collaboration collusion and plagiarism in computer science
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Collaboration, collusion and plagiarism in computer science. Bob Fraser. Why Computer Science?. “The computer is a relentlessly unforgiving arbiter of correctness” (Roberts, 2002) Computer science asks students to find the correct solution or method

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why computer science
Why Computer Science?
  • “The computer is a relentlessly unforgiving arbiter of correctness” (Roberts, 2002)
  • Computer science asks students to find the correct solution or method
  • Stanford study: 37% of cheating cases are CS, 7% of students are CS (Roberts, 2002)
outline
Outline
  • Collaboration
  • Collusion
  • Plagiarism
  • Mitigating dishonesty
collaboration
Collaboration
  • Generally to be encouraged
  • Discussing course material with peers is a primary aspect of active learning
  • Line must be drawn so that collaboration doesn’t become excessive and negatively affect learning
collusion
Collusion
  • Excessive collaboration
  • Definition is set by the course instructor
  • From Waterloo’s OAI:
    • “Clearly indicate if group collaboration is acceptable (and the level of collaboration permitted) or if students must do all work independently.”
plagiarism
Plagiarism
  • Literary theft
  • Encompasses copying whether the original author is aware of it or not
  • In computer science, the best solution is often unique, exacerbating the problem
  • Many honest students err to heavily on the side of caution to avoid plagiarism
where is the line drawn
Where is the line drawn?
  • Student A doesn\'t know how to start the assignment and so he asks student B who helps him by showing him his own work. Student A writes up the assignment in his own words but there are some similarities with student B\'s work. (Barrett & Cox, 2005)
mitigating
Mitigating
  • Make the rules clear – the onus is on you
  • Methods:
    • Appeal to their maturity
    • Detect & punish cheaters
    • Emphasize ILOs
    • Improve tutorials
    • Regular quizzes
appealing to maturity
Appealing to Maturity

“cheating isn\'t bad because it only hurts you at test time”

Palazzo et al. (2010)

detection discipline
Detection & Discipline
  • Students are less likely to cheat if they believe that they may be caught and punished (although zero tolerance is too far)
  • Many professors have looked the other way for various reasons
  • Waterloo encourages the use of Turnitin
intended learning outcomes
Intended Learning Outcomes
  • Emphasize the value of the assignment and how it fits in the course
  • Students who see the purpose and value of their work are less likely to cheat
  • It may be worthwhile to explicitly state the ILOs on the assignment
conclusions
Conclusions
  • Students should be encouraged to collaborate
  • Students should be given a precise definition of what is acceptable behaviour
  • Courses can be improved to reduce cheating and improve learning
pointers
Pointers
  • Bob Fraser->publications
  • http://www.cs.uwaterloo.ca/~r3fraser/papers/cut902_fraser.pdf
  • Key References
    • Barrett, R., & Cox, A. L. (2005). At least they are learning something: the hazy line between collaboration and collusion. Assessment & Evaluation in Higher Education, 30 (2), 107-122.
    • Palazzo, D. J., Lee, Y.-J., Warnakulasooriya, R., & Pritchard, D. E. (2010). Patterns, correlates, and reduction of homework copying. Phys. Rev. ST Phys. Educ. Res., 6 (1), 010104-1-010104-11.
    • Roberts, E. (2002). Strategies for promoting academic integrity in CS courses. Frontiers in Education Annual, 3 , F3G14-19.
    • University of Waterloo Office of Academic Integrity
      • http://uwaterloo.ca/academicintegrity/index.html
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