Using Machine Learning to Help Students with Learning Disabilities Learn

  • Francis DcruzEmail author
  • Vijitashw Tiwari
  • Mayur Soni
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 39)


The concept behind this learning modal is to connect education with technology to meet the different needs of each student. The main aim of personalized learning is to help students with disabilities.

Students with a disability often need subject matter presented through different methods, therefore it is imperative that these technological advances benefit all students with different learning styles. Machine Learning opens up new ways to help students with disabilities. Children with autism which is a neurological disorder need a personalized development system for their daily activities. Technology can play a substantial part.

The system includes 4 parts: (i) To predict the learning level of the user. (ii) Generating multimodal learning materials using web mining. (iii) User preferences are associated with the result. (iv) Personalized contents for users delineated with an intelligent interface.


Multimodal learning material Special Needs Children Web mining Machine learning 


  1. 1.
    Wagley, A., Akhter, P., Bhuiyan, M., Dahal, K., Hossain, A.: Web mining to generate multimodal learning materials for children with special needs. In: The 8th International Conference on Software, Knowledge, Intelligent Management and Applications, SKIMA 20I4, Dhaka (2014)Google Scholar
  2. 2.
    Pretschner, A., Gauch, S.: Ontology based personalized search. In: 1999 Proceedings of the 11th IEEE International Conference at Tools with Artificial Intelligence, Chicago, IL (1999)Google Scholar
  3. 3.
    Pretschner, A., Gauch, S.: Personalized search based on user search histories. In: Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence (2005)Google Scholar
  4. 4.
    Bhuiyan, M., Miraz, M.H., Banik, L.: Automated generation of learning materials for children with special needs in converged platforms using android. In: The 2nd International Symposium on Advanced and Applied Convergence (ISAAC 2014) (2014)Google Scholar
  5. 5.
    Shah, M., Shah, M., Shirke, A., Deulkar, K.: Providing personalized study material for learning disability using machine learning. Int. J. Res. Sci. Eng. (2017). e-ISSN: 2394–8299 Special Issue 7-ICEMTE March 2017Google Scholar
  6. 6.
    Mythili, M.S., Shanavas, A.R.M.: A novel approach to predict the learning skills of autistic children using SVM and decision tree. (UCSIT) Int. J. Comput. Sci. Inf. Technol. (2014)Google Scholar
  7. 7.
    Hutchinson, H., Bederson, B.: Interface design for children’s searching and browsing. U. of MDHCIL Technical report, HCIL-2005–25 (2005)Google Scholar
  8. 8.
    Hutchinson, H.: Children’s interface design for hierarchical search and browse. ACM SIGCAPH Comput. Phys. Handicap. 75, 11–12 (2003)CrossRefGoogle Scholar
  9. 9.
    Marsh, J.: Young children’s play in online virtual worlds. J. Early Child. Res. 7(3), 1–17 (2010)Google Scholar
  10. 10.
    Marsh, J.: Young children’s literacy practices in a virtual world: establishing an online interaction order. Read. Res. Q. 46(2), 101–118 (2011)CrossRefGoogle Scholar
  11. 11.
  12. 12.
    Marsh, J.: The techno-litaracy practices of young children. J. Early Child. Res. 2(1), 52–66 (2004)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Standen, P.J., Brown, D.J., Cromby, J.J.: The effective use of virtual environments in the education and rehabilitation of students with intellectual disabilities. British Journal of Educational Technology 32(3), 289–299 (2001)CrossRefGoogle Scholar
  14. 14.
    Attardi, G., Gulli, A., Sebastiani, F.: Automatic Web page categorization by link and context analysis. In: Proceedings of THAI, vol. 99, no. 99, pp. 105–119 (1999)Google Scholar
  15. 15.
    Kim, Y., Nam, T.: An efficient text filter for adult web documents. In: The 8th International Conference on Advanced Communication Technology. ICACT 2006, vol. 1, pp. 3-pp. IEEE, February 2006Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer EngineeringSt. Francis Institute of TechnologyMumbaiIndia

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