Advertisement

Improving Learning Efficiency and Evaluation Fairness for Cyber Security Courses: A Case Study

  • Emin Çalışkan
  • Risto VaarandiEmail author
  • Birgy Lorenz
Conference paper
  • 543 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 998)

Abstract

During the last decade, active learning and e-learning methods have become increasingly popular, and a number of universities have redesigned their courses by replacing lectures with online content. Since traditional lecture-based teaching methods have been criticized in recent research papers, we have studied how well a lecture-based approach suits for teaching a highly technical cyber security course. Our study is based on the analysis of survey data collected from past students who have taken the same cyber security course during the last seven years, and our findings provide insights for redesigning similar network and cyber security courses.

Keywords

Cyber security education Teaching methods E-learning 

References

  1. 1.
    Adam, D., Kioutsiouki, D., Karakostas, A., Demetriadis, S.N.: Do your students get it? Quiz it! The android classroom response system. In: IEEE 14th International Conference on Advanced Learning Technologies, pp. 168–170. IEEE (2014)Google Scholar
  2. 2.
    Akakura, T., Kawamata, T., Kato, K.: Development of a blended learning system for engineering students studying intellectual property law, and an analysis of the relationship between system usage and the knowledge acquisition process. In: IEEE 6th International Conference on Teaching, Assessment, and Learning for Engineering (TALE), pp. 114–117. IEEE (2017)Google Scholar
  3. 3.
    Bajwa, W.U.: Flipping large classes on a shoestring budget. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 7006–7010. IEEE (2018)Google Scholar
  4. 4.
    Brandsteidl, M., Mayerhofer, T., Seidl, M., Huemer, C.: Replacing traditional classroom lectures with lecture videos: an experience report. In: Proceedings of the 8th edition of the Educators’ Symposium, pp. 21–27. ACM (2012)Google Scholar
  5. 5.
    Grotz, N.: Fair exams in split groups: (Implementation of equitable computer-based exams for large groups in small test centers). In: The Global Engineering Education Conference (EDUCON), pp. 957–962. IEEE (2018)Google Scholar
  6. 6.
    Guy, R., Lownes-Jackson, M.: Web-based tutorials and traditional face-to-face lectures: a comparative analysis of student performance. In: Proceedings of the Informing Science and Information Technology Education Conference, Informing Science Institute (2013)Google Scholar
  7. 7.
    Hammer, S., Hobelsberger, M., Braun, G.: Using laboratory examination to assess computer programming competences: questionnaire-based evaluation of the impact on students. In: The Global Engineering Education Conference (EDUCON), pp. 355–363. IEEE (2018)Google Scholar
  8. 8.
    Hui, W., Hu, P.H., Clark, T.H., Tam, K.Y., Milton, J.: Technology-assisted learning: a longitudinal field study of knowledge category, learning effectiveness and satisfaction in language learning. J. Comput. Assist. Learn. 24(3), 245–259 (2008)CrossRefGoogle Scholar
  9. 9.
    Jackowska-Strumiłło, L., Nowakowski, J., Strumiłło, P., Tomczak, P.: Interactive question-based learning methodology and clickers: fundamentals of computer science course case study. In: The 6th International Conference on Human System Interaction (HSI), pp. 439–442. IEEE (2013)Google Scholar
  10. 10.
    LoPresto, M.C., Slater, T.F.: A new comparison of active learning strategies to traditional lectures for teaching college astronomy. J. Astron. Earth Sci. Educ. 3(1), 59–76 (2016)Google Scholar
  11. 11.
    Miller, C.J., McNear, J., Metz, M.J.: A comparison of traditional and engaging lecture methods in a large, professional-level course. Adv. Physiol. Educ. 37(4), 347–355 (2013)CrossRefGoogle Scholar
  12. 12.
    Özalp-Yaman, Ş., Çağiltay, N.E.: Paper-based versus computer-based testing in engineering education. In: Education Engineering (EDUCON), pp. 1631–1637. IEEE (2010)Google Scholar
  13. 13.
    Schmidt, B.: Improving motivation and learning outcome in a flipped classroom environment. In: 2014 International Conference on Interactive Collaborative Learning (ICL), pp. 689–690. IEEE (2014)Google Scholar
  14. 14.
    Seeling, P., Eickholt, J.: Levels of active learning in programming skill acquisition: from lecture to active learning rooms. In: 2017 IEEE Frontiers in Education Conference (FIE), pp. 1–5. IEEE (2017)Google Scholar
  15. 15.
    Shryock, K.J.: Engaging students inside the classroom to increase learning. In: Frontiers in Education Conference (FIE), pp. 1–7. IEEE (2015)Google Scholar
  16. 16.
    Winterstein, T., Greiner, F., Schlaak, H.F., Pullich, L.: A blended-learning concept for basic lectures in electrical engineering: a practical report. In: 2012 International Conference on Education and e-Learning Innovations (ICEELI), pp. 1–4. IEEE (2012)Google Scholar
  17. 17.
    Zhang, D.: Interactive multimedia-based e-learning: a study of effectiveness. Am. J. Distance Educ. 19(3), 149–162 (2005)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Bloom, B.S., Engelhart, M.D., Furst, E.J., Hill, W.H., Krathwohl, D.R.: Taxonomy of educational objectives: the classification of educational goals: handbook I: cognitive domain (No. 373.19 C734t). New York, USA (1956)Google Scholar
  19. 19.
    Anderson, L.W., Krathwohl, D.R., Airasian, P.W., Cruikshank, K.A., Mayer, R.E., Pintrich, P.R., Raths, J., Wittrock, M.C.: A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom’s Taxonomy of Educational Objectives, abridged edition. White Plains, New York (2001)Google Scholar
  20. 20.
    Moses, K.V., Petullo, W.M.: Teaching computer security (2014)Google Scholar
  21. 21.
    Vasserman, E.Y., Bell, R.S., Sayre, E.C.: Developing and Piloting a Quantitative Assessment Tool for Cybersecurity Courses. American Society for Engineering Education (2015)Google Scholar
  22. 22.
    Merrill, M.D.: First principles of instruction. Educ. Technol. Res. Dev. 50(3), 43–59 (2002)CrossRefGoogle Scholar
  23. 23.
    LearnDash WordPress LMS Plugin. https://www.learndash.com. Accessed 12 Oct 2018
  24. 24.
    Rsyslog home page. https://www.rsyslog.com. Accessed 25 Nov 2018
  25. 25.
    Netfilter home page. https://www.netfilter.org. Accessed 25 Nov 2018
  26. 26.
    Hansen, S.E., Atkins, E.T.: Automated system monitoring and notification with Swatch. In: USENIX 7th System Administration Conference, pp. 145–152 (1993)Google Scholar
  27. 27.
    Syslog-ng home page. https://www.syslog-ng.com. Accessed 25 Nov 2018
  28. 28.
    Vaarandi, R., Blumbergs, B., Çalışkan, E.: Simple event correlator – best practices for creating scalable configurations. In: 2015 IEEE CogSIMA Conference, pp. 96–100. IEEE (2015)Google Scholar
  29. 29.
    Snort home page. https://www.snort.org. Accessed 25 Nov 2018

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Emin Çalışkan
    • 1
  • Risto Vaarandi
    • 1
    Email author
  • Birgy Lorenz
    • 1
  1. 1.TalTech UniversityTallinnEstonia

Personalised recommendations