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The Impact of Learning Style Adaptivity in Teaching Computer Security

Published:22 June 2015Publication History

ABSTRACT

Teaching computer security is one of the most challenging tasks in computer science, because of the need to successfully integrate abstract concepts and practical applications. Several e-learning systems have been developed to address this issue. However, they usually provide the same material in the same sequence irrespective of the characteristics of the students, such as their knowledge level and learning style. In this paper, an approach to learning style adaptivity is proposed for the teaching of computer security. An e-learning system was developed to provide more personalised and adaptive learning, based on the information perception style of the Felder-Silverman model. This is the dimension of learning style, which has received the least attention in published research. In the approach, a personalised sequence of learning material is generated based on an individual learning style. The approach is evaluated in order to determine its effectiveness in learning provision. An experiment conducted with sixty subjects produced significant results. They indicate that matching computer security learning material, according to the learning style of the students, yields significantly better learning gain and student satisfaction than without matching.

References

  1. Akbulut, Y. and Cardak, C.S.Adaptive educational hypermedia accommodating learning styles: A content analysis of publications from 2000 to 2011. Computers & Education 58, 2 (2012), 835--842. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Alshammari, M., Anane, R., and Hendley, R.Adaptivity in E-Learning Systems. The 8th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS 2014), (2014), 79--86. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Alshammari, M., Anane, R., and Hendley, R.An E-Learning Investigation into Learning Style Adaptivity. The 48th Hawaii International Conference on System Sciences (HICSS-48), (2015), pp. 11--20. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Anane, R., Purohit, K., and Theodoropoulos, G.An Animated Cryptographic Learning Object. Computer Graphics, Imaging and Visualisation, 2008. CGIV'08. Fifth International Conference on, (2008), 61--68 http://www.cs.bham.ac.uk/research/projects/lemsys/DES/. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Anane, R.The Learning Object Triangle. Advanced Learning Technologies (ICALT), 2014 IEEE 14th International Conference on, (2014), 719--721. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Brusilovsky, P. and Millán, E.User models for adaptive hypermedia and adaptive educational systems. The adaptive web, (2007), 3--53. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Brusilovsky, P.Adaptive Hypermedia for Education and Training. Adaptive Technologies for Training and Education, (2012), 46.Google ScholarGoogle ScholarCross RefCross Ref
  8. Buch, K. and Sena, C.Accommodating diverse learning styles in the design and delivery of on-line learning experiences. International Journal of Engineering Education 17, 1 (2001), 93--98.Google ScholarGoogle Scholar
  9. Chrysafiadi, K. and Virvou, M.Student modeling approaches: A literature review for the last decade. Expert Systems with Applications 40, 11 (2013), 4715--4729. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Coffield, F., Moseley, D., Hall, E., and Ecclestone, K.Learning styles and pedagogy in post-16 learning: A systematic and critical review. Learning and Skills Research Centre London, London, 2004.Google ScholarGoogle Scholar
  11. Cone, B.D., Irvine, C.E., Thompson, M.F., and Nguyen, T.D.A video game for cyber security training and awareness. Computers & Security 26, 1 (2007), 63--72. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Felder, R.M., Felder, G.N., and Dietz, E.J.The effects of personality type on engineering student performance and attitudes. Journal of Engineering Education 91, 1 (2002), 3--17.Google ScholarGoogle ScholarCross RefCross Ref
  13. Felder, R.M. and Silverman, L.K.Learning and teaching styles in engineering education. Engineering education 78, 7 (1988), 674--681.Google ScholarGoogle Scholar
  14. Felder, R.M. and Spurlin, J.Applications, reliability and validity of the index of learning styles. International Journal of Engineering Education 21, 1 (2005), 103--112.Google ScholarGoogle Scholar
  15. Hu, D. and Wang, Y.Y.Teaching Computer Security using Xen in a Virtual Environment. Information Security and Assurance, 2008. ISA 2008. International Conference on, (2008), 389--392. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Hu, J., Meinel, C., and Schmitt, M.Tele-lab IT Security: An Architecture for Interactive Lessons for Security Education. Proceedings of the 35th SIGCSE Technical Symposium on Computer Science Education, ACM (2004), 412--416. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Keefe, J.W.Learning style: An overview. Student learning styles: Diagnosing and prescribing programs, (1979), 1--17.Google ScholarGoogle Scholar
  18. Klasnja-Milicevic, A., Vesin, B., Ivanovic, M., and Budimac, Z.E-Learning personalization based on hybrid recommendation strategy and learning style identification. Computers & Education 56, 3 (2011), 885--899. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Konak, A., Clark, T.K., and Nasereddin, M.Using Kolb's Experiential Learning Cycle to improve student learning in virtual computer laboratories. Computers & Education 72, 0 (2014), 11--22. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Marsa-Maestre, I., De La Hoz, E., Gimenez-Guzman, J.M., and Lopez-Carmona, M.A.Design and evaluation of a learning environment to effectively provide network security skills. Computers & Education 69, (2013), 225--236. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Mitrovic, A.An intelligent SQL tutor on the web. International Journal of Artificial Intelligence in Education 13, 2 (2003), 173--197. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Rößling, G., Joy, M., Moreno, A., et al.Enhancing Learning Management Systems to Better Support Computer Science Education. SIGCSE Bull. 40, 4 (2008), 142--166. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Schiaffino, S., Garcia, P., and Amandi, A.eTeacher: Providing personalized assistance to e-learning students. Computers & Education 51, 4 (2008), 1744--1754. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Sharma, S.K. and Sefchek, J.Teaching information systems security courses: A hands-on approach. Computers & Security 26, 4 (2007), 290--299. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Wang, Y.-S.Assessment of learner satisfaction with asynchronous electronic learning systems. Information & Management 41, 1 (2003), 75--86. Google ScholarGoogle ScholarDigital LibraryDigital Library

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        cover image ACM Conferences
        ITiCSE '15: Proceedings of the 2015 ACM Conference on Innovation and Technology in Computer Science Education
        June 2015
        370 pages
        ISBN:9781450334402
        DOI:10.1145/2729094

        Copyright © 2015 ACM

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        Publication History

        • Published: 22 June 2015

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        ITiCSE '15 Paper Acceptance Rate54of124submissions,44%Overall Acceptance Rate552of1,613submissions,34%

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