Tensor Decomposition for Student Success Prediction Models in Hands-on Cybersecurity Exercises

Cybersecurity is an ever-evolving field that demands more workers and a wider array of knowledge every year. As such, cybersecurity education remains essential - not just for professionals, but for developers and non-technical roles as well. Due to this, hands-on cybersecurity exercises, such as the ones in the eduRange platform, are increasingly important. EduRange aims to be a flexible, intuitive cybersecurity platform that allows instructors to tailor pre-existing scenarios to their classes' needs. However, when students become stuck or frustrated, learning grinds to a halt. To combat this discouragement, we want to create a semi-automated hint system that can consistently identify struggling students. Such a hint system, however, requires a large quantity of data, which can be difficult to obtain through classroom testing alone.