Reinforcing Cybersecurity Hands-on Training With Adaptive Learning

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F21%3A00120055" target="_blank" >RIV/00216224:14330/21:00120055 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1109/FIE49875.2021.9637252" target="_blank" >http://dx.doi.org/10.1109/FIE49875.2021.9637252</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/FIE49875.2021.9637252" target="_blank" >10.1109/FIE49875.2021.9637252</a>

Result language

angličtina

Original language name

Reinforcing Cybersecurity Hands-on Training With Adaptive Learning

Original language description

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.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/VI20202022158" target="_blank" >VI20202022158: Research of New Technologies to Increase the Capabilities of Cybersecurity Experts</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

2021 IEEE Frontiers in Education Conference (FIE)

ISBN

9781665438513

ISSN

1539-4565

e-ISSN

2377-634X

Number of pages

9

Pages from-to

1-9

Publisher name

IEEE

Place of publication

New York, NY, USA

Event location

Lincoln, Nebraska, USA

Event date

Jan 1, 2021

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000821947700141