Informační systém Repo
ŠEDA, Pavel, Jan VYKOPAL, Valdemar ŠVÁBENSKÝ a Pavel ČELEDA. Reinforcing Cybersecurity Hands-on Training With Adaptive Learning. In 2021 IEEE Frontiers in Education Conference (FIE). New York, NY, USA: IEEE, 2021. s. 1-9. ISBN 978-1-6654-3851-3. doi:10.1109/FIE49875.2021.9637252.
Další formáty:   BibTeX LaTeX RIS
Základní údaje
Originální název Reinforcing Cybersecurity Hands-on Training With Adaptive Learning
Autoři ŠEDA, Pavel, Jan VYKOPAL, Valdemar ŠVÁBENSKÝ a Pavel ČELEDA.
Vydání New York, NY, USA, 2021 IEEE Frontiers in Education Conference (FIE), od s. 1-9, 9 s. 2021.
Nakladatel IEEE
Další údaje
Originální jazyk angličtina
Typ výsledku Stať ve sborníku
Stát vydavatele Spojené státy
Utajení není předmětem státního či obchodního tajemství
Forma vydání elektronická verze "online"
Organizace Fakulta informatiky - Masarykova univerzita
ISBN 978-1-6654-3851-3
ISSN 1539-4565
Klíčová slova anglicky adaptive learning; case study; cybersecurity; evaluation; tutor model
Návaznosti MUNI/A/1527/2020, interní kód Repo.VI20202022158, projekt VaV.
Změnil Změnil: RNDr. Daniel Jakubík, učo 139797. Změněno: 29. 4. 2022 03:08.
This Research To Practice Full Paper presents how learning experience influences students' capability to learn and their motivation for further learning. Although each student is different, standard instruction methods do not adapt to individual students. Adaptive learning reverses this practice and attempts to improve the student experience. While adaptive learning is well-established in programming, it is rarely used in cybersecurity education. This paper is one of the first works investigating adaptive learning in cybersecurity training. First, we analyze the performance of 95 students in 12 training sessions to understand the limitations of the current training practice. Less than half of the students (45 out of 95) completed the training without displaying any solution, and only in two sessions, all students completed all phases. Then, we simulate how students would proceed in one of the past training sessions if it would offer more paths of various difficulty. Based on this simulation, we propose a novel tutor model for adaptive training, which considers students' proficiency before and during an ongoing training session. The proficiency is assessed using a pre-training questionnaire and various in-training metrics. Finally, we conduct a case study with 24 students and new training using the proposed tutor model and adaptive training format. The results show that the adaptive training does not overwhelm students as the original static training format. In particular, adaptive training enables students to enter several alternative training phases with lower difficulty than the phases in the original training. The proposed adaptive format is not restricted to particular training used in our case study. Therefore, it can be applied to practicing any cybersecurity topic or even in other related computing fields, such as networking or operating systems. Our study indicates that adaptive learning is a promising approach for improving the student experience in cybersecurity education. We also highlight diverse implications for educational practice that improve students' experience.
Zobrazeno: 11. 8. 2022 00:29