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A Comparative Study of Learning Styles and Motivational Factors in Traditional and Online Sections of a Business Course

Author

Listed:
  • Sinjini Mitra

    (Department of Information Systems and Decision Sciences, California State University, Fullerton, California 92831;)

  • Gerard Beenen

    (Department of Management, California State University, Fullerton, California 92831)

Abstract

Online learning has grown tremendously over the past decade. As the trend continues, it is important to understand which students are likely to choose to enroll in online courses and which factors affect their performance. This study explored the differences in motivational factors among students enrolled in online and traditional face-to-face sections of a bottleneck business course, with a focus on how these factors differentially affect performance in the two formats. Furthermore, it provided useful insights into students’ decisions to select a particular course format. Results based on a sample of 146 students at a large public university showed that autonomous motivation and intrinsic value predicted students’ enrollment in online courses and performance in those courses. On the other hand, performance and learning approach goal orientations predicted students’ enrollment in traditional face-to-face courses and their performance in those courses. Learning styles such as reflective observation and abstract experience predicted performance, yet had no impact on course format selection. We discuss implications of these findings for student advising, course design and delivery, and broader impacts on student retention and on-time graduation.

Suggested Citation

  • Sinjini Mitra & Gerard Beenen, 2019. "A Comparative Study of Learning Styles and Motivational Factors in Traditional and Online Sections of a Business Course," INFORMS Transactions on Education, INFORMS, vol. 20(1), pages 1-15, September.
    • Handle: RePEc:inm:orited:v:20:y:2019:i:1:p:1-15
    DOI: 10.1287/ited.2019.0211
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    References listed on IDEAS

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    1. Gloria Crisp & Erin Doran & Nicole A. Salis Reyes, 2018. "Predicting Graduation Rates at 4-year Broad Access Institutions Using a Bayesian Modeling Approach," Research in Higher Education, Springer;Association for Institutional Research, vol. 59(2), pages 133-155, March.
    2. Paula Carroll & Arthur White, 2017. "Identifying Patterns of Learner Behaviour: What Business Statistics Students Do with Learning Resources," INFORMS Transactions on Education, INFORMS, vol. 18(1), pages 1-13, September.
    3. Fox, Jonathan & Bartholomae, Suzanne, 1999. "Student learning style and educational outcomes: evidence from a family financial management course," Financial Services Review, Elsevier, vol. 8(4), pages 235-251.
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    Cited by:

    1. Catherine Cleophas & Christoph Hönnige & Frank Meisel & Philipp Meyer, 2023. "Who’s Cheating? Mining Patterns of Collusion from Text and Events in Online Exams," INFORMS Transactions on Education, INFORMS, vol. 23(2), pages 84-94, January.
    2. Stacey Vaziri & Baback Vaziri & Luis J. Novoa & Elham Torabi, 2022. "Academic Motivation in Introductory Business Analytics Courses: A Bayesian Approach," INFORMS Transactions on Education, INFORMS, vol. 22(2), pages 121-129, January.
    3. Sinjini Mitra, 2023. "How Are Students Learning in a Business Statistics Course? Evidence from Both Direct and Indirect Assessment," INFORMS Transactions on Education, INFORMS, vol. 23(2), pages 95-103, January.

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