Impact of Major Switching on Academic Performance: A Structural Equation Modeling Approach in Higher Education

Major switching is a common occurrence in higher education institutions worldwide, with over one-third of students changing their academic focus at least once during their undergraduate studies. However, there is limited understanding of the extent to which switching majors influences academic performance and how this process unfolds within a learning-situated context. In this study, a theoretical framework comprising four factors of the new learning context encountered by students on switching majors—curriculum design, teaching pattern, learning initiative, and peer effect—was constructed. The study objective was to examine the interplay between these four factors in order to identify the conditions under which switching majors could be either beneficial or unfavorable for students. A questionnaire survey was conducted at a Chinese university and valid responses were obtained from 224 students who had switched majors. Confirmatory factor analysis validated the four-factor theoretical model, while structural equation modeling revealed that major switching improves students’ academic ranking when teaching pattern and curriculum design of the new major are more appealing and well-structured. In contrast, increased learning initiative and improved peer effect do not contribute positively to academic performance. These findings provide valuable insights for undergraduates in making informed decisions about major switching and offer guidance for university administrators to refine policies and procedures related to the major-switching process.

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Publication Details

Journal
SAGE Open
Published
2025-01-01
DOI
https://doi.org/10.1177/21582440251362579
Primary Topic
Higher Education Research Studies
Type
article
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article

Impact of Major Switching on Academic Performance: A Structural Equation Modeling Approach in Higher Education

Won Shic Hong, Rongxin Zhang, Siyu Sun
SAGE Open
Higher Education Research Studies
article

Impact of Major Switching on Academic Performance: A Structural Equation Modeling Approach in Higher Education

Won Shic Hong, Rongxin Zhang, Siyu Sun
article en

Abstract

Major switching is a common occurrence in higher education institutions worldwide, with over one-third of students changing their academic focus at least once during their undergraduate studies. However, there is limited understanding of the extent to which switching majors influences academic performance and how this process unfolds within a learning-situated context. In this study, a theoretical framework comprising four factors of the new learning context encountered by students on switching majors—curriculum design, teaching pattern, learning initiative, and peer effect—was constructed. The study objective was to examine the interplay between these four factors in order to identify the conditions under which switching majors could be either beneficial or unfavorable for students. A questionnaire survey was conducted at a Chinese university and valid responses were obtained from 224 students who had switched majors. Confirmatory factor analysis validated the four-factor theoretical model, while structural equation modeling revealed that major switching improves students’ academic ranking when teaching pattern and curriculum design of the new major are more appealing and well-structured. In contrast, increased learning initiative and improved peer effect do not contribute positively to academic performance. These findings provide valuable insights for undergraduates in making informed decisions about major switching and offer guidance for university administrators to refine policies and procedures related to the major-switching process.

SAGE OpenVol. 15(3)
Zhongnan University of Economics and Law (CN), Anhui Normal University (CN), Renmin University of China (CN)
Openalex Percentile: Top 74%
Higher Education Research Studies
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Impact of Major Switching on Academic Performance: A Structural Equation Modeling Approach in Higher Education — Won Shic Hong, Rongxin Zhang, et al. · SAGE Open (2025) | TGRS Research Map | TGRS