An Intelligent Context-Aware Learning System Based on Mobile Augmented Reality

  • Jin-Il Kim
  • Inn-woo Park
  • Hee-Hyol Lee
Part of the Communications in Computer and Information Science book series (CCIS, volume 151)


Learning content using context-aware mobile technology, whether the content is manual or interactive, is hardly expected to arouse learners to interest or immersion because most of real-life environment is discrete from mobile content. For this reason, Augmented Reality is used to fix the drawback and to provide learners with an educational environment fit for desirable practice of the theory of situated learning. Increasing interest in Augmented Reality in recent years has led to multiple study efforts to build applications based on Augmented Reality, most of which require additional hardware or software, resulting in difficulties in establishing proper learning environment in the field of education. Therefore, in this paper we propose an intelligent context-aware learning system based on mobile Augmented Reality that provides a hassle-free desirable learning environment requiring nothing but a common mobile device.


Mobile Situated Learning Context-Aware Mobile Augmented Reality Intelligent Agent 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Azuma, R., Baillot, Y., Behringer, R., Feiner, S., Julier, S., MacIntyre, B.: Recent advances In augmented reality. IEEE Computer Graphics and Applications 25(6), 34–47 (2001)CrossRefGoogle Scholar
  2. 2.
    Höllerer, T., Feiner, S., Terauchi, T., Rashid, G., Ha11away, D.: Exploring MARS: Developing indoor and outdoor user interfaces to a mobile augmented rea1ity system. Computers & Graphics 23(6), 779–785 (1999)CrossRefGoogle Scholar
  3. 3.
    Woo, W., Jeon, M.-G., Nam, T.-J., Lee, S.-G., Cho, W.-D.: CAMAR, JinhanM&BGoogle Scholar
  4. 4.
    KERIS, Research on Using Augmented Reality for Interactive Educational Digital Contents (2005)Google Scholar
  5. 5.
    Cognitive and Technology Group at Vanderbilt. Anchored instruction and situated cognition revisited. Educational Technology 33(3), 52–70 (1993)Google Scholar
  6. 6.
    Mitchell, T., Caruana, R., Freitag, D., Dermott, J.M., Zabowski, D.: Experience with a learning personal assistant. Communication of the ACM 37(7), 80–91 (1994)CrossRefGoogle Scholar
  7. 7.
    Thomas, E.R., John, H.G., Henrik, E., Angel, R.P., Samson, W.T., Mark, A.M.: Reusable ontologies, knowledge-acquisition tools, and performance systems: PROTE’GE’-II solutions to Sisyphus-2. International Journal of Human-Computer Studies 44(3-4), 303–332 (1996)CrossRefGoogle Scholar
  8. 8.
    Hong, J.H., Cho, S.B.: Machine Learning and Intelligent Agents. The Korean Institute of Information Scientists and Engineers 25(3), 64–69 (2007)Google Scholar
  9. 9.
    Lee, C.S., Pan, C.Y.: An intelligent fuzzy agent for meeting scheduling decision support system. Fuzzy Sets and Systems 142(3), 467–488 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Hsinchun, C., Yi-Ming, C., Ramsey, M., Yang, C.: An intelligent personal spider(agent) for dynamic internet/intranet searching. Decision Support Systems 23(1), 41–58 (1998)CrossRefGoogle Scholar
  11. 11.
    Horvitz, E.J., Breese, D., Heckerman, D., Hovel, D., Rommelse, K.: The Lumiere project, Bayesian user modeling for inferring the goals and needs of software users. In: Proceedings of the 14th Conference on Uncertainty in n Artificial Intelligence, pp. 256–265 (1998)Google Scholar
  12. 12.
    Hamdi, M.S.: MASACAD: A multi agent-based approach to information customization. IEEE Intelligent Systems 21(1), 60–67 (2006)CrossRefGoogle Scholar
  13. 13.
  14. 14.
  15. 15.
  16. 16.
    Wagner, D., Barakonyi, I.: Augmented Reality Kanji Learning. In: Proceedings of the 2nd IEEE/ACM Symposium on Mixed and Augmented Reality (ISMAR 2003), pp. 335–336 (2003)Google Scholar
  17. 17.
    Ha, T., Woo, W.: Video see-through HMD based Hand Interface for Augmented Reality, pp. 169–174 (2006)Google Scholar
  18. 18.
    Khan, L., McLeod, D., Hovy, E.H.: Retrieval effectiveness of an ontology-based model for information selection. VLDB J. 13(1), 71–85 (2004)CrossRefGoogle Scholar
  19. 19.
    Kim, J.-i., Lee, Y.-H., Lee, H.-H.: Development of a mobile language learning assistant system based on smartphone. In: Kim, T.-h., Vasilakos, T., Sakurai, K., Xiao, Y., Zhao, G., Ślęzak, D. (eds.) FGCN 2010. CCIS, vol. 120, pp. 321–329. Springer, Heidelberg (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jin-Il Kim
    • 1
  • Inn-woo Park
    • 2
  • Hee-Hyol Lee
    • 3
  1. 1.Dept. of Electronic Eng.Hannam UniversityDaejonKorea
  2. 2.Dept. of EducationKorea UniversitySeoulKorea
  3. 3.The Graduate School of Information, Production and SystemsWaseda UniversityFukuokaJapan

Personalised recommendations