The Advent of Coronavirus Disease 2019 and the Impact of Mobile Learning on Student Learning Performance: The Mediating Role of Student Learning Behavior

Zhiwei Wang; Alia Qadir; Alia Asmat; Muhammad Sheeraz Aslam Mian; Xiaoli Luo

doi:10.3389/fpsyg.2021.796298

The Advent of Coronavirus Disease 2019 and the Impact of Mobile Learning on Student Learning Performance: The Mediating Role of Student Learning Behavior

Front Psychol. 2022 Feb 8:12:796298. doi: 10.3389/fpsyg.2021.796298. eCollection 2021.

Authors

Zhiwei Wang^{1

2}, Alia Qadir³, Alia Asmat⁴, Muhammad Sheeraz Aslam Mian⁵, Xiaoli Luo⁶

Affiliations

¹ Department of Economics and Management, Nanjing Agricultural University, Nanjing, China.
² College of Liberal Arts, Technological University of the Philippines, Manila, Philippines.
³ Department of Management Sciences, Riphah International University, Faisalabad Campus, Faisalabad, Pakistan.
⁴ Department of Psychology, University of Central Punjab, Lahore, Pakistan.
⁵ Department of Business Administration, Allama Iqbal Open University, Islamabad, Pakistan.
⁶ School of Economics, Central South University for Nationalities, Wuhan, China.

Abstract

The recent coronavirus disease 2019 (COVID-19) pandemic pushed almost all institutions to adopt online and virtual education. The uncertainty of this situation produced various questions that perplexed educationists regarding what implications the pandemic would have on educational institutions, especially regarding how the switch to online education would impact the behavior and performance of students. The vast importance of this matter attracted the attention of researchers and served as the motivation for this research, which aims to resolve this confusion by studying the use of mobile learning (ML) among students for educational purposes during the COVID-19 period. This study also examines how this situation has affected student learning behavior (LB) and performance (SP) in the higher education setting. This research is based on collaborative learning theory, sociocultural learning theory, and ML theory. This quantitative research employed the convenient sampling technique to collect data through structured questionnaires distributed to 396 students of higher education institutions who carry a mobile device. This study used descriptive and inferential statistics to make the data more meaningful. Structural equation modeling (SEM) with AMOS software was used for hypothesis testing. The results showed that ML was a significant and positive predictor of SP and LB. Moreover, student LB partially mediated the relationship between ML and SP. The findings suggest that the academic performance of students can be enhanced by building a ML environment that aligns with the LB of students. Nevertheless, content suitable for ML must be developed, and future research should be conducted on this topic.

Keywords: collaborative learning behaviors; mobile learning; personalized learning behaviors; social learning behaviors; student learning behavior; student performance.