Abstract

Practical hands-on exercises for trainees in information technologies and information systems provide tools needed to develop, operate, and test cloud-based infrastructures. Exercises, frequently carried out in simulated settings, offer a practical approach to highlight the significance of skills related to structured decision-making and detailed configuration in the command line. Therefore, the paper proposes a unique data analysis solution that encompasses safe sandboxing and examines user-provided command-line data throughout the exercises. The development of the solution focuses on command-line input parsing, tokenization, and structured analysis, providing a viewpoint on the knowledge level in simulated scenarios. The work provides insight into structured command-line data analysis and the complexities of command execution. The prototype is based on modular Bourne-Again Shell and Python modules and asynchronous data collection. The paper contributes to the educational processes by improving training performance assessment techniques and offering insights into the exemplified field of penetration testing of information systems.

Recommended Citation

Bauraitė, A., Brilingaitė, A. & Bukauskas, L. (2024). Designing Trainee Performance Assessment System for Hands-On Exercises. In B. Marcinkowski, A. Przybylek, A. Jarzębowicz, N. Iivari, E. Insfran, M. Lang, H. Linger, & C. Schneider (Eds.), Harnessing Opportunities: Reshaping ISD in the post-COVID-19 and Generative AI Era (ISD2024 Proceedings). Gdańsk, Poland: University of Gdańsk. ISBN: 978-83-972632-0-8. https://doi.org/10.62036/ISD.2024.34

Paper Type

Full Paper

DOI

10.62036/ISD.2024.34

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Designing Trainee Performance Assessment System for Hands-On Exercises

Practical hands-on exercises for trainees in information technologies and information systems provide tools needed to develop, operate, and test cloud-based infrastructures. Exercises, frequently carried out in simulated settings, offer a practical approach to highlight the significance of skills related to structured decision-making and detailed configuration in the command line. Therefore, the paper proposes a unique data analysis solution that encompasses safe sandboxing and examines user-provided command-line data throughout the exercises. The development of the solution focuses on command-line input parsing, tokenization, and structured analysis, providing a viewpoint on the knowledge level in simulated scenarios. The work provides insight into structured command-line data analysis and the complexities of command execution. The prototype is based on modular Bourne-Again Shell and Python modules and asynchronous data collection. The paper contributes to the educational processes by improving training performance assessment techniques and offering insights into the exemplified field of penetration testing of information systems.