Detecting Unsuccessful Students in Cybersecurity Exercises in Two Different Learning Environments

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F24%3A00136815" target="_blank" >RIV/00216224:14330/24:00136815 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1109/FIE61694.2024.10893135" target="_blank" >http://dx.doi.org/10.1109/FIE61694.2024.10893135</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/FIE61694.2024.10893135" target="_blank" >10.1109/FIE61694.2024.10893135</a>

Result language

angličtina

Original language name

Detecting Unsuccessful Students in Cybersecurity Exercises in Two Different Learning Environments

Original language description

This full paper in the research track evaluates the usage of data logged from cybersecurity exercises in order to predict students who are potentially at risk of performing poorly. Hands-on exercises are essential for learning since they enable students to practice their skills. In cybersecurity, hands-on exercises are often complex and require knowledge of many topics. Therefore, students may miss solutions due to gaps in their knowledge and become frustrated, which impedes their learning. Targeted aid by the instructor helps, but since the instructor's time is limited, efficient ways to detect struggling students are needed. This paper develops automated tools to predict when a student is having difficulty. We formed a dataset with the actions of 313 students from two countries and two learning environments: KYPO CRP and EDURange. These data are used in machine learning algorithms to predict the success of students in exercises deployed in these environments. After extracting features from the data, we trained and cross-validated eight classifiers for predicting the exercise outcome and evaluated their predictive power. The contribution of this paper is comparing two approaches to feature engineering, modeling, and classification performance on data from two learning environments. Using the features from either learning environment, we were able to detect and distinguish between successful and struggling students. A decision tree classifier achieved the highest balanced accuracy and sensitivity with data from both learning environments. The results show that activity data from cybersecurity exercises are suitable for predicting student success. In a potential application, such models can aid instructors in detecting struggling students and providing targeted help. We publish data and code for building these models so that others can adopt or adapt them.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

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

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2024

Confidentiality

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

Article name in the collection

Proceedings of the 54th IEEE Frontiers in Education Conference (FIE 2024)

ISBN

9798350363067

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

Washington, D.C., USA

Event date

Jan 1, 2024

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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