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The Viability of Machine Learning Models Based on Levenstein Distance and Cosine Similarity for Plagiarism Detection in Digital Exams
KTH, School of Electrical Engineering and Computer Science (EECS).
2018 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

This paper investigates the viability of a machine learning model based on similarities in text structure compared to one based on statistical properties in the text to detect cheating in digital examinations. The machine learning model comparing similarity in text structure used Levenstein distance and the one comparing statistical text properties compared cosine distance between word vectors. The paper also investigates whether security has been a driving force impacting the industrial dynamics of the digitalization of examinations in Sweden. This is done using the multi-level perspective framework and interviewing users of a digital examination platform. The results show that the machine learning model based on statistical text properties has a higher accuracy, recall, precision and F-score. Nothing is concluded from this, however, due to discussion of validity of the results from the machine learning model based on the similarities in text structure. The analysis of the industrial dynamics shows that security has been a driving force towards digitalization.

Place, publisher, year, edition, pages
2018.
Series
TRITA-EECS-EX ; 2018:441
Keywords [en]
Machine Learning, Digital Examinations, DigiExam, Industrial Dynamics, Technological Innovation Systems
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-240398OAI: oai:DiVA.org:kth-240398DiVA, id: diva2:1271998
Supervisors
Examiners
Available from: 2019-01-11 Created: 2018-12-18 Last updated: 2019-01-11Bibliographically approved

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fulltext(892 kB)43 downloads
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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf