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@article{2373677,
author = {Dočkalová Burská, Karolína and Mlynárik, Jakub Rudolf and Ošlejšek, Radek},
doi = {http://dx.doi.org/10.1007/s10639-024-12480-x},
keywords = {visual analytics; clustering analysis; hands-on learning; visualization},
language = {eng},
issn = {1360-2357},
journal = {Education and Information Technologies},
title = {Using data clustering to reveal trainees’ behavior in cybersecurity education},
url = {https://doi.org/10.1007/s10639-024-12480-x},
volume = {2024},
year = {2024}
}
TY - JOUR
ID - 2373677
AU - Dočkalová Burská, Karolína - Mlynárik, Jakub Rudolf - Ošlejšek, Radek
PY - 2024
TI - Using data clustering to reveal trainees’ behavior in cybersecurity education
JF - Education and Information Technologies
VL - 2024
PB - Springer
SN - 13602357
KW - visual analytics
KW - clustering analysis
KW - hands-on learning
KW - visualization
UR - https://doi.org/10.1007/s10639-024-12480-x
N2 - In cyber security education, hands-on training is a common type of exercise to help raise awareness and competence, and improve students' cybersecurity skills. To be able to measure the impact of the design of the particular courses, the designers need methods that can reveal hidden patterns in trainee behavior. However, the support of the designers in performing such analytic and evaluation tasks is ad-hoc and insufficient. With unsupervised machine learning methods, we designed a tool for clustering the trainee actions that can exhibit their strategies or help pinpoint flaws in the training design. By using a \emph{k-means++} algorithm, we explore clusters of trainees that unveil their specific behavior within the training sessions. The final visualization tool consists of views with scatter plots and radar charts. The former provides a two-dimensional correlation of selected trainee actions and displays their clusters. In contrast, the radar chart displays distinct clusters of trainees based on their more specific strategies or approaches when solving tasks. Through iterative training redesign, the tool can help designers identify improper training parameters and improve the quality of the courses accordingly. To evaluate the tool, we performed a qualitative evaluation of its outcomes with cybersecurity experts. The results confirm the usability of the selected methods in discovering significant trainee behavior. Our insights and recommendations can be beneficial for the design of tools for educators, even beyond cyber security.
ER -
DOČKALOVÁ BURSKÁ, Karolína, Jakub Rudolf MLYNÁRIK and Radek OŠLEJŠEK. Using data clustering to reveal trainees’ behavior in cybersecurity education. \textit{Education and Information Technologies}. Springer, 2024, vol.~2024, 27 pp. ISSN~1360-2357. Available from: https://dx.doi.org/10.1007/s10639-024-12480-x.
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