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The Influence of Learning Styles on Learners in E-Learning Environments: An Empirical Study

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  • Naser-Nick Manochehr

    (Qatar University)

Abstract

The purpose of this study was to compare the effects of e-learning versus those of traditional instructor-based learning, on student learning, based on student learning styles. Another goal was to determine if e-learning is more effective for those with a particular learning style. The Kolb Learning Style Inventory (LSI) measured the learning styles of students. This post-test, intact-group design examined the dependent variable of student knowledge based on the learning style of each subject and the learning method to which each was exposed. The results revealed that for the instructor-based learning class (traditional), the learning style was irrelevant, but for the web-based learning class (e-learning), the learning style was significantly important. The results indicated that students with the Assimilator learning style (these learn best through lecture, papers and analogies) and the Converger learning style (these learn best through laboratories, field work and observations) achieved a better result with the e-learning (web-based) method.

Suggested Citation

  • Naser-Nick Manochehr, 2006. "The Influence of Learning Styles on Learners in E-Learning Environments: An Empirical Study," Computers in Higher Education Economics Review, Economics Network, University of Bristol, vol. 18(1), pages 10-14.
  • Handle: RePEc:che:chepap:v:18:y:2006:i:1:p:10-14
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    File URL: https://www.economicsnetwork.ac.uk/cheer/ch18/manochehr.pdf
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    Cited by:

    1. Iuliana DOROBAT (SCORTA) & Floarea NASTASE, 2010. "Personalized Training in Romanian SME’s ERP Implementation Projects," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 14(3), pages 116-127.
    2. Marianne Johnson & Denise Robson, 2008. "Clickers, Student Engagement and Performance in an Introductory Economics Course: a Cautionary Tale," Computers in Higher Education Economics Review, Economics Network, University of Bristol, vol. 20(1), pages 4-12.
    3. Priya Dharshini, A. & Chandrakumarmangalam, S. & Arthi, G., 2015. "Ant colony optimization for competency based learning objects sequencing in e-learning," Applied Mathematics and Computation, Elsevier, vol. 263(C), pages 332-341.

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