Community Aware Content Adaptation for Mobile Technology Enhanced Learning

  • Ralf Klamma
  • Marc Spaniol
  • Yiwei Cao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4227)


Mobile technology enhanced learning pertains to the delivery of multimedia learning resource onto mobile end devices such as cell phones and PDAs. It also aims at supporting personalized adaptive learning in a community context. This paper presents a novel approach to supporting both aspects. The community aware content adaptation employs the MPEG-7 and MPEG-21 multimedia metadata standards to present the best possible information to mobile end devices. Meanwhile, interest patterns are derived from a community aware context analysis. We designed and developed a technology enhanced learning platform supporting architecture professionals’ study at city excursions and other mobile tasks.


Recommender System Multimedia Content Mobile Technology Mobile Learning Device Capability 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Adomavicius, G., Tuzhilin, A.: Personalization technologies: A process-oriented perspective. Commun. ACM 48(10), 83–90 (2005)CrossRefGoogle Scholar
  2. 2.
    Anido, L.E., Fernandez, M.J., Caeiro, M., Santos, J.M., Rodriguez, J.S., Llamas, M.: Educational metadata and brokerage for learning resources. Comput. Educ. 38(4), 351–374 (2002)CrossRefGoogle Scholar
  3. 3.
    Bickmore, T.W., Schilit, B.: Digestor - device-independent access to the world wide web. In: Proc. of the 6th Intl. WWW Conf., Santa Clara, CA, USA, pp. 1075–1082 (1997)Google Scholar
  4. 4.
    Blom, J., Chipchase, J., Lehikoinen, J.: Contextual and cultural challenges for user mobility research. Commun. ACM 48(7), 37–41 (2005)CrossRefGoogle Scholar
  5. 5.
    Bormans, J., Hill, K.: MPEG-21 Overview v.5 (2002),
  6. 6.
    Böszörmenyi, L., Kosch, H., Hellwagner, H., Libsie, M., Podlipnig, S.: Metadata driven adaptation in the ADMITS project. Signal Processing Image Communication 18(8), 749–766 (2003)CrossRefGoogle Scholar
  7. 7.
    Brown, K.: How does the Java logging API stack up against log4j? (July 2002),
  8. 8.
    Brusilovsky, P., Nejdl, W.: Adaptive hypermedia and adaptive web. CRC Press, Boca Raton (2004)Google Scholar
  9. 9.
    Burnett, I., Van de Walle, R., Hill, K., Bormans, J., Pereira, F.: MPEG-21: goals and achievements. IEEE Multimedia 10(4), 60–70 (2003)CrossRefGoogle Scholar
  10. 10.
    Fuller, U., Amillo, J., Laxer, C., McCracken, W.M., Mertz, J.: Facilitating student learning through study abroad and international projects. SIGCSE Bull. 37(4), 139–151 (2005)CrossRefGoogle Scholar
  11. 11.
    Gross, T., Specht, M.: Aspekte und Komponenten der Kontextmodellierung. i-com, 12–16 (2002)Google Scholar
  12. 12.
    Hagen, P., Robertson, T., Kan, M., Sadler, K.: Emerging research methods for under-standing mobile technology use. In: Proceedings of OZCHI 2005, Canberra, Australia, November 23-25 (2005)Google Scholar
  13. 13.
    ISO. Information technology – multimedia content description interface – part 5: Multimedia description schemes. Technical Report ISO/IEC 15938-5:2003, Intl. Organisation for Standardisation / Intl. Electrotechnical Commission (2003)Google Scholar
  14. 14.
    Jain, A., Murty, M.N., Flynn, P.: Data clustering: a review. ACM Computing Surveys 31(3), 264–323 (1999)CrossRefGoogle Scholar
  15. 15.
    Jannach, D., Leopold, K.: Knowledge-based multimedia adaptation for ubiquitous multimedia consumption. Journal of Network and Computer Applications, Special issue on Intelligence-based adaptation for ubiquitous multimedia communications (2006)Google Scholar
  16. 16.
    Keegan, D.: The future of learning: From elearning to mlearning. Technical report, Fern Univ., Hagen (Germany). Inst. for Research into Distance Education., Postfach 940, D-58084 Hagen, Germany (2002)Google Scholar
  17. 17.
    Klamma, R., Spaniol, M., Jarke, M., Cao, Y., Jansen, M., Toubekis, G.: ACIS: Intergenerational community learning supported by a hypermedia sites and monuments database. In: Goodyear, P., Sampson, D.G., Yang, D.J.-T., Kinshuk, Okamoto, T., Hartley, R., Chen, N.-S. (eds.) Proc. of the 5th International Conference on Advanced Learning Technologies (ICALT 2005), Kaohsiung, Taiwan, July 5-8, pp. 108–112. IEEE Computer Society, Los Alamitos (2005)CrossRefGoogle Scholar
  18. 18.
    Kosch, H.: Distributed Multimedia Database Technologies Supported by MPEG-7 and MPEG-21. CRC Press, Boca Raton (2003)CrossRefGoogle Scholar
  19. 19.
    Kravcik, M., Kaibel, A., Specht, M., Terrenghi, L.: Mobile collector for field trips. Educational Technology & Society 7(2), 25–33 (2004)Google Scholar
  20. 20.
    Lutkenhouse, T., Nelson, M.L., Bollen, J.: Distributed, real-time computation of community preferences. In: HYPERTEXT 2005: Proc. of the 16th ACM Conference on Hypertext and Hypermedia, pp. 88–97. ACM Press, New York (2005)CrossRefGoogle Scholar
  21. 21.
    Martinez, J.M., Gonzalez, C., Fernandez, O., Garcia, C., de Ramon, J.: Towards universal access to content using MPEG-7. In: Proceedings of the 10th ACM International Conference on Multimedia, pp. 199–202. ACM Press, New York (2002)CrossRefGoogle Scholar
  22. 22.
    Mohan, R., Smith, J.R., Li, C.-S.: Adapting multimedia internet content for universal access. IEEE Transactions on Multimedia 1(1), 104–114 (1999)CrossRefGoogle Scholar
  23. 23.
    O’Connor, M., Herlocker, J.: Clustering items for collaborative filtering. Technical report, Univ. of Minnesota, Dept. of Computer Science, USA (2000)Google Scholar
  24. 24.
    Sampath, L.L., Helal, A., Smith, J.: UMA-based wireless and mobile video delivery architecture. In: Proceedings of the SPIE Voice, Video and Data Communications Symposium (November 2000)Google Scholar
  25. 25.
    Schmidt, A., Beigl, M., Gellersen, H.: There is more to context than location. Computers & Graphics 23(6), 893–902 (1999)CrossRefGoogle Scholar
  26. 26.
    Somlo, G., Howe, A.: Incremental Clustering for Profile Maintenance in Information Gathering Web Agents. In: Müller, J., Andre, E., Sen, S., Frasson, C. (eds.) Proc. of the 5th Intl. Conf. on Autonomous Agents, pp. 262–269 (2001)Google Scholar
  27. 27.
    Steiger, O., Ebrahimi, T., Sanjuan, D.: Mpeg-based personalized content delivery. In: IEEE Int. Conf. on Image Processing, ICIP 2003, Barcelona, Spain. IEEE, Los Alamitos (2003)Google Scholar
  28. 28.
    Sung, M., Gips, J., Eagle, N., Madan, A., Caneel, R., De Vaul, R., Bonsen, J., Pentland, S.: Mit. edu: M-learning applications for classroom settings. Technical Report Technical Note 576, MIT Media Laboratory (2004)Google Scholar
  29. 29.
    Tane, J., Schmitz, C., Stumme, G.: Semantic resource management for the web: an e-learning application. In: WWW Alt. 2004: Proc. of the 13th Intl. World Wide Web Conf. - Alternate track, pp. 1–10. ACM Press, New York (2004)CrossRefGoogle Scholar
  30. 30.
    Ujjin, S., Bentley, P.J.: Building a lifestyle recommender system. In: WWW10: Proceedings of the 10th Intl. World Wide Web Conf. (2001)Google Scholar
  31. 31.
    Wenger, E.: Communities of Practice: Learning, Meaning, and Identity. Cambridge University Press, Cambridge (1998)Google Scholar
  32. 32.
    Zaharias, P.: Usability and e-learning: the road towards integration. eLearn 2004(6), 4 (2004)CrossRefGoogle Scholar
  33. 33.
    Zhao, G., Yang, Z.: Learning resource adaptation and delivery framework for mobile learning. In: Proceedings of 35th ASEE/IEEE Frontiers in Education Conference, Indianapolis, USA. IEEE, Los Alamitos (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ralf Klamma
    • 1
  • Marc Spaniol
    • 1
  • Yiwei Cao
    • 1
  1. 1.Lehrstuhl Informatik VRWTH Aachen UniversityGermany

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