Abstract
AbstractComputer-supported learning technologies are essential for conducting hands-on cybersecurity training. These technologies create environments that emulate a realistic IT infrastructure for the training. Within the environment, training participants use various software tools to perform offensive or defensive actions. Usage of these tools generates data that can be employed to support learning. This paper investigates innovative methods for leveraging the trainee data to provide automated feedback about the performed actions. We proposed and implemented feedback software with four modules that are based on analyzing command-line data captured during the training. The modules feature progress graphs, conformance analysis, activity timeline, and error analysis. Then, we performed field studies with 58 trainees who completed cybersecurity training, used the feedback modules, and rated them in a survey. Quantitative evaluation of responses from 45 trainees showed that the feedback is valuable and supports the training process, even though some features are not fine-tuned yet. The graph visualizations were perceived as the most understandable and useful. Qualitative evaluation of trainees’ comments revealed specific aspects of feedback that can be improved. We publish the software as an open-source component of the KYPO Cyber Range Platform. Moreover, the principles of the automated feedback generalize to different learning contexts, such as operating systems, networking, databases, and other areas of computing. Our results contribute to applied research, the development of learning technologies, and the current teaching practice.
Publisher
Springer Science and Business Media LLC
Subject
Library and Information Sciences,Education
Reference90 articles.
1. Agudo, I., Rios, R., & Nieto, A. (2019). Personalized computer security tasks with automatic evaluation and feedback. Proceedings of the 2019 SIGED International conference on information systems education and research. Association for Information Systems (AIS). Retrieved from https://www.nics.uma.es/pub/papers/1835.pdf
2. Arends, H., Keuning, H., Heeren, B., & Jeuring, J. (2017). An intelligent tutor to learn the evaluation of microcontroller I/O programming expressions. In Proceedings of the 17th Koli calling international conference on computing education research (pp. 2–9). New York, NY, USA: Association for computing machinery. Retrieved from https://doi.org/10.1145/3141880.3141884
3. Baniassad, E., Zamprogno, L., Hall, B., & Holmes, R. (2021). Stop the (autograder) insanity: Regression penalties to deter autograder overreliance. In Proceedings of the 52nd ACM Technical symposium on computer science education (pp. 1062–1068). New York, NY, USA: Association for computing machinery. Retrieved from. https://doi.org/10.1145/3408877.3432430
4. Bezáková, I., Hemaspaandra, E., Lieberman, A., Miller, H., & Narváez, D. E. (2020). Prototype of an automated feedback tool for intro CS theory. In Proceedings of the 51st ACM Technical symposium on computer science education (p. 1311). New York, NY, USA: Association for computing machinery. Retrieved from https://doi.org/10.1145/3328778.3372598
5. Bodily, R., & Verbert, K. (2017). Review of research on student-facing learning analytics dashboards and educational recommender systems. IEEE Transactions on Learning Technologies, 10(4), 405–418. Retrieved from https://doi.org/10.1109/TLT.2017.2740172
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献