Skip to main content

An Efficient Educational Data Mining Approach to Support E-learning

  • Conference paper
  • First Online:
Information Systems Design and Intelligent Applications

Abstract

Currently, data mining technique has become popular in online learning environment for learning as this technique works on huge amount of dataset. There are several e-learning systems which are based on the classroom, providing the natural interface of human-computer and the communication among to multi-modality, and the computing technology of integrated pervasive in the classroom. The pervasive computing for the development of some of the requirements is being raised for system openness, scalability and extensibility. Everyone could access the materials of learning from anywhere with the help of internet. Increased access of users also leads to security requirements and higher efficiency in data retrieval. To address these concerns, we propose an educational data mining approach and OTP system for improving the efficiency of data retrieval and security. This system provides more accuracy in data mining and secure data transmission.

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

Access this chapter

Institutional subscriptions

References

  1. Yue Suo, Naoki Miyata, Hiroki Morikawa, Toru Ishida, Yuanchun Shi.: Open Smart Classroom: Extensible and Scalable Learning System in Smart Space Using Web Service Technology. IEEE Transactions on Knowledge and Data Engineering 21(6), 814–828 (2008).

    Google Scholar 

  2. Akram, A., Samir, A. El-Seoud.: Web Services Based Authentication System for E-Learning. IJCIS 5(2), 74–78 (2007).

    Google Scholar 

  3. Myungjin, L., Wooju, K., June, S.H., Sangun P.: Semantic Association-Based Search And Visualization Method On The Semantic Web Portal. International Journal of Computer Networks & Communications 2(1), 140–152 (2010).

    Google Scholar 

  4. Khalil El-Khatib, Larry Korba, Yuefei Xu, George Yee.: Privacy and Security in E-Learning. International Journal of Distance Education Technologies 1(4), 321–334 (2003).

    Google Scholar 

  5. Shaik, G.S., Rajendra. C.: An Integration of clustering and of Adaptive E-Learning Database to Support The Analysis of Learning Processes. International Journal of Advanced Engineering and Global Technology, 1(01), 11–17 (2013).

    Google Scholar 

  6. Zhang Weiyan, Zhi-Jie Wu, Xia Tao. Web Services messages in Communication Research. Computer Engineering and Design, 26(10), 2621–2623 (2005).

    Google Scholar 

  7. Berners-Lee, T., Hendler, J. and Lassila, O.: The Semantic Web. Scientific American. pp. 29–37 (2001).

    Google Scholar 

  8. Dieter, F., Wahlster, W., Henry, L.,: Spinning the Semantic Web: Bringing the World Wide Web to Its Full Potential, MIT Press, Cambridge, (2002).

    Google Scholar 

  9. T.-D. Wu, Y.-Y. Yeh, and Y.-M. Chou.: Video Learning Object Extraction and Standardized Metadata. In: Proc. Int’l Conf. Computer Science and Software Engineering. Vol. 6, pp. 332–335 (2008).

    Google Scholar 

  10. Hausenblas, M., Karnstedt, M.: Understanding Linked Open Data as a Web-Scale Database”, In: Proceedings of the Second Int’l Conf. Advances in Databases Knowledge and Data Applications, Menuires, pp. 56–61 (2010).

    Google Scholar 

  11. Ling Guo, Xin Xiang and YuanChun Shi.: Use Web Usage Mining to assist Online E-Learning Assessment. In: Proceedings of the IEEE International Conference on Advanced Learning Technologies, Finland, (2004).

    Google Scholar 

  12. Mitali, Vijay, K., Arvind, S.: Survey on Various Cryptography Techniques, International Journal of Emerging Trends & Technology in Computer Science 2(4), 307–312 (2014).

    Google Scholar 

  13. Vaibhav, S., Vinay, S.: Vector Space Model: An Information Retrieval System. International Journal of Advanced Engineering Research and Studies. 4(2), 141–143 (2015).

    Google Scholar 

  14. Bousaaid, M., Ayaou, T., Estraillier, P.: System Interactive Cyber Presence for E- Learning to Break Down Learner Isolation. International Journal of Computer Applications 3(16), pp. 35–40 (2015).

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Padmaja Appalla .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer India

About this paper

Cite this paper

Appalla, P., Kuthadi, V.M., Marwala, T. (2016). An Efficient Educational Data Mining Approach to Support E-learning. In: Satapathy, S.C., Mandal, J.K., Udgata, S.K., Bhateja, V. (eds) Information Systems Design and Intelligent Applications. Advances in Intelligent Systems and Computing, vol 434. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2752-6_6

Download citation

  • DOI: https://doi.org/10.1007/978-81-322-2752-6_6

  • Published:

  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2750-2

  • Online ISBN: 978-81-322-2752-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics