Preference and Readiness of Nursing Students for Mobile Learning

Li, Kam Cheong; Lee, Linda Yin King; Wong, Billy Tak Ming; Yau, Ivy Sui Yu

doi:10.1007/978-981-10-7995-5_9

Cited by 2 publications

(1 citation statement)

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“… Bandura (1997) described academic LSE as the belief of individuals that they can accomplish what is required of them per the educational task or achieve a clear educational goal. Li et al (2018a) stated that LSE comprises confidence, technology experiences, the difficulty of using technology, and sources of confidence. The LSE of an individual is related to their smartphone usage and habits, computer literacy, Internet literacy, expertise, and self-confidence ( Razzaq et al, 2018 ).…”

Section: Hypotheses Developmentmentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Hypotheses Developmentmentioning

confidence: 99%