Next Generation Tele-Teaching: Latest Recording Technology, User Engagement and Automatic Metadata Retrieval

  • Franka Grünewald
  • Haojin Yang
  • Elnaz Mazandarani
  • Matthias Bauer
  • Christoph Meinel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7946)


With the latest technological development in the last decade, new opportunities for learning environments and educational systems, such as tele-teaching, arose. Nowadays recording technology includes easy and fast workflows, high definition video recording, multiple sources and diverse output formats. With the amount of tele-teaching content growing, issues with sufficient metadata start existing. One solution is the user engagement. User engagement is based on the theory of the culture of participation and includes the usage of web 2.0 technology to activate students. This also has positive didactical side-effects. Another solution is the automatic creation of metadata. Therefore we have developed an automated framework by using video OCR (Optical Character Recognition) and ASR (Automated Speech Recognition) technologies. Indexable keywords are further extracted from those OCR and ASR transcripts.


Tele-Teaching Lecture Recording Collaborative Learning Video Analysis 


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  1. [1]
    Moritz, F., Siebert, M., Meinel, C.: Improving Search in Tele-Lecturing: Using Folksonomies as Trigger to Query Semantic Datasets to extract additional metadata. In: Proceedings of the International Conference on Web Intelligence, Mining and Semantics, WIMS 2011. ACM Press, New York (Mai 2011) ISBN 9781450301480Google Scholar
  2. [2]
    Schillings, V., Meinel, C.: tele-TASK - Teleteaching Anywhere Solution Kit. In: Proceedings of ACM SIGUCCS, Providence, USA (2002)Google Scholar
  3. [3]
    Wolf, K., Linckels, S., Meinel, C.: Teleteaching Anywhere Solution Kit (tele-TASK) Goes Mobile. In: Education, pp. 366–371 (2007)Google Scholar
  4. [4]
    Ottmann, T., Müller, R.: The “Authoring on the Fly” system for automated recording and replay of (tele) presentations 8 (2000)Google Scholar
  5. [5]
    Grünewald, F., Meinel, C.: Implementing a Culture of Participation as Means for Collaboration in Tele-Teaching Using the Example of Cooperative Video Annotation. In: DeLFI 2012 - Die 10. e-Learning Fachtagung Informatik. Gesellschaft für Informatik, Hagen (2012)Google Scholar
  6. [6]
    Hofmann, C., Hollender, N., Fellner, D.W.: Workflow-Based Architecture for Collaborative Video Annotation. In: Ozok, A.A., Zaphiris, P. (eds.) OCSC 2009. LNCS, vol. 5621, pp. 33–42. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  7. [7]
    Zupancic, B.: Vorlesungsaufzeichnungen und digitale Annotationen Einsatz und Nutzen in der Lehre, Albert-Ludwigs-Universität Freiburg, Dissertation (2006)Google Scholar
  8. [8]
    Hermann, C., Ottmann, T.: Electures-Wiki – Toward Engaging Students to Actively Work with Lecture Recordings. IEEE Transactions on Learning Technologies 4(4), 315–326 (2011)Google Scholar
  9. [9]
    Cha, M., Kwak, H., Rodriguez, P., Ahn, Y., Moon, S.: I Tube, You Tube, Everybody Tubes: Analyzing the World’s Largest User Generated Content Video System. In: Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement, IMC 2007, San Diego, California, USA, pp. 1–13 (2007)Google Scholar
  10. [10]
    Hostetter, C., Bush, M.: Measuring Up Online: The Relationship between Social Presence and Student Learning Statisfaction. Journal of Scholarship of Teaching and Learning, 6(2), 1–12 (2006)Google Scholar
  11. [11]
    Kimmerle, J., Cress, U.: Group awareness and self-presentation in computer-supported information exchange. International Journal of Computer-Supported Collaborative Learning 3(1), 85–97 (2007),, doi:10.1007/s11412–007–9027–z. ISSN 1556–1607
  12. [12]
    Fischer, G.: Understanding, Fostering, and Supporting Cultures of Participation. Interactions 80(3), 42–53 (2011), Google Scholar
  13. [13]
    Dick, H., Zietz, J.: Cultures of Participation as a Persuasive Technology. i-com (2), 9–15 (2011)Google Scholar
  14. [14]
    Hürst, W., Kreuzer, T., Wiesenhütter, M.: A qualitative study towards using large vocabulary automatic speech recognition to index recorded presentations for search and access over the web. In: Proc. of IADIS WWW / Internet (ICWI), pp. 135–143 (2002)Google Scholar
  15. [15]
    Repp, S., Waitelonis, J., Sack, H., Meinel, C.: Segmentation and annotation of audiovisual recordings based on automated speech recognition. In: Yin, H., Tino, P., Corchado, E., Byrne, W., Yao, X. (eds.) IDEAL 2007. LNCS, vol. 4881, pp. 620–629. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  16. [16]
    Adcock, J., Cooper, M., Denoue, L., Pirsiavash, H.: TalkMiner: A Lecture Webcast Search Engine. In: Proc. of the ACM International Conference on Multimedia, MM 2010, pp. 241–250. ACM, Firenze (2010)Google Scholar
  17. [17]
    Wang, F., Ngo, C.-W., Pong, T.-C.: Structuring low-quality videotaped lectures for cross-reference browsing by video text analysis. Journal of Pattern Recognition 41(10), 3257–3269 (2008)CrossRefGoogle Scholar
  18. [18]
    Hunter, J., Little, S.: Building and Indexing a Distributed Multimedia Presentation Archive Using SMIL. In: Constantopoulos, P., Sølvberg, I.T. (eds.) ECDL 2001. LNCS, vol. 2163, pp. 415–428. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  19. [19]
    Yang, H.-J., Siebert, M., Lühne, P., Sack, H., Meinel, C.H.: Lecture Video Indexing and Analysis Using Video OCR Technology. In: Proc. of the 7th International Conference on Signal Image Technology and Internet Based Systems (SITIS), pp. 54–61 (2011)Google Scholar
  20. [20]
    Epshtein, B., Ofek, E., Wexler, Y.: Detecting Text in Natural Scenes with Stroke Width Transform. In: Proc. of International Conference on Computer Vision and Pattern Recognition, pp. 2963–2970 (2010)Google Scholar
  21. [21]
    Yang, H., Quehl, B., Sack, H.: A framework for improved video text detection and recognition. Multimedia Tools and Applications 1-29 (2012), ISSN 1380–7501. – 10.1007/s11042-012-1250-6
  22. [22]
    Yang, H., Grünewald, F., Meinel, C.: Automated Extraction of Lecture Outlines From Lecture Videos - A Hybrid Solution for Lecture Video Indexing. In: 4th International Conference on Computer Supported Education, Porto, Portugal, pp. 13–22 (2012)Google Scholar
  23. [23]
    Yang, H., Oehlke, C., Meinel, C.: An Automated Analysis and Indexing Framework for Lecture Video Portal. In: Popescu, E., Li, Q., Klamma, R., Leung, H., Specht, M. (eds.) ICWL 2012. LNCS, vol. 7558, pp. 285–294. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  24. [24]
    Toutanova, K., Klein, D., Manning, C., Singer, Y.: Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network. In: Proc. of Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, HLT-NAACL 2003, pp. 252–259 (2003)Google Scholar
  25. [25]
    Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Information Processing and Management, 513–523 (1988)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Franka Grünewald
    • 1
  • Haojin Yang
    • 1
  • Elnaz Mazandarani
    • 1
  • Matthias Bauer
    • 1
  • Christoph Meinel
    • 1
  1. 1.Hasso-Plattner-InstituteUniversity of PotsdamGermany

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