Heliyon (Aug 2024)
Chinese nursing students’ academic self-concept and deep learning in online courses: Does psychological capital play a moderating role?
Abstract
The advent of online education has become indispensable for nursing students seeking to acquire knowledge. However, the efficacy of online education often falls short of initial expectations. Deep learning (DL) can assist learners tackle complex problems and make innovative decisions. Despite its potential, there has been limited exploration into the underlying mechanisms of DL among nursing students, both domestically and globally. This study examined the potential moderating effect of psychological capital (PC) on the association between academic self-concept (AS-c) and DL among nursing students from China enrolled in online courses. Conducted from October 2022 to January 2023, the survey involved 635 nursing students from four public universities in eastern China, utilizing convenience sampling. Data was collected using the AS-c scale, psychological capital scale, and DL scale in online courses. Correlation analyses, univariate analyses, multiple linear regression analyses, and the PROCESS macro were employed for a comprehensive examination. The results revealed a strong positive relationship between nursing students' DL and both their AS-c (r = 0.766, P < 0.01) and PC (r = 0.714, P < 0.01), respectively. Additionally, the effect of AS-c on DL was stronger among individuals with high PC (β = 0.34, SE = 0.03, P < 0.001) compared to those with low (β = 0.29, SE = 0.02, P < 0.001) or medium (β = 0.24, SE = 0.02, P < 0.001) levels of PC, indicating that PC exerts moderating effects and promotes DL among nursing students enrolled in online courses. Based on these findings, several implications are suggested for the theory and practice of facilitating DL.
