Smart Environment for Adaptive Learning of Cybersecurity Skills

Abstract

Hands-on computing education requires a realistic learning environment that enables students to gain and deepen their skills. Available learning environments, including virtual and physical laboratories, provide students with real-world computer systems but rarely adapt the learning environment to individual students of various proficiency and background. We design a unique and novel smart environment for the adaptive training of cybersecurity skills. The environment collects a variety of student data to assign a suitable learning path through the training. To enable such adaptiveness, we propose, develop, and deploy a new tutor model and a training format. We evaluate the learning environment using two different adaptive trainings attended by 114 students of various proficiency. The results show that students were assigned tasks with a more appropriate difficulty, which enabled them to successfully complete the training. Students reported that they enjoyed the training, felt the training difficulty was appropriately designed, and would attend more training sessions like these. Instructors can use the environment for teaching any topic involving real-world computer networks and systems because it is not tailored to particular training. We freely release the software along with exemplary training so that other instructors can adopt the innovations in their teaching practice.