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On the Automatic Construction of Knowledge-Map from Handouts for MOOC Courses

  • Nen-Fu HuangEmail author
  • Chia-An Lee
  • Yi-Wei Huang
  • Po-Wen Ou
  • How-Hsuan Hsu
  • So-Chen Chen
  • Jian-Wei Tzengßer
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 81)

Abstract

Massive open online courses (MOOCs) offer valuable opportunities for freedom in learning; however, many learners face cognitive overload and conceptual and navigational disorientation. In this study, we used handouts to automatically build domain-specific knowledge maps for MOOCs. We considered handouts as conceptual models created by teachers, and we performed text mining to extract keywords from MOOC handouts. Each knowlege map is based on the structure of the handouts, each consisting of an outline, title, and content. The findings suggest that using handouts to build knowledge maps is feasible.

Keywords

Knowledge maps Learning styles Massive open online courses Open learning 

Notes

Acknowledgement

This study is supported by the Ministry of Science and Technology (MOST) of Taiwan under grant numbers MOST-105-2511-S-007-002-MY3 and MOST-105-2634-F-007-001.

References

  1. 1.
  2. 2.
  3. 3.
  4. 4.
  5. 5.
    Wang, M., Peng, J., Cheng, B., Zhou, H., Liu, J.: Knowledge visualization for self-regulated learning. Educ. Technol. Soc. 14, 28–42 (2011)Google Scholar
  6. 6.
    Holley, C.D., Dansereau, D.F.: Spatial Learning Strategies: Techniques, Applications, and Related Issues. Academic Press, New York (2014)Google Scholar
  7. 7.
    Foos, P.W.: The effect of variations in text summarization opportunities on test performance. J. Exper. Educ. 63, 89–95 (1995)CrossRefGoogle Scholar
  8. 8.
    Nesbit, J.C., Adesope, O.O.: Learning with concept and knowledge maps: a meta-analysis. Rev. Educ. Res. 76, 413–448 (2006)CrossRefGoogle Scholar
  9. 9.
    Fasihuddin, H.A., Skinner, G.D., Athauda, R.I.: Boosting the opportunities of open learning (MOOCs) through learning theories. GSTF J. Comput. (JoC) 3, 112 (2013)CrossRefGoogle Scholar
  10. 10.
    Fasihuddin, H., Skinner, G., Athauda, R.: Knowledge maps in open learning environments: an evaluation from learners perspectives. J. Inf. Technol. Appl. Educ. 4, 18–29 (2015)Google Scholar
  11. 11.
    YongYue, C., HuoSong, X.: Research on the auto-construction methods of concept map. In: International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2009, pp. 75–77. IEEE (2009)Google Scholar
  12. 12.
    Lee, J.H., Segev, A.: Knowledge maps for e-learning. Comput. Educ. 59, 353–364 (2012)CrossRefGoogle Scholar
  13. 13.
    Greca, I.M., Moreira, M.A.: Mental models, conceptual models, and modelling. Int. J. Sci. Educ. 22, 1–11 (2000)CrossRefGoogle Scholar
  14. 14.
    Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Inf. Process. Manage. 24, 513–523 (1988)CrossRefGoogle Scholar
  15. 15.
    Leskovec, J., Rajaraman, A., Ullman, J.D.: Mining of Massive Datasets. Cambridge University Press, Cambridge (2014)CrossRefGoogle Scholar
  16. 16.
    Flood, B.J.: Historical note: the start of a stop list at Biological Abstracts. J. Assoc. Inf. Sci. Technol. 50, 1066 (1999)Google Scholar
  17. 17.
    Luhn, H.P.: A statistical approach to mechanized encoding and searching of literary information. IBM J. Res. Dev. 1, 309–317 (1957)MathSciNetCrossRefGoogle Scholar
  18. 18.
  19. 19.
    The “Investment” course on ShareCourse. http://www.sharecourse.net/sharecourse/course/view/courseInfo/987

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Nen-Fu Huang
    • 1
    Email author
  • Chia-An Lee
    • 1
  • Yi-Wei Huang
    • 1
  • Po-Wen Ou
    • 1
  • How-Hsuan Hsu
    • 1
  • So-Chen Chen
    • 1
  • Jian-Wei Tzengßer
    • 1
  1. 1.Department of Computer ScienceNational Tsing Hua UniversityHsinchuTaiwan

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