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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Presentation Transcript

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