Abstract
The promise of dynamic difficulty adjustment in personalized learning is discussed, along with the potential of low-cost electroencephalography (EEG) headsets to provide quick, continuous player feedback. This is followed by the results of a study involving these elements in the context of a cybersecurity educational video game. Player actions and EEG readings were recorded, along with a pretest and posttest of student knowledge and opinions regarding information security awareness and perceived immersion. This study employed Brute Force, a tower defense game that teaches players to choose strong, unique, and memorable passwords. Participants reported significantly more responsible attitudes regarding the importance of strong, unique passwords. More successful players who played the full 15 min tended to improve at identifying strong passwords from a list. After playing the game, participants were most likely to add password length and uniqueness as important password strategies.
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References
R. Hunicke. The case for dynamic difficulty adjustment in games, in Proceedings of the 2005 ACM SIGCHI International Conference on Advances in Computer Entertainment Technology, ACM, pp. 429–433, 2005
G. Francia III, D. Thornton, M. Trifas, T. Bowden, Gamification of information security awareness training, in Emerging Trends in ICT Security, (Morgan Kaufmann, Boston, 2014), pp. 85–97
M. Mayo, Games for science and engineering education. Commun. ACM 50(7), 30–35 (2007)
P. Felicia, Developments in Current Game-Based Learning Design and Deployment (IGI Global, Hershey, PA, 2012)
A. Smith, What the Public Knows About Cybersecurity (Pew Research Center on Internet and American Life, Washington, DC, 2017)
J.P. Gee, Good Video Games + Good Learning: Collected Essays on Video Games, Learning, and Literacy, vol 27 (Peter Lang, New York, 2007)
M. Csíkszentmihályi, Finding Flow: The Psychology of Engagement with Everyday Life (Basic Books, New York, 1997)
G. Andrade, G. Ramalho, H. Santana, V. Corruble, Extending reinforcement learning to provide dynamic game balancing, in Proceedings of the Workshop on Reasoning, Representation, and Learning in Computer Games, 19th International Joint Conference on Artificial Intelligence (IJCAI), (2005), pp. 7–12
P. Demasi, J. de O Adriano, On-line coevolution for action games. Int. J. Intellig. Games Simul. 2(2) (2003)
K. Blinowska, P. Durka, Electroencephalography (EEG), in Wiley Encyclopedia of Biomedical Engineering, (Wiley, Hoboken, NJ, 2006)
Emotiv, What Is Emotiv Insight? (December 2018). https://www.emotiv.com/knowledge-base/what-is-emotiv-insight/. Accessed 1 Apr 2020
D. Wheeler, zxcvbn: Realistic password strength estimation. Dropbox Tech Blog, 2012
C. Fox, J.H. Brockmyer, The development of the game engagement questionnaire: A measure of engagement in video game playing: Response to reviews. Interact. Comput. 25(4), 290–293 (2013)
A. McCormac, D. Calic, M. Butavicius, K. Parsons, T. Zwaans, M. Pattinson, A reliable measure of information security awareness and the identification of bias in responses. Australas. J. Inf. Syst. 21 (2017)
C. Heunis, Export and Analysis of Emotiv Insight EEG Data via EEGLab (2016)
D. Afergan, E. Peck, E. Solovey, A. Jenkins, S. Hincks, E. Brown, R. Chang, R. Jacob. Dynamic difficulty using brain metrics of workload, in Proceedings of the 32nd Annual ACM Conference on Human Factors in Computing Systems, ACM, pp. 3797—3806, 2014
K. Mikami, K. Kondo, et al. Adaptable game experience based on player’s performance and EEG, in Nicograph International (NicoInt), 2017, IEEE, pp. 1–8, 2017
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Thornton, D., Turley, F. (2020). Dynamic Difficulty Adjustment in Cybersecurity Awareness Games. In: Daimi, K., Francia III, G. (eds) Innovations in Cybersecurity Education. Springer, Cham. https://doi.org/10.1007/978-3-030-50244-7_1
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DOI: https://doi.org/10.1007/978-3-030-50244-7_1
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