Learning style recognition using Artificial Neural Network for adaptive user interface in e-learning

Sucheta V. Kolekar, S. G. Sanjeevi, D. S. Bormane

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Citations (Scopus)

Abstract

Traditionally e-learning systems are emphasized on the online content generation and most of them fail in considering the requirements and learning styles of end user, while representing it. Therefore, appears the need for adaptation to the user's learning behavior. Adaptive e-learning refers to an educational system that understands the learning content and the user interface according to pedagogical aspects. End users have unique ways of learning which may directly and indirectly affect on the learning process and its outcome. In order to implement effective and efficient e-learning, the system should be capable not only in adapting the content of course to the individual characteristics of students but also concentrate on the adaptive user interface according to students' requirements. In this paper, at initial stage we are presenting an approach to recognize the learning styles of individual student according to the actions or navigations that he or she has performed on an e-learning application. This recognition technique is based on Machine Learning algorithm called Artificial Neural Networks and Web Usage Mining.

Original languageEnglish
Title of host publication2010 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2010
Pages245-249
Number of pages5
DOIs
Publication statusPublished - 01-12-2010
Event2010 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2010 - Coimbatore, India
Duration: 28-12-201029-12-2010

Conference

Conference2010 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2010
CountryIndia
CityCoimbatore
Period28-12-1029-12-10

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User interfaces
Students
Neural networks
Learning systems
Learning algorithms
Navigation

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Information Systems
  • Signal Processing
  • Control and Systems Engineering

Cite this

Kolekar, S. V., Sanjeevi, S. G., & Bormane, D. S. (2010). Learning style recognition using Artificial Neural Network for adaptive user interface in e-learning. In 2010 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2010 (pp. 245-249). [5705768] https://doi.org/10.1109/ICCIC.2010.5705768
Kolekar, Sucheta V. ; Sanjeevi, S. G. ; Bormane, D. S. / Learning style recognition using Artificial Neural Network for adaptive user interface in e-learning. 2010 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2010. 2010. pp. 245-249
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Kolekar, SV, Sanjeevi, SG & Bormane, DS 2010, Learning style recognition using Artificial Neural Network for adaptive user interface in e-learning. in 2010 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2010., 5705768, pp. 245-249, 2010 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2010, Coimbatore, India, 28-12-10. https://doi.org/10.1109/ICCIC.2010.5705768

Learning style recognition using Artificial Neural Network for adaptive user interface in e-learning. / Kolekar, Sucheta V.; Sanjeevi, S. G.; Bormane, D. S.

2010 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2010. 2010. p. 245-249 5705768.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Kolekar SV, Sanjeevi SG, Bormane DS. Learning style recognition using Artificial Neural Network for adaptive user interface in e-learning. In 2010 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2010. 2010. p. 245-249. 5705768 https://doi.org/10.1109/ICCIC.2010.5705768