D 2021

Reinforcing Cybersecurity Hands-on Training With Adaptive Learning

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

Basic information

Original name

Reinforcing Cybersecurity Hands-on Training With Adaptive Learning

Authors

ŠEDA, Pavel (203 Czech Republic, guarantor, belonging to the institution), Jan VYKOPAL (203 Czech Republic, belonging to the institution), Valdemar ŠVÁBENSKÝ (703 Slovakia, belonging to the institution) and Pavel ČELEDA (203 Czech Republic, belonging to the institution)

Edition

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

Publisher

IEEE

Other information

Language

English

Type of outcome

Stať ve sborníku

Country of publisher

United States of America

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

electronic version available online

References:

RIV identification code

RIV/00216224:14330/21:00120055

Organization

Fakulta informatiky – Repository – Repository

ISBN

978-1-6654-3851-3

ISSN

UT WoS

000821947700141

Keywords in English

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

Links

MUNI/A/1527/2020, interní kód Repo. VI20202022158, research and development project.
Změněno: 17/8/2023 03:48, RNDr. Daniel Jakubík

Abstract

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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