There has been a rapid increase in popularity of Python courses at universities and colleges worldwide for the past years, with more and more teachers using Python in their programming courses. Thanks to Python’s simplified syntax, rapid prototyping and ease of use, it’s an excellent language to use as an introduction to programming. But also more advanced courses in data science, machine learning and scientific computing have adopted Python thanks to its flexibility and modularity - using packages like Numpy or Scipy, to get Matlab- and R-like behaviour, or Jupyter Notebooks for a more scientific approach.
With an increasing number of students pursuing courses in Python, it has become further important to maintain academic integrity in the Python source code too. Detecting plagiarism is important not just to avoid having plagiarised source code but also to encourage students to learn, practice and test their own programming skills by submitting original content.
We see that teachers unfortunately often still have to skip checking for plagiarism, as it is simply impossible to manually check for plagiarism in student submissions in all the student submissions and using separate plagiarism checker tools is found to be too cumbersome. Having to manually copy and paste source code to another tool and manually parsing results often only increases an already huge workload of teachers. Because of this, teachers often do acknowledge the importance of detecting plagiarism, but simply lack the time to do so effectively. Want to learn how to autograde Python code too? Click here for our blog on autograding Python and Jupyter Notebook assignments!
