D 2021

Reinforcing Cybersecurity Hands-on Training With Adaptive Learning

ŠEDA, Pavel, Jan VYKOPAL, Valdemar ŠVÁBENSKÝ a Pavel ČELEDA

Základní údaje

Originální název

Reinforcing Cybersecurity Hands-on Training With Adaptive Learning

Autoři

ŠEDA, Pavel (203 Česká republika, garant, domácí), Jan VYKOPAL (203 Česká republika, domácí), Valdemar ŠVÁBENSKÝ (703 Slovensko, domácí) a Pavel ČELEDA (203 Česká republika, domácí)

Vydání

New York, NY, USA, 2021 IEEE Frontiers in Education Conference (FIE), od s. 1-9, 9 s. 2021

Nakladatel

IEEE

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Odkazy

Kód RIV

RIV/00216224:14330/21:00120055

Organizace

Fakulta informatiky – Masarykova univerzita – Repozitář

ISBN

978-1-6654-3851-3

ISSN

UT WoS

000821947700141

Klíčová slova anglicky

adaptive learning; case study; cybersecurity; evaluation; tutor model

Návaznosti

MUNI/A/1527/2020, interní kód Repo. VI20202022158, projekt VaV.
Změněno: 17. 8. 2023 03:48, RNDr. Daniel Jakubík

Anotace

V originále

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.

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