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Musings on misconduct

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Talk delivered by Pauline Belford at Ethicomp 2015, from our paper Musings on Misconduct.

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Musings on misconduct

  1. 1. Musings on Misconduct PAULINE BELFORD MICHAEL JAMES HERON
  2. 2. Introduction  Clashes are often observed between software engineering good practice and institutional conventions regarding plagiarism.  Plagiarism in programming is often a result of student misunderstanding regarding how far good practice extends  And as such, a degree of empathy is required when assessing and confronting incidences.  In this talk, I will discuss the nature of this clash with regards to programming in a college or university setting.  I will discuss how plagiarism is commonly identified when assessing coursework submissions, and the ethical issues raised.  We conclude the talk with some notes on institutional good practice and how to remove some of the heat from student interactions.
  3. 3. All Programming is Plagiarism?  Culture of reuse:  Standardised syntax  Standard algorithms  Design patterns  Architecture is restrictive  Loops, Selections, Sequential  Code style is often mandated  Stylistic elements often inherited  Reusability and maintainability  Emphasised as good practice
  4. 4. All Programming is Plagiarism?  We emphasise good practice in software engineering, which is often at odds with institutional definitions of academic conduct.  Students can find themselves at odds with their own discipline.  Reusing their own code (e.g. cross assessment)  Reusing the code of their colleagues  Integrating external code into their own work.  As a discipline, we emphasise that it is important not to reinvent the wheel.  And yet, making use of all resources available will likely lead to a clash with academic norms and expectations.
  5. 5. What is Academic Plagiarism?  Plagiarism implies passing work off as your own without attribution.  Students are generally au fait with the idea of plagiarism.  Can recite text book definitions  Problem is not in understanding, it is in interpretation.  Often plagiarism represents a lack of awareness that it applies in a given situation.  All academics have a responsibility to identify plagiarism.  Students can receive degree awards for work they did not submit.  Devalues the qualification for all other students.  Reflects badly on the institution when student inability is discovered.
  6. 6. What is Plagiarism in Programming?  Almost impossible to define.  Where does plagiarism live?  In lines of code?  In data structures?  In algorithms?  In architecture?  All of these and none of these.  We exacerbate this problem – we teach plagiarism as good practice.  Not intentionally, but through a lack of coherent contextualisation.  Students suffer from our flippancy in teaching these topics without fully covering the implications for misconduct.  Often due to time pressure  Often due to course pressure
  7. 7. Methods for identifying plagiarism in programming  Can’t be easily automated.  There is no real Turnitin equivalent for software code. Problem may be intractable.  Requires specialist examination of submissions by subject matter expert.  Time consuming, prone to mistakes, can’t offer full coverage.  Attention most directed at obvious candidates for examination.  But what does obvious mean here?  Course organisation can frustrate analysis:  Marking distribution, pair programming submissions, etc.
  8. 8. Ethics of Identifying Plagiarism in Programming  Directed sampling is ethically questionable.  Subject to subconscious biases  Selection bias, etc.  Focuses attention on those least likely to be successfully hoodwinking..  In the case of weaker students submitting work beyond their assumed competence.  Assumed competence?  Slanted by familiarity with students within lab situations.  May miss those students who are quietest.  Assumed competence comes from our own exposure to evolving student submissions
  9. 9. Ethics of Identifying Plagiarism in Programming  Our techniques are ineffective against commissioned work.  Little success against essay mills  Class divide?  Those with the most money are most likely to fly under the radar.  It is our familiarity with student work that directly informs sampling.  And this is troublesome.
  10. 10. Good practice  Should inform all students at the beginning of a course that semi- random subset will be selected for mini-viva.  Non stigmatising – not only those under suspicion  Non comprehensive – not all students will be selected.  Selection criteria for mini-viva is all students under suspicion and a random sampling of all others.  Students that are under suspicion are selected for forensic dissection.  So too is a completely random sampling of all students.  Each selected student undergoes the same forensic examination of coursework.  Same process applied regardless of inclusion criteria.
  11. 11. Fair Dealings in Academic Misconduct  Many institutions bias academic misconduct hearings against the student.  Often unintentionally, and usually without malice.  Students are often unaware of the charge or evidence until they are confronted in the meeting.  This creates unnecessary tensions, stresses on the students, and skews the outcome.  It’s unfair to judge a student based on their perceived inability to explain irregularities under stress in a high-stakes environment.  We recommend that students see fully annotated transcripts of their work beforehand, so they can effectively marshal a defence or explanation.  Or are aware of the strength of evidence prior to the hearing.
  12. 12. Conclusion  Students often plagiarise not as a result of malice, but instead an outcome of their lack of specialist knowledge.  Students often lack the skills to properly evaluate the degree of contribution they made to a submission.  To a certain extent, all programming is plagiarism, and we are often flippant in our treatment of good practice.  Academics need to be mindful of the fact they play an important role in helping students interpret plagiarism guidelines within complex environments.  In no way are we attempting to minimise the responsibility of the student in cases of genuine plagiarism.  We are only attempting to examine and contemplate our own role in the process.

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