocess.

Två forskningsfrågor formuleras för att undersöka problemet, vilka faktorer påverkar uppsatsen fullbordande eller ej. Faktorer som kan vara användbara för ledningen att känna till, och för det andra vilka ledningsprinciper kan vara till hjälp för att säkerställa hög kvalitet i högre utbildning.

Frågorna besvaras genom flera studier som använder blandade metoder och learning analyticstekniker, inklusive data mining och tillämpning av statistika metoder och maskininlärningsalgoritmer. Totalt har över 3 000 uppsatsprojekt studerats.

Avhandlingen bidrar till en bättre förståelse av de faktorer som påverkar avslutande eller icke avslutande av ett uppsatsprojekt, samt riktlinjer för uppsatsprocessen pekar dessutom på en ny metodik som använder learning analytics och maskininlärning för att stödja datadrivna beslut om uppsatsprocesser.

sted, utgiver, år, opplag, sider
Stockholm: Department of Computer and Systems Sciences, Stokcholm University, 2020. s. 76
Serie
Report Series / Department of Computer & Systems Sciences, ISSN 1101-8526 ; No. 20-010
HSV kategori
Forskningsprogram
data- och systemvetenskap
Identifikatorer
urn:nbn:se:su:diva-181430 (URN)978-91-7911-180-9 (ISBN)978-91-7911-181-6 (ISBN)
Disputas
2020-08-26, L50, NOD-huset, Borgarfjordsgatan 12, Kista, 09:00 (svensk)
Opponent
Veileder
Merknad

At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 5: Submitted.

Tilgjengelig fra: 2020-06-03 Laget: 2020-05-12 Sist oppdatert: 2020-05-26bibliografisk kontrollert

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