Skip to main content

A Hybrid System: Neural Network with Data Mining in an e-Learning Environment

  • Conference paper
Knowledge-Based Intelligent Information and Engineering Systems (KES 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4693))

Abstract

This paper proposed a hybrid system combining the self-organizing map (SOM) of a neural network with the data mining (DM) method, for course recommendations in the e-learning system. SOM systems have been successfully used in several domains of artificial intelligence. Although many researches focused on e-learning system implementation and personal curriculum design, they do not give e-learners useful suggestions for selecting potential courses according to their interests or background. In order to enhance the efficiency and capability of e-learning systems, we combined the SOM method to deal with the cluster problems of the DM systems, SOM/DM for short. The experiment was carried out in a business college of a university in Taiwan, by applying the SOM/DM method to recommend courses to e-learners. The results indicated that the SOM/DM method has excellent performance.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Arasu, A., Cho, J., Garcia-Molina, H., Paepcke, A., Raghavan, S.: Searching the Web. ACM Transactions on Internet Technology 1(1), 97–101 (2001)

    Article  Google Scholar 

  2. Aydin, C.H., Tasci, D.: Measuring Readiness for e-Learning: Reflections from an Emerging Country. Educational Technology & Society 8(4), 244–257 (2005)

    Google Scholar 

  3. Berghel, H.: Cyberspace 2000: Dealing with information overload. Communications of the ACM 40(2), 19–24 (1997)

    Article  Google Scholar 

  4. Borchers, A., Herlocker, J., Konstanand, J., Riedl, J.: Ganging up on information overload. Computer 31(4), 106–108 (1998)

    Article  Google Scholar 

  5. Bose, I., Mahapatra, R.: Business data mining - a machine learning perspective. Information & Management 39, 211–225 (2001)

    Article  Google Scholar 

  6. Chen, C.M., Lee, H.M., Chen, Y.H.: Personalized e-learning system using Item Response Theory. Computers and Education 44, 237–255 (2005)

    Article  Google Scholar 

  7. Herlocker, J., Konstan, J.: Content-independent, task-focused recommendations. IEEE Internet Computing 5, 40–47 (2001)

    Article  Google Scholar 

  8. Huang, M.J., Chen, M.Y., Lee, S.C.: Integrating Data Mining with Case-based Reasoning for Chronic Diseases Prognosis and Diagnosis. accepted to appear in Expert Systems with Applications 32(3), 856–867 (2007)

    Google Scholar 

  9. International Data Corporation Website, http://www.idc.com/

  10. Kirkos, E., Spathis, C., Manolopoulos, Y.: Data Mining techniques for the detection of fraudulent financial statements. Expert Systems with Applications 23(4), 995–1003 (2007)

    Article  Google Scholar 

  11. Kohonen, T.: Self-organized formation of topologically correct feature maps. Biological Cybernetics 43(1), 59–69 (1982)

    Article  MathSciNet  MATH  Google Scholar 

  12. Ma, Z.: Web-Based Intelligent e-Learning Systems: Technologies and Applications, Information Science Publishing, pages 388 (2006)

    Google Scholar 

  13. Rashid, A.M., Albert, I., Cosley, D., Lam, S.K., McNee, S., Konstan, J.A.: Getting to know you: Learning new user preferences in recommender systems. In: Proceedings of the 2002 international conference on intelligent user interfaces, pp. 127–134 (2002)

    Google Scholar 

  14. Tseng, V.S., Lin, K.W.: Efficient mining and prediction of user behavior patterns in mobile web systems. Information and Software Technology 48(6), 357–369 (2006)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Tai, D.WS., Wu, HJ., Li, PH. (2007). A Hybrid System: Neural Network with Data Mining in an e-Learning Environment. In: Apolloni, B., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2007. Lecture Notes in Computer Science(), vol 4693. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74827-4_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-74827-4_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-74826-7

  • Online ISBN: 978-3-540-74827-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics