Designing Adaptive Cybersecurity Hands-on Training

This Research To Practice Full Paper presents an instructor guide and a tool to improve the creation of cybersecurity hands-on training with adaptive learning support. Adaptive learning uses students' performance and skills to assign suitable tasks to improve their learning experience. While it is well-established in many domains, it is rarely used in operating systems, networking, and cybersecurity. In this paper, we improve and present how to ease the creation and optimization process of adaptive hands-on training by instructors. To the best of our knowledge, this paper is one of the first works investigating the process of creating cybersecurity training with adaptive learning. The training uses metrics such as pre-training assessment and performance during the previous tasks in training to assign suitable tasks for each student. With the help of the developed tool, we demonstrate how metrics settings influence the students' transitions between training tasks. The instructors can easily visualize students' transitions throughout the training. This approach helps the instructors adapt the metrics to predict students' transitions between tasks for each training session. The results from performed simulations show that our tool might increase the efficiency of the adaptive training and students' experience even more. Using the experience from the simulations and past training sessions, we propose the design process for the whole creation of adaptive training. This design process is general enough to be adopted by other domains such as operating systems and networking that may use adaptive learning techniques for their hands-on assignments. We have released the tool and all the software components under an open-source license, so other instructors can freely use and adopt them.