Blended Teaching Innovation and Social Anxiety: A Case Study at Xinyang Agriculture and Forestry University’s School of Foreign Languages

Xiaoyu Wang, Yushuang Liu and Jin Ye ()
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Xiaoyu Wang: Xinyang Agriculture and Forestry University
Yushuang Liu: Tianjing University of Technology
Jin Ye: Xinyang Agriculture and Forestry University

Journal of the Knowledge Economy, 2024, vol. 15, issue 3, No 130, 13574-13600

Abstract: Abstract This research delves into the multifaceted relationship between blended teaching, students’ social anxiety, and the crucial role played by teacher support. The study is based on a meticulous questionnaire survey conducted across two classes within the School of Foreign Languages at Xinyang Agriculture and Forestry University, providing invaluable insights into the impact of blended teaching on students’ psychological well-being. The findings reveal compelling disparities in students’ personality traits within the experimental class following the implementation of blended teaching reforms. While the “music group” dimension showed no significant differences, students in the experimental class exhibited a notable increase in “daring,” suggesting a shift in their comfort levels during classroom activities. Moreover, there was a discernible trend towards introversion and a significant upswing in anxiety levels among these students post-reform. These shifts in personality traits are closely linked to the demands placed on students in the blended teaching environment. Blended teaching necessitates a higher degree of autonomous learning, with online adaptation and passive learning emerging as key factors influencing students’ social anxiety. Importantly, teacher support emerges as a critical moderating factor in the impact of blended teaching on students’ psychological well-being. It plays a pivotal role in alleviating students’ social anxiety, underscoring the importance of educators’ involvement and guidance in this dynamic learning landscape.

Keywords: Blended teaching; Social anxiety; Teacher support; Autonomous learning (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s13132-023-01641-x

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