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laurence hellyer lawrence beadle and munjoth lalli n.
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Plagiarism Detection in Microsoft Excel Assignments PowerPoint Presentation
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Plagiarism Detection in Microsoft Excel Assignments

Plagiarism Detection in Microsoft Excel Assignments

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Plagiarism Detection in Microsoft Excel Assignments

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  1. Laurence Hellyer, Lawrence Beadle and MunjothLalli Plagiarism Detection in Microsoft Excel Assignments

  2. Managing Academic Misconduct & Preventing Plagiarism A Typical Microsoft Excel Assignment…

  3. A Familiar Problem Managing Academic Misconduct & Preventing Plagiarism

  4. Managing Academic Misconduct & Preventing Plagiarism Plagiarising an Excel Assignment • Plagiarism: The action of taking someone else's work • Text Cells • Formula Cells • Charts • Numeric Cells – these are often specified by the assignment (i.e. assume an interest rate of 18%)

  5. Case Study Suspected Plagiarism Detected by Human Markers Managing Academic Misconduct & Preventing Plagiarism

  6. Managing Academic Misconduct & Preventing Plagiarism Existing Solutions? • TurnItIn • Moss • Cheater Cheater Pumpkin Eater (CCPE) • MEAGER §

  7. Managing Academic Misconduct & Preventing Plagiarism Human Markers Detecting Plagiarism • Microsoft Excel files can save meta-data about the file: • Author • Last saved by • Creation time • Last modification time • Registered Company

  8. Managing Academic Misconduct & Preventing Plagiarism Human Markers Detecting Plagiarism (Sometimes) • Microsoft Excel files can save meta-data about the file: • Author • Last saved by • Creation time • Last modification time • Registered Company

  9. Managing Academic Misconduct & Preventing Plagiarism ExcelSmash • Analyses 400 students in < 2 minutes • Output rapidly identifies submissions with similar content

  10. Managing Academic Misconduct & Preventing Plagiarism Data Used by ExcelSmash… Submission server Student username Author, Last saved by, Creation and modification time, Company Name Strings found in Text cells Strings representing formulas found in Formula cells Excel 97-2003, 2007

  11. Managing Academic Misconduct & Preventing Plagiarism Analysing Submissions • Pair wise comparisons of submissions • 80,000 comparisons for 400 submissions • Individual tests on each submissions • If a submission fails a test we add a “red flag” to the submission • Each test has an associated severity score • Only report submissions that exceed a run-time specified threshold

  12. Managing Academic Misconduct & Preventing Plagiarism Pair Wise Comparisons

  13. Managing Academic Misconduct & Preventing Plagiarism Individual File Tests

  14. Managing Academic Misconduct & Preventing Plagiarism Example Output Login: aaaa --- Severity: 7 Author match “Andrew” with: bbbb --- Severity: 5 Author “Andrew” and last saved by “aaaa” mis-match --- Severity: 2 Login: cccc --- Severity: 23 Similar creation time to dddd --- Severity: 1   Similar creation time to eeee --- Severity: 1 Similar creation time to ffff --- Severity: 1 100% similar text to ffff --- Severity: 10 100% similar formula to ffff --- Severity: 10

  15. Managing Academic Misconduct & Preventing Plagiarism Example Plagiarism Detected by ExcelSmash

  16. Managing Academic Misconduct & Preventing Plagiarism Text Matching • Case insensitive string equality Please Enter Your Annual Salary Annual Salary Please Enter Your Annual Salary Please enter your annual salary

  17. Managing Academic Misconduct & Preventing Plagiarism Percentage Similar Content

  18. Managing Academic Misconduct & Preventing Plagiarism Formula Matching • Case insensitive string equality =AVERAGE(H1:H10)*100 =100*AVERAGE(H1:H10) =SUM(A1:D4) =SUM(A2:D5)

  19. Managing Academic Misconduct & Preventing Plagiarism Percentage Similar Content

  20. Managing Academic Misconduct & Preventing Plagiarism Case Study

  21. Managing Academic Misconduct & Preventing Plagiarism Case Study Suspected Plagiarism Detected in 2007-08 Cohort (382 students)

  22. Managing Academic Misconduct & Preventing Plagiarism ExcelSmash Conclusions • New tool aimed at detecting possible plagiarism within Microsoft Excel assignments • Quickly identifies submissions requiring further scrutiny • Improved detection of intra group and especially inter group plagiarism compared to human markers

  23. Managing Academic Misconduct & Preventing Plagiarism Thank you Questions? www.cs.kent.ac.uk/~lh243