D 2022

Evaluating Two Approaches to Assessing Student Progress in Cybersecurity Exercises

ŠVÁBENSKÝ, Valdemar; Richard WEISS; Jack COOK; Jan VYKOPAL; Pavel ČELEDA et. al.

Basic information

Original name

Evaluating Two Approaches to Assessing Student Progress in Cybersecurity Exercises

Authors

ŠVÁBENSKÝ, Valdemar; Richard WEISS; Jack COOK; Jan VYKOPAL; Pavel ČELEDA; Jens MACHE; Radoslav CHUDOVSKÝ and Ankur CHATTOPADHYAY

Edition

New York, NY, USA, Proceedings of the 53rd ACM Technical Symposium on Computer Science Education (SIGCSE '22), p. 787-793, 7 pp. 2022

Publisher

ACM

Other information

Language

English

Type of outcome

Proceedings paper

Country of publisher

United States of America

Confidentiality degree

is not subject to a state or trade secret

Publication form

electronic version available online

References:

URL URL

Organization

Ústav výpočetní techniky – Repository – Repository

ISBN

978-1-4503-9070-5

DOI

http://dx.doi.org/10.1145/3478431.3499414

UT WoS

000884263800114

EID Scopus

2-s2.0-85126106998

Keywords in English

cybersecurity education; command-line history; educational data mining; learning analytics; assessment; modeling

Links

EF16_019/0000822, research and development project. MUNI/A/1520/2021, interní kód Repo.
Changed: 31/3/2023 04:07, RNDr. Daniel Jakubík

Abstract

V originále

Cybersecurity students need to develop practical skills such as using command-line tools. Hands-on exercises are the most direct way to assess these skills, but assessing students' mastery is a challenging task for instructors. We aim to alleviate this issue by modeling and visualizing student progress automatically throughout the exercise. The progress is summarized by graph models based on the shell commands students typed to achieve discrete tasks within the exercise. We implemented two types of models and compared them using data from 46 students at two universities. To evaluate our models, we surveyed 22 experienced computing instructors and qualitatively analyzed their responses. The majority of instructors interpreted the graph models effectively and identified strengths, weaknesses, and assessment use cases for each model. Based on the evaluation, we provide recommendations to instructors and explain how our graph models innovate teaching and promote further research. The impact of this paper is threefold. First, it demonstrates how multiple institutions can collaborate to share approaches to modeling student progress in hands-on exercises. Second, our modeling techniques generalize to data from different environments to support student assessment, even outside the cybersecurity domain. Third, we share the acquired data and open-source software so that others can use the models in their classes or research.
Displayed: 6/7/2025 17:23