Securely Autograding Cybersecurity Exercises UsingWeb Accessible Jupyter Notebooks

The rapidly growing demand for computer science expertise combined with the pandemic era forced much education into large hybrid or fully remote learning environments, placing new emphasis on online learning platforms and automatic grading. Jupyter notebooks are a popular way to teach coding skills, as they provide an online way to distribute assignments with a low-cost Python coding environment to students and are also heavily used in data science, making the skills learned transferrable to the real world. However, autograding Jupyter notebooks is challenging, and contemporary tools have a number of pitfalls that make it difficult to integrate into a larger learning platform. As such, we implement our own grading system for Jupyter notebooks within the context of a broader gamified learning platform used in a cybersecurity course. Significant emphasis is given to the design, feedback, and security, as we often wish to introduce vulnerabilities within the student's learning environment for them to exploit while simultaneously protecting the system from misuse. We evaluate the system during its use in the Fall 2021 semester, discussing both its successes and failures, and provide transferrable lessons other instructors can use in their own systems, as the autograding systems used by many instructors are home-grown.