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An Automatic and Innovative Approach for Converting Pedagogical Text Documents to Visual Learning Object

  • Ali Shariq Imran
  • Atif Mansoor
  • ABM Tariqul Islam
Part of the Communications in Computer and Information Science book series (CCIS, volume 435)

Abstract

In this paper, we present a novel idea of converting pedagogical text documents to visual learning objects by automatically extracting nouns and semantic keywords from the text documents, and representing these keywords as a word cloud. A word cloud contains words that are weighted based on frequency, time, appearance, etc., depending on the concept they are used for. Each word in the word cloud would correspond to a visual representation of that word. A visual representation may contain drawings, figures, images, etc. that explains the given concept. The extracted keywords are used to query the Internet to find the corresponding visual representation of a given word. The idea is to bring text documents to life by creating a visual representation of the important concepts from the text documents. This paper is a work in progress.

Keywords

pedagogic text documents visual learning object word cloud 

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References

  1. 1.
    Burmark, L.: Visual Literacy: Learn to See, See to Learn. Association for Supervision and Curriculum Development, p. 115 (2002) ISBN 0871206404, ISSN 978087120640Google Scholar
  2. 2.
    David, H.J., Carr, C., Yueh, H.-P.: Computers as Mindtools for Engaging Learners in Critical Thinking. TechTrends 43(2), 24–32 (1998)CrossRefGoogle Scholar
  3. 3.
    Hyerle, D.: Visual Tools for Transforming Information into Knowledge, 2nd edn., Corwin (2009)Google Scholar
  4. 4.
    Manning, C.D., Raghavan, P., Schutze, H.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008)CrossRefzbMATHGoogle Scholar
  5. 5.
    Sharma, D.: Stemming Algorithms: A Comparative Study and Their Analysis. International Journal of Applied Information Systems 4(3), 7–12 (2012)CrossRefGoogle Scholar
  6. 6.
    Qiu, Y., Guan, G., Zhiyong, W., Feng, D.A.: Improving News Video Annotation with Semantic Context. In: Proceedings of the 2010 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2010, pp. 214–219. IEEE Computer Society, Washington, DC (2010)Google Scholar
  7. 7.
    Li, H., Tian, Y., Ye, B., Cai, Q.: Comparison of Current Semantic Similarity Methods in WordNet. In: International Conference on Computer Application and System Modeling (ICCASM), October 22-24, vol. 4, pp. V4-408–V4-411 (2010)Google Scholar
  8. 8.
    Agirre, E., Edmonds, P.: Word Sense Disambiguation: Algorithms and Applications. Text, Speech and Language Technology, vol. 33, XXII (2006) ISBN 978-1-4020-4809-8Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Ali Shariq Imran
    • 1
  • Atif Mansoor
    • 2
  • ABM Tariqul Islam
    • 3
  1. 1.Faculty of Comp. Science and Media TechnologyGjøvik University CollegeNorway
  2. 2.Institute of Avionics and AeronauticsAir UniversityIslamabadPakistan
  3. 3.Visual Computing Lab, Dept. of Comp. ScienceUniversity of RostockGermany

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