Building a self-evolving iMonsters board game for cyber-security education

ABSTRACT

In this paper, to handle the problem of the quick evolution of cyber-security attacks, we developed the iMonsters board game and proposed the attack and defense knowledge self-evolving algorithm. Three versions of the iMonsters were launched in 2013, 2017, and 2019, respectively. Accordingly, the cyber-security ontology can be refined by the ontology fusion-or-splitting procedure for the newly collected cyber-security incidents, as well as the roles and rules of the iMonsters can be refined by the gaming portfolio mining procedure for the collected portfolio. Furthermore, we conducted game-based learning (GBL), a quasi-experiment of pre/post-testing, and concept map testing using the iMonsters board game in a children’s summer camp. The experimental results indicate that the students can acquire up-to-date cyber-security knowledge with the iMonsters better than students who learn in a traditional classroom setting, and the students’ satisfaction with acquiring cyber-security knowledge with the instructional design of the iMonsters is better compared with learning in a traditional classroom setting. Satisfaction with the new version has continuously increased. Besides, the results of the in-depth interviews show that the new version was easier to learn. Thus, we may conclude that using the self-evolving iMonsters can improve learning effectiveness and participation in GBL.

KEYWORDS:

• Security education
• game-based learning
• iMonsters board game
• ontology
• concept maps

Acknowledgment

This research was partially supported by the National Science and Technology Council(NSTC)of the Republic of China, Taiwan, under grants MOST 108-2511-H-468-005-MY2 and MOST 106-2511-S-468-002-MY3. The authors would also like to thank Asia University for supporting the teaching/learning materials. Our gratitude (also) goes to [Michael Burton], Asia University.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research was partially supported by the National Science and Technology Council (NSTC) of the Republic of China, Taiwan, under grants MOST 108-2511-H-468-005-MY2 and MOST 106-2511-S-468-002-MY3.

Notes on contributors

Shian-Shyong Tseng

Shian-Shyong Tseng received the Ph.D. degree in computer engineering from National Chiao Tung University in 1984. From 1991 to 1992 and 1996 to 1998, he acted as the Chairman of Department of Computer and Information Science at the same university. From 1992 to 1996, he was the Director of the Computer Center at Ministry of Education.

He was the Dean of the College of Computer Science, Asia University from 2005 to 2008. He now acts as a Vice President of ASIA University, and is a Chair Professor there.

In Dec. 1999, he founded Taiwan Network Information Center (TWNIC), and was the Chairman of the board of directors of TWNIC over 12 years. He was also a science and technology consultant of Ministry of Education from 2001 to 2005. He received TANET Lifetime Achievement Award in 2018.

Dr. Tseng is an Editor-in-Chief of International Journal of Digital Learning Technology. His current research interests include artificial intelligence, data mining, computer-assisted learning, and Internet-based applications.

Tsung-Yu Yang

Tsung-Yu Yang received his Master degree in Department of M-Commerce and Multimedia Applications from Asia University, Taiwan, R.O.C. in 2016.

He studies a Ph.D about Department of Computer Science & Information Engineering, Asia University, Taiwan, R.O.C.

His current research interests include cyber security, data mining and game-based learning.

Wen-Chung Shih

Wen-Chung Shih received the Ph.D. degree in computer science from the National Chiao Tung University in 2007. From 2008 to 2014, he has been on the faculty of the Department of Applied Informatics and Multimedia at Asia University. From August 2014 to 2019, he has been on the faculty of the Department of Computer Science and Information Engineering at Asia University, Taiwan, and was also an Associate Professor and Chairman there. From August 2019, he has on the faculty of the Department of M-Commerce and Multimedia Applications at Asia University, Taiwan. His research interests include e-Learning, cloud computing, expert systems and artificial intelligence.

Bo-Yang Shan

Bo-Yang Shan received his Master degree in Computer Science from Asia University, Taiwan, R.O.C. in 2019.

His current research interests include game-based learning and data mining.