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Automatic Detection of Students Learning Style in Learning Management System

  • T. SheebaEmail author
  • Reshmy Krishnan
Conference paper
Part of the Advances in Science, Technology & Innovation book series (ASTI)

Abstract

Learning style is one of the major factors of student performance in any learning environment. Determining the learning style of students enhances the performance of learning process. This paper proposes an approach to classify students learning style automatically based on their learning behavior. One of the best widely used classifier algorithm is decision tree which is proposed in this paper. The main concern in decision tree classifier is the construction of significant rules which are required for accurately identifying learning styles. Lack of significant rules would result in misclassification of learning style. Hence, the main focus of this paper is to construct most significant rules which would strengthen the existing decision tree classifier to precisely and accurately detect the learning style of students. The student behavior is obtained from the web log files and then mapped with three learning dimensions of standard Felder Silverman learning style model. Subsequently, by employing significant rules in decision tree classifier, the student behavior has been automatically classified with high accuracy. This approach was experimented on 100 students for the online course created in Moodle Learning Management System. The evaluation result is obtained using inference engine with forward reasoning searches of the rules until the correct learning style is determined. The result is then analyzed with a confusion matrix of actual class and predicted class which shows that processing dimension shows variance whereas perception and input dimension were detected correctly with an average accuracy of 87%.

Keywords

Learning management system Weblog Learning styles Felder-Silverman learning style model Decision tree classifier 

References

  1. 1.
    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
  2. 2.
    Ahmad, N., Tasir, Z., Kasim, J., Sahat, H.: Automatic detection of learning styles in learning management systems by using literature based method. In: 13th International Educational Technology Conference, Vol. 103, pp. 181–189, Procedia, Elsevier (2013)Google Scholar
  3. 3.
    Garcı´a, P., Amandi, A., Schiaffino, S., Campo, M.: Evaluating Bayesian networks precision for detecting students learning styles. Comput. Educ. 49(3), 794–808 (2007)CrossRefGoogle Scholar
  4. 4.
    Abdullah, M.A.: Learning style classification based on student’s behavior in Moodle learning management system. TAMLAI Trans. Mach. Learn. Artif. Intell. 3(1) (2015)Google Scholar
  5. 5.
    Yannibelli, V., Godoy, D., Amandi, A.: A genetic algorithm approach to recognize students’ learning styles. Interact. Learn. Environ. 14(1), 55–78 (2006)CrossRefGoogle Scholar
  6. 6.
    Chang, Y.C., Kao, W.-Y., Chu, C.-P., Chiu, C.H.: A learning style classification mechanism for e-learning. Comput Educ 53(2), 273–285 (2009)CrossRefGoogle Scholar
  7. 7.
    Kolekar, S.V., Sanjeevi, S.G., Bormane, D.S.: Learning style recognition using artificial neural network for adaptive user interface in e-learning. In: Computational Intelligence and Computing Research, 2010 IEEE International Conference, pp. 1–5, IEEE (2011)Google Scholar
  8. 8.
    Al-Azawei, A., Badii, A.: State of the art of learning styles-based adaptive educational hypermedia systems (LS-BAEHSS). Int. J. Comput. Sci. Inf. Technol. (IJCSIT) 6(3), 1–19 (2014)Google Scholar
  9. 9.
    Villaverde, J.E., Godoy, D., Amandi, A.: Learning styles’ recognition in e-learning environments with feed-forward neural networks. J. Comput. Assist. Learn. 22(3), 197–206 (2006)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringKarpagam UniversityCoimbatore, TamilNaduIndia
  2. 2.Department of ComputingMuscat CollegeRuwiSultanate of Oman

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