Mining the Frequent Attributes Using Feature Selection Technique for Learning Disability Students

Authors

  • P. Saraswathi  Research Scholar, Bharathiar University, Coimbatore, Tamil Nadu, India
  • Dr. N. Nagadeepa  Principal, Karur Velalar College of Arts & Science for Woman, Kuppam (PO), Karur,Tamil Nadu, India

Keywords:

Learning Disability, ICT, Assistive Technology, Ranking Technique, Mining Approach

Abstract

In recent era, learning disability is the most essential problem that affects the educational background of the students. The educational community faces many challenges in addressing these desires more in learning disability student. Students with learning disability have inadequate use of new technology. To overcome these issues focussed in this paper to identify the top ranking attributes that leads learning disability. The proposed work aims to study on identifying the most frequent symptoms of learning disabilities using the ranking technique in mining approach. Based on the result, the paper concludes necessitate of ICT with assistive technology used to enhance the educational background of learning disability student for all learning disabled student community. In this paper identifies the barriers of LD student and also concludes the technology used by the students with LD contributes towards their betterment in educational achievement.

References

  1. Margaret Mary .T, Hanumanthappa. M, "Hybrid Classification approach HDLMM for Learning Disability Prediction in School going Children using Data Mining Technique", Journal of Theoretical and Applied Information Technology", Vol. 95 No. 13, July 2017.
  2. Julie M. David, Kannan Balakrishnan, "Significance of Classification techniques in prediction of learning disabilities", International Journal of Artificial Intelligence and applications, Vol. 1 No. 4, October 2010.
  3. Pooja Thakar, Anil Mehta, Manisha, "Performance Analysis and Prediction in Educational data mining: A Research Travelogue", International Journal of Computer Applications, Volume 110 No.15, January 2015.
  4. Pekka Rasanen, "Educational Neuroscience as a tool to understand learning and learning disabilities in mathematics", International Conference on Education Data Mining", 2015.
  5. K. Ambili, P. Afsar, "A framework for learning disability prediction in school children using naïve bayes - neural network fusion technique", Journal of Information, Knowledge and Research in Computer Engineering", Volume 04, Issue 01,October 2016.
  6. Sabu M.K, "Feature Selection: A Novel Approach for the prediction of learning disabilities in school aged children", CSCP, pp 127-137, 2015.
  7. Thakaa Z. Mohammad, Abeer M. Mahmoud, El-Sayed M. El-Horbart, Mohamed I. Roushdy and Abdel-Badeeh M. Salem, "An Intelligent Educational Data Mining Classification Model for Teaching English for Slow Learner Students", International Journal of Computer Science, Vol. 2 Issue. 8, August 2014.
  8. Julie M. David, Kannan Balakrishnan, "Performance Improvement of Fuzzy and Neuro Fuzzy Systems:Prediction of Learning Disabilities in School age children", I.J. Intelligent Systems and Applications, 34-52, 2013.
  9. Zachariah Kariuki Mbugua, Komen Kibet, George Mungiria Muthaa, George Reche Nknonke, "Factors contributing to students poor performance in mathematics at Kenya certificate of secondary education in Kenya: A case of Baringo County, Kenya", American International Journal of Contemporary Research, Vol 2 No 6, June 2012.
  10. Abolfazl Shahbazi, Maryam Karambeygi, "Application of data mining in rural planning", International Journal of Engineering and Innovative Technology, Vol 5 No. 1, July 2015.
  11. Brijesh Kumar Bhardwaj, Saurabh Pal, "Data Mining: A prediction for performance improvement using classification", International Journal of Computer Science and Information Security, Vol. 9 No. 4, April 2011.

Downloads

Published

2018-02-28

Issue

Section

Research Articles

How to Cite

[1]
P. Saraswathi, Dr. N. Nagadeepa, " Mining the Frequent Attributes Using Feature Selection Technique for Learning Disability Students, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 4, Issue 2, pp.579-582, January-February-2018.