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Differentiating the learning styles of college students in different disciplines in a college English blended learning setting

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  • Jie Hu
  • Yi Peng
  • Xueliang Chen
  • Hangyan Yu

Abstract

Learning styles are critical to educational psychology, especially when investigating various contextual factors that interact with individual learning styles. Drawing upon Biglan’s taxonomy of academic tribes, this study systematically analyzed the learning styles of 790 sophomores in a blended learning course with 46 specializations using a novel machine learning algorithm called the support vector machine (SVM). Moreover, an SVM-based recursive feature elimination (SVM-RFE) technique was integrated to identify the differential features among distinct disciplines. The findings of this study shed light on the optimal feature sets that collectively determined students’ discipline-specific learning styles in a college blended learning setting.

Suggested Citation

  • Jie Hu & Yi Peng & Xueliang Chen & Hangyan Yu, 2021. "Differentiating the learning styles of college students in different disciplines in a college English blended learning setting," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-26, May.
  • Handle: RePEc:plo:pone00:0251545
    DOI: 10.1371/journal.pone.0251545
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    References listed on IDEAS

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    1. Sarah Yusoff & Rohana Yusoff & Nur Hidayah Md Noh, 2017. "Blended Learning Approach for Less Proficient Students," SAGE Open, , vol. 7(3), pages 21582440177, August.
    2. Aditya Khamparia & Babita Pandey, 2018. "SVM and PCA Based Learning Feature Classification Approaches for E-Learning System," International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), IGI Global, vol. 13(2), pages 32-45, April.
    3. Qingdong Wu & Bo Yan & Chao Zhang & Lu Wang & Guobao Ning & B. Yu, 2014. "Displacement Prediction of Tunnel Surrounding Rock: A Comparison of Support Vector Machine and Artificial Neural Network," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-6, July.
    4. Charness, Gary & Gneezy, Uri, 2012. "Strong Evidence for Gender Differences in Risk Taking," Journal of Economic Behavior & Organization, Elsevier, vol. 83(1), pages 50-58.
    5. Samuel C. A. Pereira, 2021. "On the precision of information," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 30(3), pages 569-584, August.
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    Cited by:

    1. Hamisu Kasimu Meshanu & Kwaku Esia-Donkor, 2023. "Public Junior High School Pupils’ Perceptions of their Learning Style Preferences and their Relationship with Academic Achievement in Social Studies in East Mamprusi Municipality, Ghana," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 7(10), pages 1693-1706, October.
    2. Natasha Dzulkalnine & Nur Athirah Sumardi & Mohamed Nor Azhari Azman & Ihfasuziella Ibrahim & Nur Qudus & Syarifah Mastura Syed Abu Bakar, 2024. "The Preferences of Student’s Learning Method Based on Course, Gender and Age: Visual, Audio, Reading & Kinesthetic (VARK)," Information Management and Business Review, AMH International, vol. 16(2), pages 1-11.

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