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A Learning Style Identification Approach in Adaptive E-Learning System

  • Hanaa El Fazazi
  • Abderrazzak Samadi
  • Mohamed Qbadou
  • Khalifa Mansouri
  • Mouhcine Elgarej
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 111)

Abstract

Adaptive e-learning systems are considered one of the interesting research areas in technology-based learning strategies. The main goal of these systems is to offer learns a personal and a unique learning experience based on their preferences, needs, educational background, learning style, etc. The objective of this research is to identify the learning style of the learner. The identification is based on using web Log Mining data which contain learning behavior of the learner, and then the learning styles are mapped to Felder-Silverman Learning Style Model categories using Fuzzy C means Algorithm. The learning style can be changed over a period of time therefore the system has to adapt to the changes. For this, an Artificial Neural Network Algorithm is used to predict the learning style of a learner.

Keywords

Adaptive e-learning system Learning style Felder-Silverman Learning Style Model Fuzzy C means Algorithm Artificial Neural Network Algorithm 

References

  1. 1.
    Truong, H.M.: Integrating learning styles and adaptive e-learning system: current developments, problems and opportunities. Comput. Hum. Behav. 55, 1193 (2016)CrossRefGoogle Scholar
  2. 2.
    Alshammari, M.T.: Adaptation Based on Learning Style and Knowledge Level in E-learning Systems (2016)Google Scholar
  3. 3.
    Binh, H.T., Duy, B.T.: Predicting students’ performance based on learning style by using artificial neural networks. In: IEEE International Conference on Knowledge and Systems Engineering (KSE) (2017)Google Scholar
  4. 4.
    Felder, R.M., Silverman, L.K.: Learning and teaching styles in engineering education. Eng. Educ. 78, 674–681 (1988)Google Scholar
  5. 5.
    Abdullah, M.A.: Learning style classification based on student’s behavior in moodle learning management system. Trans. Mach. Learn. Artif. Intell. 3(1), 28 (2015)Google Scholar
  6. 6.
    Graf, S., Kinshuk, Liu, T.-C.: Identifying learning styles in learning management systems by using indications from students’ behaviour. In: IEEE International Conference on Advanced Learning Technologies (ICALT 2008), pp. 482–486 (2008)Google Scholar
  7. 7.
    Feldman, J., Monteserin, A., Amandi, A.: Automatic detection of learning styles: state of the art. Artif. Intell. Rev. 44(2), 157–186 (2015)CrossRefGoogle Scholar
  8. 8.
    Kolekar, S.V., Sanjeevi, S.G., Bormane, D.S.: Learning style recognition using artificial neural network for adaptive user interface in e-learning. In: IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pp 1–5 (2010)Google Scholar
  9. 9.
    Radwan, N.: An adaptive learning management system based on learner’s learning style. Int. Arab J. E-Technol. 3, 7 (2014)Google Scholar
  10. 10.
    Agbonifo, O.C.: Fuzzy c-means clustering model for identification of students’ learning preferences in online environment. Int. J. Comput. Appl. Inform. Technol. 4(1), 15–21 (2013)Google Scholar
  11. 11.
    Hogo, M.A.: Evaluation of e-learners behaviour using different fuzzy clustering models: a comparative study. Int. J. Comput. Sci. E-educ. 7(2), 131–140 (2010)Google Scholar
  12. 12.
    Kolekar, S.V., Sanjeevi, S.G., Bormane, D.S.: Learning style recognition using artificial neural network for adaptive user interface in E-learning. In: IEEE International Conference on Computational Intelligence and Computing Research (2010)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Hanaa El Fazazi
    • 1
  • Abderrazzak Samadi
    • 1
  • Mohamed Qbadou
    • 1
  • Khalifa Mansouri
    • 1
  • Mouhcine Elgarej
    • 1
  1. 1.Laboratory Signals, Distributed Systems and Artificial IntelligenceENSET, University Hassan IIMohammediaMorocco

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